Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization

PubMed Central

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. PMID:22163833

Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

PubMed

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry.

PubMed

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-03-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry

PubMed Central

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-01-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings. PMID:26938541

Remote Structural Health Monitoring and Advanced Prognostics of Wind Turbines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Douglas Brown; Bernard Laskowski

The prospect of substantial investment in wind energy generation represents a significant capital investment strategy. In order to maximize the life-cycle of wind turbines, associated rotors, gears, and structural towers, a capability to detect and predict (prognostics) the onset of mechanical faults at a sufficiently early stage for maintenance actions to be planned would significantly reduce both maintenance and operational costs. Advancement towards this effort has been made through the development of anomaly detection, fault detection and fault diagnosis routines to identify selected fault modes of a wind turbine based on available sensor data preceding an unscheduled emergency shutdown. Themore » anomaly detection approach employs spectral techniques to find an approximation of the data using a combination of attributes that capture the bulk of variability in the data. Fault detection and diagnosis (FDD) is performed using a neural network-based classifier trained from baseline and fault data recorded during known failure conditions. The approach has been evaluated for known baseline conditions and three selected failure modes: pitch rate failure, low oil pressure failure and a gearbox gear-tooth failure. Experimental results demonstrate the approach can distinguish between these failure modes and normal baseline behavior within a specified statistical accuracy.« less

A leakage-free resonance sparse decomposition technique for bearing fault detection in gearboxes

NASA Astrophysics Data System (ADS)

Osman, Shazali; Wang, Wilson

2018-03-01

Most of rotating machinery deficiencies are related to defects in rolling element bearings. Reliable bearing fault detection still remains a challenging task, especially for bearings in gearboxes as bearing-defect-related features are nonstationary and modulated by gear mesh vibration. A new leakage-free resonance sparse decomposition (LRSD) technique is proposed in this paper for early bearing fault detection of gearboxes. In the proposed LRSD technique, a leakage-free filter is suggested to remove strong gear mesh and shaft running signatures. A kurtosis and cosine distance measure is suggested to select appropriate redundancy r and quality factor Q. The signal residual is processed by signal sparse decomposition for highpass and lowpass resonance analysis to extract representative features for bearing fault detection. The effectiveness of the proposed technique is verified by a succession of experimental tests corresponding to different gearbox and bearing conditions.

Improving the performance of univariate control charts for abnormal detection and classification

NASA Astrophysics Data System (ADS)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

PubMed Central

Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

2014-01-01

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

Studies on Automobile Clutch Release Bearing Characteristics with Acoustic Emission

NASA Astrophysics Data System (ADS)

Chen, Guoliang; Chen, Xiaoyang

Automobile clutch release bearings are important automotive driveline components. For the clutch release bearing, early fatigue failure diagnosis is significant, but the early fatigue failure response signal is not obvious, because failure signals are susceptible to noise on the transmission path and to working environment factors such as interference. With an improvement in vehicle design, clutch release bearing fatigue life indicators have increasingly become an important requirement. Contact fatigue is the main failure mode of release rolling bearing components. Acoustic emission techniques in contact fatigue failure detection have unique advantages, which include highly sensitive nondestructive testing methods. In the acoustic emission technique to detect a bearing, signals are collected from multiple sensors. Each signal contains partial fault information, and there is overlap between the signals' fault information. Therefore, the sensor signals receive simultaneous source information integration is complete fragment rolling bearing fault acoustic emission signal, which is the key issue of accurate fault diagnosis. Release bearing comprises the following components: the outer ring, inner ring, rolling ball, cage. When a failure occurs (such as cracking, pitting), the other components will impact damaged point to produce acoustic emission signal. Release bearings mainly emit an acoustic emission waveform with a Rayleigh wave propagation. Elastic waves emitted from the sound source, and it is through the part surface bearing scattering. Dynamic simulation of rolling bearing failure will contribute to a more in-depth understanding of the characteristics of rolling bearing failure, because monitoring and fault diagnosis of rolling bearings provide a theoretical basis and foundation.

Ste. Genevieve Fault Zone, Missouri and Illinois. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, W.J.; Lumm, D.K.

1985-07-01

The Ste. Genevieve Fault Zone is a major structural feature which strikes NW-SE for about 190 km on the NE flank of the Ozark Dome. There is up to 900 m of vertical displacement on high angle normal and reverse faults in the fault zone. At both ends the Ste. Genevieve Fault Zone dies out into a monocline. Two periods of faulting occurred. The first was in late Middle Devonian time and the second from latest Mississippian through early Pennsylvanian time, with possible minor post-Pennsylvanian movement. No evidence was found to support the hypothesis that the Ste. Genevieve Fault Zonemore » is part of a northwestward extension of the late Precambrian-early Cambrian Reelfoot Rift. The magnetic and gravity anomalies cited in support of the ''St. Louis arm'' of the Reelfoot Rift possible reflect deep crystal features underlying and older than the volcanic terrain of the St. Francois Mountains (1.2 to 1.5 billion years old). In regard to neotectonics no displacements of Quaternary sediments have been detected, but small earthquakes occur from time to time along the Ste. Genevieve Fault Zone. Many faults in the zone appear capable of slipping under the current stress regime of east-northeast to west-southwest horizontal compression. We conclude that the zone may continue to experience small earth movements, but catastrophic quakes similar to those at New Madrid in 1811-12 are unlikely. 32 figs., 1 tab.« less

Artificial Intelligence Application in Power Generation Industry: Initial considerations

NASA Astrophysics Data System (ADS)

Ismail, Rahmat Izaizi B.; Ismail Alnaimi, Firas B.; AL-Qrimli, Haidar F.

2016-03-01

With increased competitiveness in power generation industries, more resources are directed in optimizing plant operation, including fault detection and diagnosis. One of the most powerful tools in faults detection and diagnosis is artificial intelligence (AI). Faults should be detected early so correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary interruption and downtime. For the last few decades there has been major interest towards intelligent condition monitoring system (ICMS) application in power plant especially with AI development particularly in artificial neural network (ANN). ANN is based on quite simple principles, but takes advantage of their mathematical nature, non-linear iteration to demonstrate powerful problem solving ability. With massive possibility and room for improvement in AI, the inspiration for researching them are apparent, and literally, hundreds of papers have been published, discussing the findings of hybrid AI for condition monitoring purposes. In this paper, the studies of ANN and genetic algorithm (GA) application will be presented.

Software-implemented fault insertion: An FTMP example

NASA Technical Reports Server (NTRS)

Czeck, Edward W.; Siewiorek, Daniel P.; Segall, Zary Z.

1987-01-01

This report presents a model for fault insertion through software; describes its implementation on a fault-tolerant computer, FTMP; presents a summary of fault detection, identification, and reconfiguration data collected with software-implemented fault insertion; and compares the results to hardware fault insertion data. Experimental results show detection time to be a function of time of insertion and system workload. For the fault detection time, there is no correlation between software-inserted faults and hardware-inserted faults; this is because hardware-inserted faults must manifest as errors before detection, whereas software-inserted faults immediately exercise the error detection mechanisms. In summary, the software-implemented fault insertion is able to be used as an evaluation technique for the fault-handling capabilities of a system in fault detection, identification and recovery. Although the software-inserted faults do not map directly to hardware-inserted faults, experiments show software-implemented fault insertion is capable of emulating hardware fault insertion, with greater ease and automation.

Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging

NASA Astrophysics Data System (ADS)

Jaffery, Zainul Abdin; Dubey, Ashwani Kumar; Irshad; Haque, Ahteshamul

2017-06-01

Degradation of PV modules can cause excessive overheating which results in a reduced power output and eventually failure of solar panel. To maintain the long term reliability of solar modules and maximize the power output, faults in modules need to be diagnosed at an early stage. This paper provides a comprehensive algorithm for fault diagnosis in solar modules using infrared thermography. Infrared Thermography (IRT) is a reliable, non-destructive, fast and cost effective technique which is widely used to identify where and how faults occurred in an electrical installation. Infrared images were used for condition monitoring of solar modules and fuzzy logic have been used to incorporate intelligent classification of faults. An automatic approach has been suggested for fault detection, classification and analysis. IR images were acquired using an IR camera. To have an estimation of thermal condition of PV module, the faulty panel images were compared to a healthy PV module thermal image. A fuzzy rule-base was used to classify faults automatically. Maintenance actions have been advised based on type of faults.

A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays

PubMed Central

Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

2017-01-01

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system. PMID:28245623

A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays.

PubMed

Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

2017-02-25

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform

NASA Astrophysics Data System (ADS)

Abd-el-Malek, Mina; Abdelsalam, Ahmed K.; Hassan, Ola E.

2017-09-01

Robustness, low running cost and reduced maintenance lead Induction Motors (IMs) to pioneerly penetrate the industrial drive system fields. Broken rotor bars (BRBs) can be considered as an important fault that needs to be early assessed to minimize the maintenance cost and labor time. The majority of recent BRBs' fault diagnostic techniques focus on differentiating between healthy and faulty rotor cage. In this paper, a new technique is proposed for detecting the location of the broken bar in the rotor. The proposed technique relies on monitoring certain statistical parameters estimated from the analysis of the start-up stator current envelope. The envelope of the signal is obtained using Hilbert Transformation (HT). The proposed technique offers non-invasive, fast computational and accurate location diagnostic process. Various simulation scenarios are presented that validate the effectiveness of the proposed technique.

Real-time Microseismic Processing for Induced Seismicity Hazard Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzel, Eric M.

Induced seismicity is inherently associated with underground fluid injections. If fluids are injected in proximity to a pre-existing fault or fracture system, the resulting elevated pressures can trigger dynamic earthquake slip, which could both damage surface structures and create new migration pathways. The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterizationmore » phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.« less

Experimental Measurements of Permeability Evolution along Faults during Progressive Slip

NASA Astrophysics Data System (ADS)

Strutz, M.; Mitchell, T. M.; Renner, J.

2010-12-01

Little is currently known about the dynamic changes in fault-parallel permeability along rough faults during progressive slip. With increasing slip, asperities are worn to produce gouge which can dramatically reduce along fault permeability within the slip zone. However, faults can have a range of roughness which can affect both the porosity and both the amount and distribution of fault wear material produced in the slipping zone during the early stages of fault evolution. In this novel study we investigate experimentally the evolution of permeability along a fault plane in granite sawcut sliding blocks with a variety of intial roughnesses in a triaxial apparatus. Drillholes in the samples allow the permeability to be measured along the fault plane during loading and subsequent fault displacement. Use of the pore pressure oscillation technique (PPO) allows the continuous measurement of permeability without having to stop loading. To achieve a range of intial starting roughnesses, faults sawcut surfaces were prepared using a variety of corundum powders ranging from 10 µm to 220 µm, and for coarser roughness were air-blasted with glass beads up to 800µm in size. Fault roughness has been quantified with a laser profileometer. During sliding, we measure the acoustic emissions in order to detect grain cracking and asperity shearing which may relate to both the mechanical and permeability data. Permeability shows relative reductions of up to over 4 orders of magnitude during stable sliding as asperities are sheared to produce a fine fault gouge. This variation in permeability is greatest for the roughest faults, reducing as fault roughness decreases. The onset of permeability reduction is contemporaneous with a dramatic reduction in the amount of detected acoustic emissions, where a continuous layer of fault gouge has developed. The amount of fault gouge produced is related to the initial roughness, with the rough faults showing larger fault gouge layers at the end of slip. Following large stress drops and stick slip events, permeability can both increase and decrease due to dynamic changes in pore pressure during fast sliding events. We present a summary of preliminary data to date, and discuss some of the problems and unknowns when using the PPO method to measure permeability.

Final Technical Report: PV Fault Detection Tool.

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Bruce Hardison; Jones, Christian Birk

The PV Fault Detection Tool project plans to demonstrate that the FDT can (a) detect catastrophic and degradation faults and (b) identify the type of fault. This will be accomplished by collecting fault signatures using different instruments and integrating this information to establish a logical controller for detecting, diagnosing and classifying each fault.

Detecting Gear Tooth Fatigue Cracks in Advance of Complete Fracture

NASA Technical Reports Server (NTRS)

Zakrajsek, James J.; Lewicki, David G.

1996-01-01

Results of using vibration-based methods to detect gear tooth fatigue cracks are presented. An experimental test rig was used to fail a number of spur gear specimens through bending fatigue. The gear tooth fatigue crack in each test was initiated through a small notch in the fillet area of a tooth on the gear. The primary purpose of these tests was to verify analytical predictions of fatigue crack propagation direction and rate as a function of gear rim thickness. The vibration signal from a total of three tests was monitored and recorded for gear fault detection research. The damage consisted of complete rim fracture on the two thin rim gears and single tooth fracture on the standard full rim test gear. Vibration-based fault detection methods were applied to the vibration signal both on-line and after the tests were completed. The objectives of this effort were to identify methods capable of detecting the fatigue crack and to determine how far in advance of total failure positive detection was given. Results show that the fault detection methods failed to respond to the fatigue crack prior to complete rim fracture in the thin rim gear tests. In the standard full rim gear test all of the methods responded to the fatigue crack in advance of tooth fracture; however, only three of the methods responded to the fatigue crack in the early stages of crack propagation.

Rocket Engine Health Management: Early Definition of Critical Flight Measurements

NASA Technical Reports Server (NTRS)

Christenson, Rick L.; Nelson, Michael A.; Butas, John P.

2003-01-01

The NASA led Space Launch Initiative (SLI) program has established key requirements related to safety, reliability, launch availability and operations cost to be met by the next generation of reusable launch vehicles. Key to meeting these requirements will be an integrated vehicle health management ( M) system that includes sensors, harnesses, software, memory, and processors. Such a system must be integrated across all the vehicle subsystems and meet component, subsystem, and system requirements relative to fault detection, fault isolation, and false alarm rate. The purpose of this activity is to evolve techniques for defining critical flight engine system measurements-early within the definition of an engine health management system (EHMS). Two approaches, performance-based and failure mode-based, are integrated to provide a proposed set of measurements to be collected. This integrated approach is applied to MSFC s MC-1 engine. Early identification of measurements supports early identification of candidate sensor systems whose design and impacts to the engine components must be considered in engine design.

Seismic evidence for change of the tectonic regime in Messinian, northern Marmara Sea, Turkey

NASA Astrophysics Data System (ADS)

Alp, Hakan; Vardar, Denizhan; Alpar, Bedri; Ustaömer, Timur

2018-01-01

New Chirp seismic data collected from the northern margin of the Marmara Sea in June 2015 and previous Sparker seismic profiles recorded in 1999 suggest a change in tectonic regime in Messinian. New tectonic lineaments and fault segments were detected at offshore the Çekmece lagoons region that is located on one of the possible water corridors with the Paratethys. The faults only affect the older seismic unit (U1), which can be best outlined on the Chirp data. The E-W trending fault offshore Avcılar (OAF) borders the northern edge of a tightly folded sedimentary zone. The NNE-SSW trending fault, namely the Büyükçekmece Fault (BF), passing through the Büyükçekmece Bay, follows a buried valley. Its evolution must be related to the development of the Early Miocene - Early Pliocene Thrace-Eskişehir fault zone (TEFZ). BF and OAF indicate old tectonic activities in the region, which continued to the North Anatolian fault becoming the most dominant tectonic element in the region. The upper surface of the stratigraphic unit U1 and its terraces define the thickness of younger deposits (U2), which is thinner in the middle of the shelf. The morphology of the tightly folded zone controls those terraces, which correspond to the Bakırköy Formation and Kıraç member on land. The topmost parts of the terraces must have been eroded during sea level low-stands and cutting of the paleo-valleys. There is no evidence of any tectonic deformation or active fault in the younger seismic unit (U2).

Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

NASA Astrophysics Data System (ADS)

Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

2014-01-01

In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

Early Oscillation Detection for DC/DC Converter Fault Diagnosis

NASA Technical Reports Server (NTRS)

Wang, Bright L.

2011-01-01

The electrical power system of a spacecraft plays a very critical role for space mission success. Such a modern power system may contain numerous hybrid DC/DC converters both inside the power system electronics (PSE) units and onboard most of the flight electronics modules. One of the faulty conditions for DC/DC converter that poses serious threats to mission safety is the random occurrence of oscillation related to inherent instability characteristics of the DC/DC converters and design deficiency of the power systems. To ensure the highest reliability of the power system, oscillations in any form shall be promptly detected during part level testing, system integration tests, flight health monitoring, and on-board fault diagnosis. The popular gain/phase margin analysis method is capable of predicting stability levels of DC/DC converters, but it is limited only to verification of designs and to part-level testing on some of the models. This method has to inject noise signals into the control loop circuitry as required, thus, interrupts the DC/DC converter's normal operation and increases risks of degrading and damaging the flight unit. A novel technique to detect oscillations at early stage for flight hybrid DC/DC converters was developed.

Rolling element bearings diagnostics using the Symbolic Aggregate approXimation

NASA Astrophysics Data System (ADS)

Georgoulas, George; Karvelis, Petros; Loutas, Theodoros; Stylios, Chrysostomos D.

2015-08-01

Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

NASA Astrophysics Data System (ADS)

Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

2009-04-01

Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

Parameter Transient Behavior Analysis on Fault Tolerant Control System

NASA Technical Reports Server (NTRS)

Belcastro, Christine (Technical Monitor); Shin, Jong-Yeob

2003-01-01

In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured based on fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. This paper illustrates analysis of a FTC system based on estimated fault parameter transient behavior which may include false fault detections during a short time interval. Using Lyapunov function analysis, the upper bound of an induced-L2 norm of the FTC system performance is calculated as a function of a fault detection time and the exponential decay rate of the Lyapunov function.

Development of a GNSS-Enhanced Tsunami Early Warning System

NASA Astrophysics Data System (ADS)

Bawden, G. W.; Melbourne, T. I.; Bock, Y.; Song, Y. T.; Komjathy, A.

2015-12-01

The past decade has witnessed a terrible loss of life and economic disruption caused by large earthquakes and resultant tsunamis impacting coastal communities and infrastructure across the Indo-Pacific region. NASA has funded the early development of a prototype real-time Global Navigation Satellite System (RT-GNSS) based rapid earthquake and tsunami early warning (GNSS-TEW) system that may be used to enhance seismic tsunami early warning systems for large earthquakes. This prototype GNSS-TEW system geodetically estimates fault parameters (earthquake magnitude, location, strike, dip, and slip magnitude/direction on a gridded fault plane both along strike and at depth) and tsunami source parameters (seafloor displacement, tsunami energy scale, and 3D tsunami initials) within minutes after the mainshock based on dynamic numerical inversions/regressions of the real-time measured displacements within a spatially distributed real-time GNSS network(s) spanning the epicentral region. It is also possible to measure fluctuations in the ionosphere's total electron content (TEC) in the RT-GNSS data caused by the pressure wave from the tsunami. This TEC approach can detect if a tsunami has been triggered by an earthquake, track its waves as they propagate through the oceanic basins, and provide upwards of 45 minutes early warning. These combined real-time geodetic approaches will very quickly address a number of important questions in the immediate minutes following a major earthquake: How big was the earthquake and what are its fault parameters? Could the earthquake have produced a tsunami and was a tsunami generated?

Robust Fault Detection and Isolation for Stochastic Systems

NASA Technical Reports Server (NTRS)

George, Jemin; Gregory, Irene M.

2010-01-01

This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

Fault Detection and Correction for the Solar Dynamics Observatory Attitude Control System

NASA Technical Reports Server (NTRS)

Starin, Scott R.; Vess, Melissa F.; Kenney, Thomas M.; Maldonado, Manuel D.; Morgenstern, Wendy M.

2007-01-01

The Solar Dynamics Observatory is an Explorer-class mission that will launch in early 2009. The spacecraft will operate in a geosynchronous orbit, sending data 24 hours a day to a devoted ground station in White Sands, New Mexico. It will carry a suite of instruments designed to observe the Sun in multiple wavelengths at unprecedented resolution. The Atmospheric Imaging Assembly includes four telescopes with focal plane CCDs that can image the full solar disk in four different visible wavelengths. The Extreme-ultraviolet Variability Experiment will collect time-correlated data on the activity of the Sun's corona. The Helioseismic and Magnetic Imager will enable study of pressure waves moving through the body of the Sun. The attitude control system on Solar Dynamics Observatory is responsible for four main phases of activity. The physical safety of the spacecraft after separation must be guaranteed. Fine attitude determination and control must be sufficient for instrument calibration maneuvers. The mission science mode requires 2-arcsecond control according to error signals provided by guide telescopes on the Atmospheric Imaging Assembly, one of the three instruments to be carried. Lastly, accurate execution of linear and angular momentum changes to the spacecraft must be provided for momentum management and orbit maintenance. In thsp aper, single-fault tolerant fault detection and correction of the Solar Dynamics Observatory attitude control system is described. The attitude control hardware suite for the mission is catalogued, with special attention to redundancy at the hardware level. Four reaction wheels are used where any three are satisfactory. Four pairs of redundant thrusters are employed for orbit change maneuvers and momentum management. Three two-axis gyroscopes provide full redundancy for rate sensing. A digital Sun sensor and two autonomous star trackers provide two-out-of-three redundancy for fine attitude determination. The use of software to maximize chances of recovery from any hardware or software fault is detailed. A generic fault detection and correction software structure is used, allowing additions, deletions, and adjustments to fault detection and correction rules. This software structure is fed by in-line fault tests that are also able to take appropriate actions to avoid corruption of the data stream.

On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

Assessment of Data and Knowledge Fusion Strategies for Diagnostics and Prognostics

DTIC Science & Technology

2001-04-05

prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection , and predicting time...or better than the sum of the parts. Specific to health management, this means reduced uncertainty in current condition assessment reduced (improving...achieve time synchronous averaged vibration features. Semmm Amy -U....1A MreN T.g 4 Id F~As- Anomaly DEtection Figure 1 - Fusion Application Areas At a

Model-based fault detection and isolation for intermittently active faults with application to motion-based thruster fault detection and isolation for spacecraft

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2008-01-01

The present invention is a method for detecting and isolating fault modes in a system having a model describing its behavior and regularly sampled measurements. The models are used to calculate past and present deviations from measurements that would result with no faults present, as well as with one or more potential fault modes present. Algorithms that calculate and store these deviations, along with memory of when said faults, if present, would have an effect on the said actual measurements, are used to detect when a fault is present. Related algorithms are used to exonerate false fault modes and finally to isolate the true fault mode. This invention is presented with application to detection and isolation of thruster faults for a thruster-controlled spacecraft. As a supporting aspect of the invention, a novel, effective, and efficient filtering method for estimating the derivative of a noisy signal is presented.

Fault zones ruptured during the early 2014 Cephalonia Island (Ionian Sea, Western Greece) earthquakes (January 26 and February 3, Mw 6.0) based on the associated co-seismic surface ruptures

NASA Astrophysics Data System (ADS)

Lekkas, Efthymios L.; Mavroulis, Spyridon D.

2016-01-01

The early 2014 Cephalonia Island (Ionian Sea, Western Greece) earthquake sequence comprised two main shocks with almost the same magnitude (moment magnitude (Mw) 6.0) occurring successively within a short time (January 26 and February 3) and space (Paliki peninsula in Western Cephalonia) interval. Εach earthquake was induced by the rupture of a different pre-existing onshore active fault zone and produced different co-seismic surface rupture zones. Co-seismic surface rupture structures were predominantly strike-slip-related structures including V-shaped conjugate surface ruptures, dextral and sinistral strike-slip surface ruptures, restraining and releasing bends, Riedel structures ( R, R', P, T), small-scale bookshelf faulting, and flower structures. An extensional component was present across surface rupture zones resulting in ground openings (sinkholes), small-scale grabens, and co-seismic dip-slip (normal) displacements. A compressional component was also present across surface rupture zones resulting in co-seismic dip-slip (reverse) displacements. From the comparison of our field geological observations with already published surface deformation measurements by DInSAR Interferometry, it is concluded that there is a strong correlation among the surface rupture zones, the ruptured active fault zones, and the detected displacement discontinuities in Paliki peninsula.

Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique.

PubMed

Chen, Wen; Chowdhury, Fahmida N; Djuric, Ana; Yeh, Chih-Ping

2014-09-01

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Main propulsion functional path analysis for performance monitoring fault detection and annunciation

NASA Technical Reports Server (NTRS)

Keesler, E. L.

1974-01-01

A total of 48 operational flight instrumentation measurements were identified for use in performance monitoring and fault detection. The Operational Flight Instrumentation List contains all measurements identified for fault detection and annunciation. Some 16 controller data words were identified for use in fault detection and annunciation.

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

PubMed

Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

2016-07-01

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

PubMed

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-04-28

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids

PubMed Central

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-01-01

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability. PMID:28452925

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

NASA Technical Reports Server (NTRS)

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Tectono-stratigraphic evolution of normal fault zones: Thal Fault Zone, Suez Rift, Egypt

NASA Astrophysics Data System (ADS)

Leppard, Christopher William

The evolution of linkage of normal fault populations to form continuous, basin bounding normal fault zones is recognised as an important control on the stratigraphic evolution of rift-basins. This project aims to investigate the temporal and spatial evolution of normal fault populations and associated syn-rift deposits from the initiation of early-formed, isolated normal faults (rift-initiation) to the development of a through-going fault zone (rift-climax) by documenting the tectono-stratigraphic evolution of the Sarbut EI Gamal segment of the exceptionally well-exposed Thai fault zone, Suez Rift, Egypt. A number of dated stratal surfaces mapped around the syn-rift depocentre of the Sarbut El Gamal segment allow constraints to be placed on the timing and style of deformation, and the spatial variability of facies along this segment of the fault zone. Data collected indicates that during the first 3.5 My of rifting the structural style was characterised by numerous, closely spaced, short (< 3 km), low displacement (< 200 m) synthetic and antithetic normal faults within 1 - 2 km of the present-day fault segment trace, accommodating surface deformation associated with the development of a fault propagation monocline above the buried, pre-cursor strands of the Sarbut El Gamal fault segment. The progressive localisation of displacement onto the fault segment during rift-climax resulted in the development of a major, surface-breaking fault 3.5 - 5 My after the onset of rifting and is recorded by the death of early-formed synthetic and antithetic faults up-section, and thickening of syn-rift strata towards the fault segment. The influence of intrabasinal highs at the tips of the Sarbut EI Gamal fault segment on the pre-rift sub-crop level, combined with observations from the early-formed structures and coeval deposits suggest that the overall length of the fault segment was fixed from an early stage. The fault segment is interpreted to have grown through rapid lateral propagation and early linkage of the precursor fault strands at depth before the fault segment broke surface, followed by the accumulation of displacement on the linked fault segment with minimal lateral propagation. This style of fault growth contrasts conventional fault growth models by which growth occurs through incremental increases in both displacement and length through time. The evolution of normal fault populations and fault zones exerts a first- order control on basin physiography and sediment supply, and therefore, the architecture and distribution of coeval syn-rift stratigraphy. The early syn-rift continental, Abu Zenima Formation, to shallow marine, Nukhul Formation show a pronounced westward increase in thickness controlled by the series of synthetic and antithetic faults up to 3 km west of present day Thai fault. The orientation of these faults controlled the location of fluvial conglomerates, sandstones and mudstones that shifted to the topographic lows created. The progressive localisation of displacement onto the Sarbut El Gamal fault segment during rift-climax resulted in an overall change in basin geometry. Accelerated subsidence rates led to sedimentation rates being outpaced by subsidence resulting in the development of a marine, sediment-starved, underfilled hangingwall depocentre characterised by slope-to-basinal depositional environments, with a laterally continuous slope apron in the immediate hangingwall, and point-sourced submarine fans. Controls on the spatial distribution, three dimensional architecture, and facies stacking patterns of coeval syn-rift deposits are identified as: I) structural style of the evolution and linkage of normal fault populations, ii) basin physiography, iii) evolution of drainage catchments, iv) bedrock lithology, and v) variations in sea/lake level.

Slowing extrusion tectonics: Lowered estimate of post-Early Miocene slip rate for the Altyn Tagh fault

USGS Publications Warehouse

Yue, Y.; Ritts, B.D.; Graham, S.A.; Wooden, J.L.; Gehrels, G.E.; Zhang, Z.

2004-01-01

Determination of long-term slip rate for the Altyn Tagh fault is essential for testing whether Asian tectonics is dominated by lateral extrusion or distributed crustal shortening. Previous slip-history studies focused on either Quaternary slip-rate measurements or pre-Early Miocene total-offset estimates and do not allow a clear distinction between rates based on the two. The magmatic and metamorphic history revealed by SHRIMP zircon dating of clasts from Miocene conglomerate in the Xorkol basin north of the Altyn Tagh fault strikingly matches that of basement in the southern Qilian Shan and northern Qaidam regions south of the fault. This match requires that the post-Early Miocene long-term slip rate along the Altyn Tagh fault cannot exceed 10 mm/year, supporting the hypothesis of distributed crustal thickening for post-Early Miocene times. This low long-term slip rate and recently documented large pre-Early Miocene cumulative offset across the fault support a two-stage evolution, wherein Asian tectonics was dominated by lateral extrusion before the end of Early Miocene, and since then has been dominated by distributed crustal thickening and rapid plateau uplift. ?? 2003 Elsevier B.V. All rights reserved.

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.

2010-01-01

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

A signal-based fault detection and classification method for heavy haul wagons

NASA Astrophysics Data System (ADS)

Li, Chunsheng; Luo, Shihui; Cole, Colin; Spiryagin, Maksym; Sun, Yanquan

2017-12-01

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

Weak characteristic information extraction from early fault of wind turbine generator gearbox

NASA Astrophysics Data System (ADS)

Xu, Xiaoli; Liu, Xiuli

2017-09-01

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

A distributed fault-detection and diagnosis system using on-line parameter estimation

NASA Technical Reports Server (NTRS)

Guo, T.-H.; Merrill, W.; Duyar, A.

1991-01-01

The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

Planetary Gearbox Fault Detection Using Vibration Separation Techniques

NASA Technical Reports Server (NTRS)

Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason

2011-01-01

Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection.

Applications of Fault Detection in Vibrating Structures

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

PV Systems Reliability Final Technical Report: Ground Fault Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lavrova, Olga; Flicker, Jack David; Johnson, Jay

We have examined ground faults in PhotoVoltaic (PV) arrays and the efficacy of fuse, current detection (RCD), current sense monitoring/relays (CSM), isolation/insulation (Riso) monitoring, and Ground Fault Detection and Isolation (GFID) using simulations based on a Simulation Program with Integrated Circuit Emphasis SPICE ground fault circuit model, experimental ground faults installed on real arrays, and theoretical equations.

Application of a Subspace-Based Fault Detection Method to Industrial Structures

NASA Astrophysics Data System (ADS)

Mevel, L.; Hermans, L.; van der Auweraer, H.

1999-11-01

Early detection and localization of damage allow increased expectations of reliability, safety and reduction of the maintenance cost. This paper deals with the industrial validation of a technique to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and to detect slight deviations in a modal model derived from in-operation measured data. In this paper, a statistical local approach based on covariance-driven stochastic subspace identification is proposed. The capabilities and limitations of the method with respect to health monitoring and damage detection are discussed and it is explained how the method can be practically used in industrial environments. After the successful validation of the proposed method on a few laboratory structures, its application to a sports car is discussed. The example illustrates that the method allows the early detection of a vibration-induced fatigue problem of a sports car.

Seismic Monitoring Prior to and During DFDP-2 Drilling, Alpine Fault, New Zealand: Matched-Filter Detection Testing and the Real-Time Monitoring System

NASA Astrophysics Data System (ADS)

Boese, C. M.; Chamberlain, C. J.; Townend, J.

2015-12-01

In preparation for the second stage of the Deep Fault Drilling Project (DFDP) and as part of related research projects, borehole and surface seismic stations were installed near the intended DFDP-2 drill-site in the Whataroa Valley from late 2008. The final four borehole stations were installed within 1.2 km of the drill-site in early 2013 to provide near-field observations of any seismicity that occurred during drilling and thus provide input into operational decision-making processes if required. The basis for making operational decisions in response to any detected seismicity had been established as part of a safety review conducted in early 2014 and was implemented using a "traffic light" system, a communications plan, and other operational documents. Continuous real-time earthquake monitoring took place throughout the drilling period, between September and late December 2014, and involved a team of up to 15 seismologists working in shifts near the drill-site and overseas. Prior to drilling, records from 55 local earthquakes and 14 quarry blasts were used as master templates in a matched-filter detection algorithm to test the capabilities of the seismic network for detecting seismicity near the drill site. The newly detected microseismicity was clustered near the DFDP-1 drill site at Gaunt Creek, 7.4 km southwest of DFDP-2. Relocations of these detected events provide more information about the fault geometry in this area. Although no detectable seismicity occurred within 5 km of the drill site during the drilling period, the region is capable of generating earthquakes that would have required an operational response had they occurred while drilling was underway (including a M2.9 event northwest of Gaunt Creek on 15 August 2014). The largest event to occur while drilling was underway was of M4.5 and occurred approximately 40 km east of the DFDP-2 drill site. In this presentation, we summarize the setup and operations of the seismic network and discuss key aspects of seismicity recorded prior to and during drilling operations.

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2003-01-01

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Measurement of fault latency in a digital avionic miniprocessor

NASA Technical Reports Server (NTRS)

Mcgough, J. G.; Swern, F. L.

1981-01-01

The results of fault injection experiments utilizing a gate-level emulation of the central processor unit of the Bendix BDX-930 digital computer are presented. The failure detection coverage of comparison-monitoring and a typical avionics CPU self-test program was determined. The specific tasks and experiments included: (1) inject randomly selected gate-level and pin-level faults and emulate six software programs using comparison-monitoring to detect the faults; (2) based upon the derived empirical data develop and validate a model of fault latency that will forecast a software program's detecting ability; (3) given a typical avionics self-test program, inject randomly selected faults at both the gate-level and pin-level and determine the proportion of faults detected; (4) determine why faults were undetected; (5) recommend how the emulation can be extended to multiprocessor systems such as SIFT; and (6) determine the proportion of faults detected by a uniprocessor BIT (built-in-test) irrespective of self-test.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

PubMed

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

Modeling of a latent fault detector in a digital system

NASA Technical Reports Server (NTRS)

Nagel, P. M.

1978-01-01

Methods of modeling the detection time or latency period of a hardware fault in a digital system are proposed that explain how a computer detects faults in a computational mode. The objectives were to study how software reacts to a fault, to account for as many variables as possible affecting detection and to forecast a given program's detecting ability prior to computation. A series of experiments were conducted on a small emulated microprocessor with fault injection capability. Results indicate that the detecting capability of a program largely depends on the instruction subset used during computation and the frequency of its use and has little direct dependence on such variables as fault mode, number set, degree of branching and program length. A model is discussed which employs an analog with balls in an urn to explain the rate of which subsequent repetitions of an instruction or instruction set detect a given fault.

Methodology for Designing Fault-Protection Software

NASA Technical Reports Server (NTRS)

Barltrop, Kevin; Levison, Jeffrey; Kan, Edwin

2006-01-01

A document describes a methodology for designing fault-protection (FP) software for autonomous spacecraft. The methodology embodies and extends established engineering practices in the technical discipline of Fault Detection, Diagnosis, Mitigation, and Recovery; and has been successfully implemented in the Deep Impact Spacecraft, a NASA Discovery mission. Based on established concepts of Fault Monitors and Responses, this FP methodology extends the notion of Opinion, Symptom, Alarm (aka Fault), and Response with numerous new notions, sub-notions, software constructs, and logic and timing gates. For example, Monitor generates a RawOpinion, which graduates into Opinion, categorized into no-opinion, acceptable, or unacceptable opinion. RaiseSymptom, ForceSymptom, and ClearSymptom govern the establishment and then mapping to an Alarm (aka Fault). Local Response is distinguished from FP System Response. A 1-to-n and n-to- 1 mapping is established among Monitors, Symptoms, and Responses. Responses are categorized by device versus by function. Responses operate in tiers, where the early tiers attempt to resolve the Fault in a localized step-by-step fashion, relegating more system-level response to later tier(s). Recovery actions are gated by epoch recovery timing, enabling strategy, urgency, MaxRetry gate, hardware availability, hazardous versus ordinary fault, and many other priority gates. This methodology is systematic, logical, and uses multiple linked tables, parameter files, and recovery command sequences. The credibility of the FP design is proven via a fault-tree analysis "top-down" approach, and a functional fault-mode-effects-and-analysis via "bottoms-up" approach. Via this process, the mitigation and recovery strategy(s) per Fault Containment Region scope (width versus depth) the FP architecture.

Implementing a C++ Version of the Joint Seismic-Geodetic Algorithm for Finite-Fault Detection and Slip Inversion for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Guillemot, C.; Murray, J. R.

2015-12-01

The earthquake early warning (EEW) systems in California and elsewhere can greatly benefit from algorithms that generate estimates of finite-fault parameters. These estimates could significantly improve real-time shaking calculations and yield important information for immediate disaster response. Minson et al. (2015) determined that combining FinDer's seismic-based algorithm (Böse et al., 2012) with BEFORES' geodetic-based algorithm (Minson et al., 2014) yields a more robust and informative joint solution than using either algorithm alone. FinDer examines the distribution of peak ground accelerations from seismic stations and determines the best finite-fault extent and strike from template matching. BEFORES employs a Bayesian framework to search for the best slip inversion over all possible fault geometries in terms of strike and dip. Using FinDer and BEFORES together generates estimates of finite-fault extent, strike, dip, preferred slip, and magnitude. To yield the quickest, most flexible, and open-source version of the joint algorithm, we translated BEFORES and FinDer from Matlab into C++. We are now developing a C++ Application Protocol Interface for these two algorithms to be connected to the seismic and geodetic data flowing from the EEW system. The interface that is being developed will also enable communication between the two algorithms to generate the joint solution of finite-fault parameters. Once this interface is developed and implemented, the next step will be to run test seismic and geodetic data through the system via the Earthworm module, Tank Player. This will allow us to examine algorithm performance on simulated data and past real events.

Using Fuzzy Clustering for Real-time Space Flight Safety

NASA Technical Reports Server (NTRS)

Lee, Charles; Haskell, Richard E.; Hanna, Darrin; Alena, Richard L.

2004-01-01

To ensure space flight safety, it is necessary to monitor myriad sensor readings on the ground and in flight. Since a space shuttle has many sensors, monitoring data and drawing conclusions from information contained within the data in real time is challenging. The nature of the information can be critical to the success of the mission and safety of the crew and therefore, must be processed with minimal data-processing time. Data analysis algorithms could be used to synthesize sensor readings and compare data associated with normal operation with the data obtained that contain fault patterns to draw conclusions. Detecting abnormal operation during early stages in the transition from safe to unsafe operation requires a large amount of historical data that can be categorized into different classes (non-risk, risk). Even though the 40 years of shuttle flight program has accumulated volumes of historical data, these data don t comprehensively represent all possible fault patterns since fault patterns are usually unknown before the fault occurs. This paper presents a method that uses a similarity measure between fuzzy clusters to detect possible faults in real time. A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This method is applied to downlink data from Columbia flights. The results show that this technique can detect an unexpected fault that has not been present in the training data set.

Potential fault region detection in TFDS images based on convolutional neural network

NASA Astrophysics Data System (ADS)

Sun, Junhua; Xiao, Zhongwen

2016-10-01

In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Early Tertiary transtension-related deformation and magmatism along the Tintina fault system, Alaska

USGS Publications Warehouse

Till, A.B.; Roeske, S.M.; Bradley, D.C.; Friedman, R.; Layer, P.W.

2007-01-01

Transtensional deformation was concentrated in a zone adjacent to the Tintina strike-slip fault system in Alaska during the early Tertiary. The deformation occurred along the Victoria Creek fault, the trace of the Tintina system that connects it with the Kaltag fault; together the Tintina and Kaltag fault systems girdle Alaska from east to west. Over an area of ???25 by 70 km between the Victoria Creek and Tozitna faults, bimodal volcanics erupted; lacustrine and fluvial rocks were deposited; plutons were emplaced and deformed; and metamorphic rocks cooled, all at about the same time. Plutonic and volcanic rocks in this zone yield U-Pb zircon ages of ca. 60 Ma; 40Ar/ 39Ar cooling ages from those plutons and adjacent metamorphic rocks are also ca. 60 Ma. Although early Tertiary magmatism occurred over a broad area in central Alaska, meta- morphism and ductile deformation accompanied that magmatism in this one zone only. Within the zone of deformation, pluton aureoles and metamorphic rocks display consistent NE-SW-stretching lineations parallel to the Victoria Creek fault, suggesting that deformation processes involved subhorizontal elongation of the package. The most deeply buried metamorphic rocks, kyanite-bearing metapelites, occur as lenses adjacent to the fault, which cuts the crust to the Moho (Beaudoin et al., 1997). Geochronologic data and field relationships suggest that the amount of early Tertiary exhumation was greatest adjacent to the Victoria Creek fault. The early Tertiary crustal-scale events that may have operated to produce transtension in this area are (1) increased heat flux and related bimodal within-plate magmatism, (2) movement on a releasing stepover within the Tintina fault system or on a regional scale involving both the Tintina and the Kobuk fault systems, and (3) oroclinal bending of the Tintina-Kaltag fault system with counterclockwise rotation of western Alaska. ?? 2007 The Geological Society of America. All rights reserved.

Classification of Aircraft Maneuvers for Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Clancy, Daniel (Technical Monitor)

2002-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Flight elements: Fault detection and fault management

NASA Technical Reports Server (NTRS)

Lum, H.; Patterson-Hine, A.; Edge, J. T.; Lawler, D.

1990-01-01

Fault management for an intelligent computational system must be developed using a top down integrated engineering approach. An approach proposed includes integrating the overall environment involving sensors and their associated data; design knowledge capture; operations; fault detection, identification, and reconfiguration; testability; causal models including digraph matrix analysis; and overall performance impacts on the hardware and software architecture. Implementation of the concept to achieve a real time intelligent fault detection and management system will be accomplished via the implementation of several objectives, which are: Development of fault tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, diagnosis, and reconfiguration at all system levels providing fault isolation and system integration; Optimize system operations to manage degraded system performance through system integration; and Lower development and operations costs through the implementation of an intelligent real time fault detection and fault management system and an information management system.

Microseismic techniques for avoiding induced seismicity during fluid injection

DOE PAGES

Matzel, Eric; White, Joshua; Templeton, Dennise; ...

2014-01-01

The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterization phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.

Estimation of spectral kurtosis

NASA Astrophysics Data System (ADS)

Sutawanir

2017-03-01

Rolling bearings are the most important elements in rotating machinery. Bearing frequently fall out of service for various reasons: heavy loads, unsuitable lubrications, ineffective sealing. Bearing faults may cause a decrease in performance. Analysis of bearing vibration signals has attracted attention in the field of monitoring and fault diagnosis. Bearing vibration signals give rich information for early detection of bearing failures. Spectral kurtosis, SK, is a parameter in frequency domain indicating how the impulsiveness of a signal varies with frequency. Faults in rolling bearings give rise to a series of short impulse responses as the rolling elements strike faults, SK potentially useful for determining frequency bands dominated by bearing fault signals. SK can provide a measure of the distance of the analyzed bearings from a healthy one. SK provides additional information given by the power spectral density (psd). This paper aims to explore the estimation of spectral kurtosis using short time Fourier transform known as spectrogram. The estimation of SK is similar to the estimation of psd. The estimation falls in model-free estimation and plug-in estimator. Some numerical studies using simulations are discussed to support the methodology. Spectral kurtosis of some stationary signals are analytically obtained and used in simulation study. Kurtosis of time domain has been a popular tool for detecting non-normality. Spectral kurtosis is an extension of kurtosis in frequency domain. The relationship between time domain and frequency domain analysis is establish through power spectrum-autocovariance Fourier transform. Fourier transform is the main tool for estimation in frequency domain. The power spectral density is estimated through periodogram. In this paper, the short time Fourier transform of the spectral kurtosis is reviewed, a bearing fault (inner ring and outer ring) is simulated. The bearing response, power spectrum, and spectral kurtosis are plotted to visualize the pattern of each fault. Keywords: frequency domain Fourier transform, spectral kurtosis, bearing fault

A distributed fault-tolerant signal processor /FTSP/

NASA Astrophysics Data System (ADS)

Bonneau, R. J.; Evett, R. C.; Young, M. J.

1980-01-01

A digital fault-tolerant signal processor (FTSP), an example of a self-repairing programmable system is analyzed. The design configuration is discussed in terms of fault tolerance, system-level fault detection, isolation and common memory. Special attention is given to the FDIR (fault detection isolation and reconfiguration) logic, noting that the reconfiguration decisions are based on configuration, summary status, end-around tests, and north marker/synchro data. Several mechanisms of fault detection are described which initiate reconfiguration at different levels. It is concluded that the reliability of a signal processor can be significantly enhanced by the use of fault-tolerant techniques.

Fiber Bragg grating sensor for fault detection in high voltage overhead transmission lines

NASA Astrophysics Data System (ADS)

Moghadas, Amin

2011-12-01

A fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by fiber Bragg grating (FBG) sensors. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signals. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG sensors and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

PubMed Central

Moghadas, Amin A.; Shadaram, Mehdi

2010-01-01

In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system. PMID:22163416

Multidisciplinary approach for fault detection: Integration of PS-InSAR, geomorphological, stratigraphic and structural data in the Venafro intermontane basin (Central-Southern Apennines, Italy)

NASA Astrophysics Data System (ADS)

Amato, Vincenzo; Aucelli, Pietro P. C.; Bellucci Sessa, Eliana; Cesarano, Massimo; Incontri, Pietro; Pappone, Gerardo; Valente, Ettore; Vilardo, Giuseppe

2017-04-01

A multidisciplinary methodology, integrating stratigraphic, geomorphological and structural data, combined with GIS-aided analysis and PS-InSAR interferometric data, was applied to characterize the relationships between ground deformations and the stratigraphic and the morphostructural setting of the Venafro intermontane basin. This basin is a morphostructural depression related to NW-SE and NE-SW oriented high angle normal faults bordering and crossing it. In particular, a well-known active fault crossing the plain is the Aquae Juliae Fault, whose recent activity is evidenced by archeoseismological data. The approach applied here reveals new evidence of possible faulting, acting during the Lower to Upper Pleistocene, which has driven the morphotectonic and the environmental evolution of the basin. In particular, the tectonic setting emerging from this study highlights the influence of the NW-SE oriented extensional phase during the late Lower Pleistocene - early Middle Pleistocene, in the generation of NE-SW trending, SE dipping, high-angle faults and NW-SE trending, high-angle transtensive faults. This phase has been followed by a NE-SW extensional one, responsible for the formation of NW-SE trending, both NW and SE dipping, high-angle normal faults, and the reactivation of the oldest NE-SW oriented structures. These NW-SE trending normal faults include the Aquae Juliae Fault and a new one, unknown until now, crossing the plain between the Venafro village and the Colle Cupone Mt. (hereinafter named the Venafro-Colle Cupone Fault, VCCF). This fault has controlled deposition of the youngest sedimentary units (late Middle Pleistocene to late Upper Pleistocene) suggesting its recent activity and it is well constrained by PS-InSAR data, as testified by the increase of the subsidence rate in the hanging wall block.

Multiple tests for wind turbine fault detection and score fusion using two- level multidimensional scaling (MDS)

NASA Astrophysics Data System (ADS)

Ye, Xiang; Gao, Weihua; Yan, Yanjun; Osadciw, Lisa A.

2010-04-01

Wind is an important renewable energy source. The energy and economic return from building wind farms justify the expensive investments in doing so. However, without an effective monitoring system, underperforming or faulty turbines will cause a huge loss in revenue. Early detection of such failures help prevent these undesired working conditions. We develop three tests on power curve, rotor speed curve, pitch angle curve of individual turbine. In each test, multiple states are defined to distinguish different working conditions, including complete shut-downs, under-performing states, abnormally frequent default states, as well as normal working states. These three tests are combined to reach a final conclusion, which is more effective than any single test. Through extensive data mining of historical data and verification from farm operators, some state combinations are discovered to be strong indicators of spindle failures, lightning strikes, anemometer faults, etc, for fault detection. In each individual test, and in the score fusion of these tests, we apply multidimensional scaling (MDS) to reduce the high dimensional feature space into a 3-dimensional visualization, from which it is easier to discover turbine working information. This approach gains a qualitative understanding of turbine performance status to detect faults, and also provides explanations on what has happened for detailed diagnostics. The state-of-the-art SCADA (Supervisory Control And Data Acquisition) system in industry can only answer the question whether there are abnormal working states, and our evaluation of multiple states in multiple tests is also promising for diagnostics. In the future, these tests can be readily incorporated in a Bayesian network for intelligent analysis and decision support.

Contrasting catastrophic eruptions predicted by different intrusion and collapse scenarios.

PubMed

Rincón, M; Márquez, A; Herrera, R; Alonso-Torres, A; Granja-Bruña, J L; van Wyk de Vries, B

2018-04-18

Catastrophic volcanic eruptions triggered by landslide collapses can jet upwards or blast sideways. Magma intrusion is related to both landslide-triggered eruptive scenarios (lateral or vertical), but it is not clear how such different responses are produced, nor if any precursor can be used for forecasting them. We approach this problem with physical analogue modelling enhanced with X-ray Multiple Detector Computed Tomography scanning, used to track evolution of internal intrusion, and its related faulting and surface deformation. We find that intrusions produce three different volcano deformation patterns, one of them involving asymmetric intrusion and deformation, with the early development of a listric slump fault producing pronounced slippage of one sector. This previously undescribed early deep potential slip surface provides a unified explanation for the two different eruptive scenarios (lateral vs. vertical). Lateral blast only occurs in flank collapse when the intrusion has risen into the sliding block. Otherwise, vertical rather than lateral expansion of magma is promoted by summit dilatation and flank buttressing. The distinctive surface deformation evolution detected opens the possibility to forecast the possible eruptive scenarios: laterally directed blast should only be expected when surface deformation begins to develop oblique to the first major fault.

Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G.

2000-01-01

The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

A Kalman Filter Based Technique for Stator Turn-Fault Detection of the Induction Motors

NASA Astrophysics Data System (ADS)

Ghanbari, Teymoor; Samet, Haidar

2017-11-01

Monitoring of the Induction Motors (IMs) through stator current for different faults diagnosis has considerable economic and technical advantages in comparison with the other techniques in this content. Among different faults of an IM, stator and bearing faults are more probable types, which can be detected by analyzing signatures of the stator currents. One of the most reliable indicators for fault detection of IMs is lower sidebands of power frequency in the stator currents. This paper deals with a novel simple technique for detecting stator turn-fault of the IMs. Frequencies of the lower sidebands are determined using the motor specifications and their amplitudes are estimated by a Kalman Filter (KF). Instantaneous Total Harmonic Distortion (ITHD) of these harmonics is calculated. Since variation of the ITHD for the three-phase currents is considerable in case of stator turn-fault, the fault can be detected using this criterion, confidently. Different simulation results verify high performance of the proposed method. The performance of the method is also confirmed using some experiments.

An uncertainty-based distributed fault detection mechanism for wireless sensor networks.

PubMed

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-04-25

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

A Game Theoretic Fault Detection Filter

NASA Technical Reports Server (NTRS)

Chung, Walter H.; Speyer, Jason L.

1995-01-01

The fault detection process is modelled as a disturbance attenuation problem. The solution to this problem is found via differential game theory, leading to an H(sub infinity) filter which bounds the transmission of all exogenous signals save the fault to be detected. For a general class of linear systems which includes some time-varying systems, it is shown that this transmission bound can be taken to zero by simultaneously bringing the sensor noise weighting to zero. Thus, in the limit, a complete transmission block can he achieved, making the game filter into a fault detection filter. When we specialize this result to time-invariant system, it is found that the detection filter attained in the limit is identical to the well known Beard-Jones Fault Detection Filter. That is, all fault inputs other than the one to be detected (the "nuisance faults") are restricted to an invariant subspace which is unobservable to a projection on the output. For time-invariant systems, it is also shown that in the limit, the order of the state-space and the game filter can be reduced by factoring out the invariant subspace. The result is a lower dimensional filter which can observe only the fault to be detected. A reduced-order filter can also he generated for time-varying systems, though the computational overhead may be intensive. An example given at the end of the paper demonstrates the effectiveness of the filter as a tool for fault detection and identification.

Enhanced data validation strategy of air quality monitoring network.

PubMed

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

2018-01-01

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement). Copyright © 2017 Elsevier Inc. All rights reserved.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Expert System Detects Power-Distribution Faults

NASA Technical Reports Server (NTRS)

Walters, Jerry L.; Quinn, Todd M.

1994-01-01

Autonomous Power Expert (APEX) computer program is prototype expert-system program detecting faults in electrical-power-distribution system. Assists human operators in diagnosing faults and deciding what adjustments or repairs needed for immediate recovery from faults or for maintenance to correct initially nonthreatening conditions that could develop into faults. Written in Lisp.

Detection of CMOS bridging faults using minimal stuck-at fault test sets

NASA Technical Reports Server (NTRS)

Ijaz, Nabeel; Frenzel, James F.

1993-01-01

The performance of minimal stuck-at fault test sets at detecting bridging faults are evaluated. New functional models of circuit primitives are presented which allow accurate representation of bridging faults under switch-level simulation. The effectiveness of the patterns is evaluated using both voltage and current testing.

Engine condition monitoring: CF6 family 60's through the 80's

NASA Technical Reports Server (NTRS)

Kent, H. J.; Dienger, G.

1981-01-01

The on condition program is described in terms of its effectiveness as a maintenance tool both at the line station as well as at home base by the early detection of engine faults, erroneous instrumentation signals and by verification of engine health. The system encompasses all known methods from manual procedures to the fully automated airborne integrated data system.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Recent Improvements to the Finite-Fault Rupture Detector Algorithm: FinDer II

NASA Astrophysics Data System (ADS)

Smith, D.; Boese, M.; Heaton, T. H.

2015-12-01

Constraining the finite-fault rupture extent and azimuth is crucial for accurately estimating ground-motion in large earthquakes. Detecting and modeling finite-fault ruptures in real-time is thus essential to both earthquake early warning (EEW) and rapid emergency response. Following extensive real-time and offline testing, the finite-fault rupture detector algorithm, FinDer (Böse et al., 2012 & 2015), was successfully integrated into the California-wide ShakeAlert EEW demonstration system. Since April 2015, FinDer has been scanning real-time waveform data from approximately 420 strong-motion stations in California for peak ground acceleration (PGA) patterns indicative of earthquakes. FinDer analyzes strong-motion data by comparing spatial images of observed PGA with theoretical templates modeled from empirical ground-motion prediction equations (GMPEs). If the correlation between the observed and theoretical PGA is sufficiently high, a report is sent to ShakeAlert including the estimated centroid position, length, and strike, and their uncertainties, of an ongoing fault rupture. Rupture estimates are continuously updated as new data arrives. As part of a joint effort between USGS Menlo Park, ETH Zurich, and Caltech, we have rewritten FinDer in C++ to obtain a faster and more flexible implementation. One new feature of FinDer II is that multiple contour lines of high-frequency PGA are computed and correlated with templates, allowing the detection of both large earthquakes and much smaller (~ M3.5) events shortly after their nucleation. Unlike previous EEW algorithms, FinDer II thus provides a modeling approach for both small-magnitude point-source and larger-magnitude finite-fault ruptures with consistent error estimates for the entire event magnitude range.

Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

PubMed

Li, Yunji; Wu, QingE; Peng, Li

2018-01-23

In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

NASA Technical Reports Server (NTRS)

Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

1992-01-01

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Spatial-temporal evolutions of early aftershocks following the 2013 Mw 6.6 Lushan earthquake in Sichuan, China

NASA Astrophysics Data System (ADS)

Wu, Jing; Yao, Dongdong; Meng, Xiaofeng; Peng, Zhigang; Su, Jinrong; Long, Feng

2017-04-01

We perform a comprehensive detection of early aftershocks following the 2013 Mw 6.6 Lushan earthquake, which occurred in the southern Longmenshan Fault Zone in Sichuan Province, China, about 5 years after the 2008 Mw 7.9 Wenchuan earthquake. We use events in both standard and relocated catalogs as templates to scan through continuous waveforms 2 days before and 3 days after the main shock. We successfully reduce the magnitude of completeness Mc by more than 1 order and obtain up to 6 times more events than listed in both catalogs. Aftershocks in the first hour mostly occur around the main shock slip region, and aftershocks at later times show systematic expansions in the along-strike, perpendicular-strike, and updip directions. Although postseismic deformation following the Lushan main shock has not been clearly identified, we suggest that early aftershock expansions are likely driven by afterslip of the Lushan main shock. This is consistent with the observations that most aftershocks were in the stress shadow of the Lushan main shock and that there was significant slip deficit in the top 10 km of the crust. We also find that seismicity on the back thrust fault was activated as soon as 20 min after the main shock, earlier than previously reported. We are unable to detect any clear foreshocks in the last 2 days before the Lushan main shock.

Fault Analysis and Detection in Microgrids with High PV Penetration

DOE Office of Scientific and Technical Information (OSTI.GOV)

El Khatib, Mohamed; Hernandez Alvidrez, Javier; Ellis, Abraham

In this report we focus on analyzing current-controlled PV inverters behaviour under faults in order to develop fault detection schemes for microgrids with high PV penetration. Inverter model suitable for steady state fault studies is presented and the impact of PV inverters on two protection elements is analyzed. The studied protection elements are superimposed quantities based directional element and negative sequence directional element. Additionally, several non-overcurrent fault detection schemes are discussed in this report for microgrids with high PV penetration. A detailed time-domain simulation study is presented to assess the performance of the presented fault detection schemes under different microgridmore » modes of operation.« less

Development of monitoring and diagnostic methods for robots used in remediation of waste sites. 1997 annual progress report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tecza, J.

1998-06-01

'Safe and efficient clean up of hazardous and radioactive waste sites throughout the DOE complex will require extensive use of robots. This research effort focuses on developing Monitoring and Diagnostic (M and D) methods for robots that will provide early detection, isolation, and tracking of impending faults before they result in serious failure. The utility and effectiveness of applying M and D methods to hydraulic robots has never been proven. The present research program is utilizing seeded faults in a laboratory test rig that is representative of an existing hydraulically-powered remediation robot. This report summarizes activity conducted in the firstmore » 9 months of the project. The research team has analyzed the Rosie Mobile Worksystem as a representative hydraulic robot, developed a test rig for implanted fault testing, developed a test plan and agenda, and established methods for acquiring and analyzing the test data.'« less

Maneuver Classification for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.

2003-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

Classification of Aircraft Maneuvers for Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Koga, Dennis (Technical Monitor)

2002-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

PubMed Central

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-01-01

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio. PMID:24776937

Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

NASA Astrophysics Data System (ADS)

Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

2013-12-01

Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can be paired with infrastructure overlays, allowing emergency response teams to identify sites that may have been exposed to damage. The faults will also be incorporated into a database for future integration into fault models and earthquake simulations, improving future earthquake hazard assessment. As new faults are mapped, they will further understanding of the complex fault systems and earthquake hazards within the seismically dynamic state of California.

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

A testing-coverage software reliability model considering fault removal efficiency and error generation.

PubMed

Li, Qiuying; Pham, Hoang

2017-01-01

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

Fault detection of helicopter gearboxes using the multi-valued influence matrix method

NASA Technical Reports Server (NTRS)

Chin, Hsinyung; Danai, Kourosh; Lewicki, David G.

1993-01-01

In this paper we investigate the effectiveness of a pattern classifying fault detection system that is designed to cope with the variability of fault signatures inherent in helicopter gearboxes. For detection, the measurements are monitored on-line and flagged upon the detection of abnormalities, so that they can be attributed to a faulty or normal case. As such, the detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and influence matrix are tuned during a training session so as to minimize the error in detection. To demonstrate the effectiveness of this detection system, it was applied to vibration measurements collected from a helicopter gearbox during normal operation and at various fault instances. The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set.

Fault recovery for real-time, multi-tasking computer system

NASA Technical Reports Server (NTRS)

Hess, Richard (Inventor); Kelly, Gerald B. (Inventor); Rogers, Randy (Inventor); Stange, Kent A. (Inventor)

2011-01-01

System and methods for providing a recoverable real time multi-tasking computer system are disclosed. In one embodiment, a system comprises a real time computing environment, wherein the real time computing environment is adapted to execute one or more applications and wherein each application is time and space partitioned. The system further comprises a fault detection system adapted to detect one or more faults affecting the real time computing environment and a fault recovery system, wherein upon the detection of a fault the fault recovery system is adapted to restore a backup set of state variables.

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

NASA Astrophysics Data System (ADS)

Li, De Z.; Wang, Wilson; Ismail, Fathy

2017-11-01

Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong

2011-01-01

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

The Late Oligocene to Early Miocene early evolution of rifting in the southwestern part of the Roer Valley Graben

NASA Astrophysics Data System (ADS)

Deckers, Jef

2016-06-01

The Roer Valley Graben is a Mesozoic continental rift basin that was reactivated during the Late Oligocene. The study area is located in the graben area of the southwestern part of the Roer Valley Graben. Rifting initiated in the study area with the development of a large number of faults in the prerift strata. Some of these faults were rooted in preexisting zones of weakness in the Mesozoic strata. Early in the Late Oligocene, several faults died out in the study area as strain became focused upon others, some of which were able to link into several-kilometer-long systems. Within the Late Oligocene to Early Miocene northwestward prograding shallow marine syn-rift deposits, the number of active faults further decreased with time. A relatively strong decrease was observed around the Oligocene/Miocene boundary and represents a further focus of strain onto the long fault systems. Miocene extensional strain was not accommodated by further growth, but predominantly by displacements along the long fault systems. Since the Oligocene/Miocene boundary coincides with a radical change in the European intraplate stress field, the latter might have contributed significantly to the simultaneous change of fault kinematics in the study area.

Identifiability of Additive Actuator and Sensor Faults by State Augmentation

NASA Technical Reports Server (NTRS)

Joshi, Suresh; Gonzalez, Oscar R.; Upchurch, Jason M.

2014-01-01

A class of fault detection and identification (FDI) methods for bias-type actuator and sensor faults is explored in detail from the point of view of fault identifiability. The methods use state augmentation along with banks of Kalman-Bucy filters for fault detection, fault pattern determination, and fault value estimation. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults is presented. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions are demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.

Automatic Channel Fault Detection on a Small Animal APD-Based Digital PET Scanner

NASA Astrophysics Data System (ADS)

Charest, Jonathan; Beaudoin, Jean-François; Cadorette, Jules; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

2014-10-01

Avalanche photodiode (APD) based positron emission tomography (PET) scanners show enhanced imaging capabilities in terms of spatial resolution and contrast due to the one to one coupling and size of individual crystal-APD detectors. However, to ensure the maximal performance, these PET scanners require proper calibration by qualified scanner operators, which can become a cumbersome task because of the huge number of channels they are made of. An intelligent system (IS) intends to alleviate this workload by enabling a diagnosis of the observational errors of the scanner. The IS can be broken down into four hierarchical blocks: parameter extraction, channel fault detection, prioritization and diagnosis. One of the main activities of the IS consists in analyzing available channel data such as: normalization coincidence counts and single count rates, crystal identification classification data, energy histograms, APD bias and noise thresholds to establish the channel health status that will be used to detect channel faults. This paper focuses on the first two blocks of the IS: parameter extraction and channel fault detection. The purpose of the parameter extraction block is to process available data on individual channels into parameters that are subsequently used by the fault detection block to generate the channel health status. To ensure extensibility, the channel fault detection block is divided into indicators representing different aspects of PET scanner performance: sensitivity, timing, crystal identification and energy. Some experiments on a 8 cm axial length LabPET scanner located at the Sherbrooke Molecular Imaging Center demonstrated an erroneous channel fault detection rate of 10% (with a 95% confidence interval (CI) of [9, 11]) which is considered tolerable. Globally, the IS achieves a channel fault detection efficiency of 96% (CI: [95, 97]), which proves that many faults can be detected automatically. Increased fault detection efficiency would be advantageous but, the achieved results would already benefit scanner operators in their maintenance task.

Structural architecture and tectonic evolution of the Maghara inverted basin, Northern Sinai, Egypt

NASA Astrophysics Data System (ADS)

Moustafa, Adel R.

2014-05-01

Large NE-SW oriented asymmetric inversion anticlines bounded on their southeastern sides by reverse faults affect the exposed Mesozoic and Cenozoic sedimentary rocks of the Maghara area (northern Sinai). Seismic data indicate an earlier Jurassic rifting phase and surface structures indicate Late Cretaceous-Early Tertiary inversion phase. The geometry of the early extensional fault system clearly affected the sense of slip of the inverted faults and the geometry of the inversion anticlines. Rift-parallel fault segments were reactivated by reverse slip whereas rift-oblique fault segments were reactivated as oblique-slip faults or lateral/oblique ramps. New syn-inversion faults include two short conjugate strike-slip sets dissecting the forelimbs of inversion anticlines and the inverted faults as well as a set of transverse normal faults dissecting the backlimbs. Small anticline-syncline fold pairs ornamenting the steep flanks of the inversion anticlines are located at the transfer zones between en echelon segments of the inverted faults.

Joint Seismic-Geodetic Algorithm for Finite-Fault Detection and Slip Inversion in the West Coast ShakeAlert System

NASA Astrophysics Data System (ADS)

Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Murray, J. R.

2016-12-01

Finite-fault source algorithms can greatly benefit earthquake early warning (EEW) systems. Estimates of finite-fault parameters provide spatial information, which can significantly improve real-time shaking calculations and help with disaster response. In this project, we have focused on integrating a finite-fault seismic-geodetic algorithm into the West Coast ShakeAlert framework. The seismic part is FinDer 2, a C++ version of the algorithm developed by Böse et al. (2012). It interpolates peak ground accelerations and calculates the best fault length and strike from template matching. The geodetic part is a C++ version of BEFORES, the algorithm developed by Minson et al. (2014) that uses a Bayesian methodology to search for the most probable slip distribution on a fault of unknown orientation. Ultimately, these two will be used together where FinDer generates a Bayesian prior for BEFORES via the methodology of Minson et al. (2015), and the joint solution will generate estimates of finite-fault extent, strike, dip, best slip distribution, and magnitude. We have created C++ versions of both FinDer and BEFORES using open source libraries and have developed a C++ Application Protocol Interface (API) for them both. Their APIs allow FinDer and BEFORES to contribute to the ShakeAlert system via an open source messaging system, ActiveMQ. FinDer has been receiving real-time data, detecting earthquakes, and reporting messages on the development system for several months. We are also testing FinDer extensively with Earthworm tankplayer files. BEFORES has been tested with ActiveMQ messaging in the ShakeAlert framework, and works off a FinDer trigger. We are finishing the FinDer-BEFORES connections in this framework, and testing this system via seismic-geodetic tankplayer files. This will include actual and simulated data.

Fault recovery characteristics of the fault tolerant multi-processor

NASA Technical Reports Server (NTRS)

Padilla, Peter A.

1990-01-01

The fault handling performance of the fault tolerant multiprocessor (FTMP) was investigated. Fault handling errors detected during fault injection experiments were characterized. In these fault injection experiments, the FTMP disabled a working unit instead of the faulted unit once every 500 faults, on the average. System design weaknesses allow active faults to exercise a part of the fault management software that handles byzantine or lying faults. It is pointed out that these weak areas in the FTMP's design increase the probability that, for any hardware fault, a good LRU (line replaceable unit) is mistakenly disabled by the fault management software. It is concluded that fault injection can help detect and analyze the behavior of a system in the ultra-reliable regime. Although fault injection testing cannot be exhaustive, it has been demonstrated that it provides a unique capability to unmask problems and to characterize the behavior of a fault-tolerant system.

Shallow Faulting in Morelia, Mexico, Based on Seismic Tomography and Geodetically Detected Land Subsidence

NASA Astrophysics Data System (ADS)

Cabral-Cano, E.; Arciniega-Ceballos, A.; Vergara-Huerta, F.; Chaussard, E.; Wdowinski, S.; DeMets, C.; Salazar-Tlaczani, L.

2013-12-01

Subsidence has been a common occurrence in several cities in central Mexico for the past three decades. This process causes substantial damage to the urban infrastructure and housing in several cities and it is a major factor to be considered when planning urban development, land-use zoning and hazard mitigation strategies. Since the early 1980's the city of Morelia in Central Mexico has experienced subsidence associated with groundwater extraction in excess of natural recharge from rainfall. Previous works have focused on the detection and temporal evolution of the subsidence spatial distribution. The most recent InSAR analysis confirms the permanence of previously detected rapidly subsiding areas such as the Rio Grande Meander area and also defines 2 subsidence patches previously undetected in the newly developed suburban sectors west of Morelia at the Fraccionamiento Del Bosque along, south of Hwy. 15 and another patch located north of Morelia along Gabino Castañeda del Rio Ave. Because subsidence-induced, shallow faulting develops at high horizontal strain localization, newly developed a subsidence areas are particularly prone to faulting and fissuring. Shallow faulting increases groundwater vulnerability because it disrupts discharge hydraulic infrastructure and creates a direct path for transport of surface pollutants into the underlying aquifer. Other sectors in Morelia that have been experiencing subsidence for longer time have already developed well defined faults such as La Colina, Central Camionera, Torremolinos and La Paloma faults. Local construction codes in the vicinity of these faults define a very narrow swath along which housing construction is not allowed. In order to better characterize these fault systems and provide better criteria for future municipal construction codes we have surveyed the La Colina and Torremolinos fault systems in the western sector of Morelia using seismic tomographic techniques. Our results indicate that La Colina Fault include secondary faults at depths up to 4-8m below the surface and located up to 24m away from the main fault trace. The Torremolinos fault system includes secondary faults, which are present up to 8m deep and 12-18m away from the main fault trace. Even though the InSAR analysis provides an unsurpassed synoptic view, a higher temporal resolution observation of fault movement has been pursued using the MOIT continuously operating GPS station, which is located within 100 m from the La Colina main fault trace. GPS data is also particularly useful to decompose horizontal and vertical motion in the absence of both ascending and descending SAR data acquisitions. Observations since July 2009 show a total general displacement trend of -39mm/yr and a total horizontal differential motion of 41.8 mm/yr and -4.7mm/yr in its latitudinal and Longitudinal components respectively in respect to the motion observed at the MOGA GPS station located 5.0 km to the SSE within an area which is not affected by subsidence. In addition to the overall trend, high amplitude excursions at the MOIT station with individual residual amplitudes up to 20mm, 25mm, and 60mm in its latitudinal, longitudinal and vertical components respectively vertical are observed. The correlation of fault motion excursions in relationship to the rainfall records will be analyzed.

Fault detection and isolation

NASA Technical Reports Server (NTRS)

Bernath, Greg

1994-01-01

In order for a current satellite-based navigation system (such as the Global Positioning System, GPS) to meet integrity requirements, there must be a way of detecting erroneous measurements, without help from outside the system. This process is called Fault Detection and Isolation (FDI). Fault detection requires at least one redundant measurement, and can be done with a parity space algorithm. The best way around the fault isolation problem is not necessarily isolating the bad measurement, but finding a new combination of measurements which excludes it.

Prototype Earthquake Early Warning System for Areas of Highest Seismic Risk in the Western U.S.

NASA Astrophysics Data System (ADS)

Bock, Y.; Geng, J.; Goldberg, D.; Saunders, J. K.; Haase, J. S.; Squibb, M. B.; Melgar, D.; Crowell, B. W.; Clayton, R. W.; Yu, E.; Walls, C. P.; Mann, D.; Mencin, D.; Mattioli, G. S.

2015-12-01

We report on a prototype earthquake early warning system for the Western U.S. based on GNSS (GPS+GLONASS) observations, and where available collocated GNSS and accelerometer data (seismogeodesy). We estimate with latency of 2-3 seconds GNSS displacement waveforms from more than 120 stations, focusing on the southern segment of the San Andreas fault, the Hayward and Rodgers Creek faults and Cascadia. The displacements are estimated using precise point positioning with ambiguity resolution (PPP-AR), which provides for efficient processing of hundreds of "clients" within the region of interest with respect to a reference frame well outside the expected zone of deformation. The GNSS displacements are useful for alleviating magnitude saturation concerns, rapid earthquake magnitude estimation using peak ground displacements, CMT solutions and finite fault slip models. However, GNSS alone is insufficient for strict earthquake early warning (i.e., P wave detection). Therefore, we employ a self-contained seismogeodetic technique, where collocations of GNSS and accelerometer instruments are available, to estimate real-time displacement and velocity waveforms using PPP-AR with accelerometers (PPP-ARA). Using the velocity waveforms we can detect the P wave arrival for earthquakes of interest (>M 5.5), estimate a hypocenter, S wave propagation, and earthquake magnitude using Pd scaling relationships within seconds. Currently we are gearing up to receive observatory-grade accelerometer data from the CISN. We have deployed 25 inexpensive MEMS accelerometers at existing GNSS stations. The SIO Geodetic Modules that control the flow of the GNSS and accelerometer data are being upgraded with in situ PPP-ARA and P wave picking. In situ processing allows us to use the data at the highest sampling rate of the GNSS receiver (10 Hz or higher), in combination with the 100 Hz accelerometer data. Adding the GLONASS data allows for increased precision in the vertical, an important factor in P wave detection, and by reducing outliers, increasing the number of visible satellites and significantly reducing the time required for reinitialization of phase ambiguities. We plan to make our displacement and velocity waveforms available to the USGS ShakeAlert system and others in Earthworm format.

Potential earthquake faults offshore Southern California, from the eastern Santa Barbara Channel south to Dana Point

USGS Publications Warehouse

Fisher, M.A.; Sorlien, C.C.; Sliter, R.W.

2009-01-01

Urban areas in Southern California are at risk from major earthquakes, not only quakes generated by long-recognized onshore faults but also ones that occur along poorly understood offshore faults. We summarize recent research findings concerning these lesser known faults. Research by the U.S. Geological Survey during the past five years indicates that these faults from the eastern Santa Barbara Channel south to Dana Point pose a potential earthquake threat. Historical seismicity in this area indicates that, in general, offshore faults can unleash earthquakes having at least moderate (M 5-6) magnitude. Estimating the earthquake hazard in Southern California is complicated by strain partitioning and by inheritance of structures from early tectonic episodes. The three main episodes are Mesozoic through early Miocene subduction, early Miocene crustal extension coeval with rotation of the Western Transverse Ranges, and Pliocene and younger transpression related to plate-boundary motion along the San Andreas Fault. Additional complication in the analysis of earthquake hazards derives from the partitioning of tectonic strain into strike-slip and thrust components along separate but kinematically related faults. The eastern Santa Barbara Basin is deformed by large active reverse and thrust faults, and this area appears to be underlain regionally by the north-dipping Channel Islands thrust fault. These faults could produce moderate to strong earthquakes and destructive tsunamis. On the Malibu coast, earthquakes along offshore faults could have left-lateral-oblique focal mechanisms, and the Santa Monica Mountains thrust fault, which underlies the oblique faults, could give rise to large (M ??7) earthquakes. Offshore faults near Santa Monica Bay and the San Pedro shelf are likely to produce both strike-slip and thrust earthquakes along northwest-striking faults. In all areas, transverse structures, such as lateral ramps and tear faults, which crosscut the main faults, could segment earthquake rupture zones. ?? 2009 The Geological Society of America.

Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

2017-06-01

In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

A testing-coverage software reliability model considering fault removal efficiency and error generation

PubMed Central

Li, Qiuying; Pham, Hoang

2017-01-01

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance. PMID:28750091

Fault detection of Tennessee Eastman process based on topological features and SVM

NASA Astrophysics Data System (ADS)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

Event-Triggered Fault Detection of Nonlinear Networked Systems.

PubMed

Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

2017-04-01

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices.

PubMed

Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang

2017-03-01

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Solar system fault detection

DOEpatents

Farrington, R.B.; Pruett, J.C. Jr.

1984-05-14

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Solar system fault detection

DOEpatents

Farrington, Robert B.; Pruett, Jr., James C.

1986-01-01

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

From experiment to design -- Fault characterization and detection in parallel computer systems using computational accelerators

NASA Astrophysics Data System (ADS)

Yim, Keun Soo

This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of program states that included dynamically allocated memory (to be spatially comprehensive). In GPUs, we used fault injection studies to demonstrate the importance of detecting silent data corruption (SDC) errors that are mainly due to the lack of fine-grained protections and the massive use of fault-insensitive data. This dissertation also presents transparent fault tolerance frameworks and techniques that are directly applicable to hybrid computers built using only commercial off-the-shelf hardware components. This dissertation shows that by developing understanding of the failure characteristics and error propagation paths of target programs, we were able to create fault tolerance frameworks and techniques that can quickly detect and recover from hardware faults with low performance and hardware overheads.

A Sparsity-based Framework for Resolution Enhancement in Optical Fault Analysis of Integrated Circuits

DTIC Science & Technology

2015-01-01

for IC fault detection . This section provides background information on inversion methods. Conventional inversion techniques and their shortcomings are...physical techniques, electron beam imaging/analysis, ion beam techniques, scanning probe techniques. Electrical tests are used to detect faults in 13 an...hand, there is also the second harmonic technique through which duty cycle degradation faults are detected by collecting the magnitude and the phase of

Creating an automated chiller fault detection and diagnostics tool using a data fault library.

PubMed

Bailey, Margaret B; Kreider, Jan F

2003-07-01

Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.

Arc burst pattern analysis fault detection system

NASA Technical Reports Server (NTRS)

Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

1997-01-01

A method and apparatus are provided for detecting an arcing fault on a power line carrying a load current. Parameters indicative of power flow and possible fault events on the line, such as voltage and load current, are monitored and analyzed for an arc burst pattern exhibited by arcing faults in a power system. These arcing faults are detected by identifying bursts of each half-cycle of the fundamental current. Bursts occurring at or near a voltage peak indicate arcing on that phase. Once a faulted phase line is identified, a comparison of the current and voltage reveals whether the fault is located in a downstream direction of power flow toward customers, or upstream toward a generation station. If the fault is located downstream, the line is de-energized, and if located upstream, the line may remain energized to prevent unnecessary power outages.

Multiple fault separation and detection by joint subspace learning for the health assessment of wind turbine gearboxes

NASA Astrophysics Data System (ADS)

Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Zi, Yanyang; Yan, Ruqiang

2017-09-01

The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.

Advanced power system protection and incipient fault detection and protection of spaceborne power systems

NASA Technical Reports Server (NTRS)

Russell, B. Don

1989-01-01

This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

Fault tolerant filtering and fault detection for quantum systems driven by fields in single photon states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Qing, E-mail: qing.gao.chance@gmail.com; Dong, Daoyi, E-mail: daoyidong@gmail.com; Petersen, Ian R., E-mail: i.r.petersen@gmai.com

The purpose of this paper is to solve the fault tolerant filtering and fault detection problem for a class of open quantum systems driven by a continuous-mode bosonic input field in single photon states when the systems are subject to stochastic faults. Optimal estimates of both the system observables and the fault process are simultaneously calculated and characterized by a set of coupled recursive quantum stochastic differential equations.

Coseismic and Postseismic Deformation Following the 2011 Mw 9.0 Tohoku Earthquake and its Mw 7.9 Aftershock: Searching for Fault-localized Relaxation of Coseismic Stress Increments

NASA Astrophysics Data System (ADS)

Wang, F.; Bevis, M. G.; Blewitt, G.; Gomez, D.

2017-12-01

We study the postseismic transient displacements following the 2011 Mw 9.0 Tohoku earthquake using the Nevada Geodetic Laboratory's daily and 5-minute interval PPP solutions for 1,272 continuous GPS stations in Japan, with particular emphasis on the early transient displacements of these stations. One significant complication is the Mw 7.9 aftershock that occurred just 29.3 minutes after the main shock, since the coseismic (and postseismic) displacements driven by the aftershock are superimposed on the postseismic transients driven by the main shock. We address the question of whether or not the stresses induced by the Mw 9.0 main shock were relaxed by any major faults within Japan. The notion is that significant stress relaxation which is localized on a fault system should be manifested in the spatial pattern of the postseismic transient displacement field in the vicinity of that system. This would provide a basis for distinguishing between faults that engage in stick-slip behavior and those that creep instead. The distinction is important in that it has implications for the seismic risk associated with upper plate faulting. We will make the case that we do detect localized fault creeping in response to the coseismic stress field produced by the Mw 9 event.

Development of a morphological convolution operator for bearing fault detection

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

2018-05-01

This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

Early warning of freshwater salinization due to upward brine displacement by species transport simulations combined with a hydrochemical genesis model

NASA Astrophysics Data System (ADS)

Langer, Maria; Kühn, Michael

2016-04-01

Shallow groundwater resources could be possibly affected by intruding brines, which are displaced along hydraulically conductive faults as result of subsurface activities like CO2 injection. To avoid salinization of potable freshwater aquifers an early detection of intruding saline water is necessary, especially in regions where an initial geogenic salinization already exists. Our study is based on work of Tillner et al. [1] and Langer et al. [2] who investigated the influence of permeable fault systems on brine displacement for the prospective storage site Beeskow-Birkholz in the Northeast German Basin. With a 3D regional scale model considering the deep groundwater system, they demonstrated that the existence of hydraulically conductive faults is not necessarily an exclusion criterion for potential injection sites, because salinization of shallower aquifers strongly depends on the effective damage zone volume, the initial salinity distribution and overlying reservoirs [2], while permeability of fault zones does not influence salinization of shallower aquifers significantly [1]. Here we extracted a 2D cross section regarding the upper 220 m of the study area mainly represented by shallow freshwater aquifers, but also considering an initial geogenic salinization [3]. We took flow rates of the intruding brines from the previous studies [2] and implemented species transport simulations with the program code SHEMAT [4]. Results are investigated and interpreted with the hydrochemical genesis model GEBAH [5] which has been already applied as early warning of saltwater intrusions into freshwater aquifers and surface water [6]. GEBAH allows a categorization of groundwater by the ion ratios of the dissolved components and offers a first indicative determination for an existence and the intensity of saline water intrusion in shallow groundwater aquifer, independent of the concentration of the solution. With our model we investigated the migration of saline water through a fault or an erosional channel which both allows an exchange between the shallow freshwater and the deeper saline water complex. The salinization potential of a drinking water well in vicinity to the brine source was determined for different scenarios. [1] Tillner E., Kempka T., Nakaten B., Kühn M. (2013) Brine migration through fault zones: 3D numerical simulations for a prospective CO2 storage site in Northeast Germany. International Journal of Greenhouse Gas Control 19, 689-703. doi: 10.1016/ j.ijggc.2013.03.012 [2] Langer M., Tillner E., Kempka T., Kühn M. (2015) Effective damage zone volume of fault zones and initial salinity distribution determine intensity of shallow aquifer salinization in geological underground utilization. Hydrology and Earth System Sciences Discussion, 12, 5703-5748. doi: 10.5194/hessd-12-5703-2015 [3] Hotzan, G., and Voss, T. (2013): Complex hydrogeochemic-genetic mapping for evaluation of the endangerment of pleistocene and tertiary aquifers by saline waters in the region Storkow-Frankfurt (Oder)-Eisenhüttenstadt. Brandenburgische Geowissenschaftliche Beiträge, 20 (1/2), 62-82. (in German) [4] Clauser C. (2003) SHEMAT and Processing SHEMAT - Numerical simulation of reactive flow in hot aquifers, Springer Publishers, Heidelberg [5] Rechlin, B., Hoffknecht, A., Scholz, H., Helms, A. (2010): Genetic evaluation of analyses from the hydrosphere. Software GEBAH Vers. 1.1 LBGR/GCI, Cottbus, Königs Wusterhausen (in German) [6] Rechlin, B. (2008): A method for a concentration free early detection of saltwater intrusions into freshwater aquifers and surface water. Brandenburgische Geowissenschaftliche Beiträge, 15 (1/2), 57-68. (in German)

Solar Photovoltaic (PV) Distributed Generation Systems - Control and Protection

NASA Astrophysics Data System (ADS)

Yi, Zhehan

This dissertation proposes a comprehensive control, power management, and fault detection strategy for solar photovoltaic (PV) distribution generations. Battery storages are typically employed in PV systems to mitigate the power fluctuation caused by unstable solar irradiance. With AC and DC loads, a PV-battery system can be treated as a hybrid microgrid which contains both DC and AC power resources and buses. In this thesis, a control power and management system (CAPMS) for PV-battery hybrid microgrid is proposed, which provides 1) the DC and AC bus voltage and AC frequency regulating scheme and controllers designed to track set points; 2) a power flow management strategy in the hybrid microgrid to achieve system generation and demand balance in both grid-connected and islanded modes; 3) smooth transition control during grid reconnection by frequency and phase synchronization control between the main grid and microgrid. Due to the increasing demands for PV power, scales of PV systems are getting larger and fault detection in PV arrays becomes challenging. High-impedance faults, low-mismatch faults, and faults occurred in low irradiance conditions tend to be hidden due to low fault currents, particularly, when a PV maximum power point tracking (MPPT) algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. In this dissertation, fault detection challenges in PV arrays are analyzed in depth, considering the crossing relations among the characteristics of PV, interactions with MPPT algorithms, and the nature of solar irradiance. Two fault detection schemes are then designed as attempts to address these technical issues, which detect faults inside PV arrays accurately even under challenging circumstances, e.g., faults in low irradiance conditions or high-impedance faults. Taking advantage of multi-resolution signal decomposition (MSD), a powerful signal processing technique based on discrete wavelet transformation (DWT), the first attempt is devised, which extracts the features of both line-to-line (L-L) and line-to-ground (L-G) faults and employs a fuzzy inference system (FIS) for the decision-making stage of fault detection. This scheme is then improved as the second attempt by further studying the system's behaviors during L-L faults, extracting more efficient fault features, and devising a more advanced decision-making stage: the two-stage support vector machine (SVM). For the first time, the two-stage SVM method is proposed in this dissertation to detect L-L faults in PV system with satisfactory accuracies. Numerous simulation and experimental case studies are carried out to verify the proposed control and protection strategies. Simulation environment is set up using the PSCAD/EMTDC and Matlab/Simulink software packages. Experimental case studies are conducted in a PV-battery hybrid microgrid using the dSPACE real-time controller to demonstrate the ease of hardware implementation and the controller performance. Another small-scale grid-connected PV system is set up to verify both fault detection algorithms which demonstrate promising performances and fault detecting accuracies.

Abnormal fault-recovery characteristics of the fault-tolerant multiprocessor uncovered using a new fault-injection methodology

NASA Technical Reports Server (NTRS)

Padilla, Peter A.

1991-01-01

An investigation was made in AIRLAB of the fault handling performance of the Fault Tolerant MultiProcessor (FTMP). Fault handling errors detected during fault injection experiments were characterized. In these fault injection experiments, the FTMP disabled a working unit instead of the faulted unit once in every 500 faults, on the average. System design weaknesses allow active faults to exercise a part of the fault management software that handles Byzantine or lying faults. Byzantine faults behave such that the faulted unit points to a working unit as the source of errors. The design's problems involve: (1) the design and interface between the simplex error detection hardware and the error processing software, (2) the functional capabilities of the FTMP system bus, and (3) the communication requirements of a multiprocessor architecture. These weak areas in the FTMP's design increase the probability that, for any hardware fault, a good line replacement unit (LRU) is mistakenly disabled by the fault management software.

Mesozoic strike-slip movement of the Dunhua-Mishan Fault Zone in NE China: A response to oceanic plate subduction

NASA Astrophysics Data System (ADS)

Liu, Cheng; Zhu, Guang; Zhang, Shuai; Gu, Chengchuan; Li, Yunjian; Su, Nan; Xiao, Shiye

2018-01-01

The NE-striking Dunhua-Mishan Fault Zone (DMFZ) is one of two branches of the continental-scale sinistral Tan-Lu Fault Zone in NE China. The field data presented here indicate that the ca. 1000 km long DMFZ records two phases of sinistral faulting. The structures produced by these two phases of faulting include NE-SW-striking ductile shear belts and brittle faults, respectively. Mylonite-hosted microstructures and quartz c-axis fabrics suggest deformation temperatures of 450 °C-500 °C for the ductile shear belts. Combining new zircon U-Pb dates for 14 igneous rock samples analyzed during this study with the geology of this region indicates these shear belts formed during the earliest Early Cretaceous. This phase of sinistral displacement represents the initial formation of the DMFZ in response to the northward propagation of the Tan-Lu Fault Zone into NE China. A phase of Early Cretaceous rifting was followed by a second phase of sinistral faulting at 102-96 Ma, as evidenced by our new U-Pb ages for associated igneous rocks. Combining our new data with the results of previous research indicates that the DFMZ records a four-stage Cretaceous evolutionary history, where initial sinistral faulting at the beginning of the Early Cretaceous gave way to rifting during the rest of the Early Cretaceous. This was followed by a second phase of sinistral faulting at the beginning of the Late Cretaceous and a second phase of local rifting during the rest of the Late Cretaceous. The Cretaceous evolution of the DMFZ records the synchronous tectonic evolution of the NE China continent bordering the Pacific Ocean. Two phases of regional N-S compression generated the two phases of sinistral faulting within the DMFZ, whereas two-stage regional extension generated the two phases of rifting. The two compressive events were the result of the rapid low-angle subduction of the Izanagi and Pacific plates, whereas the two-stage extension was caused by the roll-back of these respective plates. The final closure of the Mongol-Okhotsk Ocean at the beginning of the Early Cretaceous intensified the synchronous compression in NE China, causing the northward propagation of the Tan-Lu Fault Zone.

Switch failure diagnosis based on inductor current observation for boost converters

NASA Astrophysics Data System (ADS)

Jamshidpour, E.; Poure, P.; Saadate, S.

2016-09-01

Face to the growing number of applications using DC-DC power converters, the improvement of their reliability is subject to an increasing number of studies. Especially in safety critical applications, designing fault-tolerant converters is becoming mandatory. In this paper, a switch fault-tolerant DC-DC converter is studied. First, some of the fastest Fault Detection Algorithms (FDAs) are recalled. Then, a fast switch FDA is proposed which can detect both types of failures; open circuit fault as well as short circuit fault can be detected in less than one switching period. Second, a fault-tolerant converter which can be reconfigured under those types of fault is introduced. Hardware-In-the-Loop (HIL) results and experimental validations are given to verify the validity of the proposed switch fault-tolerant approach in the case of a single switch DC-DC boost converter with one redundant switch.

System for detecting and limiting electrical ground faults within electrical devices

DOEpatents

Gaubatz, Donald C.

1990-01-01

An electrical ground fault detection and limitation system for employment with a nuclear reactor utilizing a liquid metal coolant. Elongate electromagnetic pumps submerged within the liquid metal coolant and electrical support equipment experiencing an insulation breakdown occasion the development of electrical ground fault current. Without some form of detection and control, these currents may build to damaging power levels to expose the pump drive components to liquid metal coolant such as sodium with resultant undesirable secondary effects. Such electrical ground fault currents are detected and controlled through the employment of an isolated power input to the pumps and with the use of a ground fault control conductor providing a direct return path from the affected components to the power source. By incorporating a resistance arrangement with the ground fault control conductor, the amount of fault current permitted to flow may be regulated to the extent that the reactor may remain in operation until maintenance may be performed, notwithstanding the existence of the fault. Monitors such as synchronous demodulators may be employed to identify and evaluate fault currents for each phase of a polyphase power, and control input to the submerged pump and associated support equipment.

Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

NASA Astrophysics Data System (ADS)

Boutros, Tony; Liang, Ming

2011-08-01

Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

NASA Technical Reports Server (NTRS)

Anzalone, Evan

2018-01-01

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of both magnitude and time. As such, the Navigation team is taking advantage of the INS's capability to schedule and change fault detection thresholds in flight. These values are optimized along a nominal trajectory in order to maximize probability of mission success, and reducing the probability of false positives (defined as when the INS would report a second fault condition resulting in loss of mission, but the vehicle would still meet insertion requirements within system-level margins). This paper will describe an optimization approach using Genetic Algorithms to tune the threshold parameters to maximize vehicle resilience to second fault events as a function of potential fault magnitude and time of fault over an ascent mission profile. The analysis approach, and performance assessment of the results will be presented to demonstrate the applicability of this process to second fault detection to maximize mission probability of success.

Model-Based Fault Tolerant Control

NASA Technical Reports Server (NTRS)

Kumar, Aditya; Viassolo, Daniel

2008-01-01

The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

PubMed

Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

2017-10-03

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

NASA Astrophysics Data System (ADS)

Golafshan, Reza; Yuce Sanliturk, Kenan

2016-03-01

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis

DTIC Science & Technology

2014-10-02

takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper...different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the...maintenance strategy requires the technologies of: (a) on- line condition monitoring, (b) fault detection and diagnosis, and (c) prognostics. Figure 1

Multi-thresholds for fault isolation in the presence of uncertainties.

PubMed

Touati, Youcef; Mellal, Mohamed Arezki; Benazzouz, Djamel

2016-05-01

Monitoring of the faults is an important task in mechatronics. It involves the detection and isolation of faults which are performed by using the residuals. These residuals represent numerical values that define certain intervals called thresholds. In fact, the fault is detected if the residuals exceed the thresholds. In addition, each considered fault must activate a unique set of residuals to be isolated. However, in the presence of uncertainties, false decisions can occur due to the low sensitivity of certain residuals towards faults. In this paper, an efficient approach to make decision on fault isolation in the presence of uncertainties is proposed. Based on the bond graph tool, the approach is developed in order to generate systematically the relations between residuals and faults. The generated relations allow the estimation of the minimum detectable and isolable fault values. The latter is used to calculate the thresholds of isolation for each residual. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A review on data-driven fault severity assessment in rolling bearings

NASA Astrophysics Data System (ADS)

Cerrada, Mariela; Sánchez, René-Vinicio; Li, Chuan; Pacheco, Fannia; Cabrera, Diego; Valente de Oliveira, José; Vásquez, Rafael E.

2018-01-01

Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.

Automatic Fault Characterization via Abnormality-Enhanced Classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bronevetsky, G; Laguna, I; de Supinski, B R

Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help tomore » identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.« less

Early Miocene shortening in the lower Comondú Group in Baja California Sur (México)

NASA Astrophysics Data System (ADS)

Bonini, Marco; Cerca, Mariano; Moratti, Giovanna; López-Martínez, Margarita; Corti, Giacomo; Gracia-Marroquín, Diego

2017-11-01

The Late Oligocene-Early Miocene volcaniclastic deposits of Baja California Sur form most of the exposed western margin of the Gulf of California rift. In some places these deposits, collectively referred to as Comondú Group, show complex deformation patterns given by the coexistence of tectonic and gravitational features. The area north of La Paz is characterized by the occurrence of several slump bodies, which are displaced by normal faults connected with the rift opening. In some places we have identified 100's m scale thrust-related folds and reverse faults that we have interpreted as shortening features. The latter displace the slump layers and are offset by the normal faults. If confirmed, this would represent the first report of a shortening event in the Early Miocene volcaniclastic deposits of Baja California Sur. The observed shortening has modest magnitude (ca 3-5% bulk shortening), and has been detected in a sector extending over 100 km north from La Paz. New 40Ar-39*Ar ages, integrated with existing radiometric age datasets, constrain the timing of this shortening episode. The rocks affected by shortening have ages between 24 and 21 Ma, and are capped by undeformed volcanic rocks with ages spanning between 19.4 and 17.2 Ma. These relationships define an intra-Early Miocene unconformity, which we interpret to be related to the shortening deformation. The available timing constraints allow us to infer that a main ENE-to-ESE-trending shortening was short-lived, possibly ca. 19.4-21 Ma. The account of this shortening event may shed some light on the complex subduction and microplate processes that preceded the continental rifting of the Gulf of California.

Immunity-Based Aircraft Fault Detection System

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

Fault kinematics and localised inversion within the Troms-Finnmark Fault Complex, SW Barents Sea

NASA Astrophysics Data System (ADS)

Zervas, I.; Omosanya, K. O.; Lippard, S. J.; Johansen, S. E.

2018-04-01

The areas bounding the Troms-Finnmark Fault Complex are affected by complex tectonic evolution. In this work, the history of fault growth, reactivation, and inversion of major faults in the Troms-Finnmark Fault Complex and the Ringvassøy Loppa Fault Complex is interpreted from three-dimensional seismic data, structural maps and fault displacement plots. Our results reveal eight normal faults bounding rotated fault blocks in the Troms-Finnmark Fault Complex. Both the throw-depth and displacement-distance plots show that the faults exhibit complex configurations of lateral and vertical segmentation with varied profiles. Some of the faults were reactivated by dip-linkages during the Late Jurassic and exhibit polycyclic fault growth, including radial, syn-sedimentary, and hybrid propagation. Localised positive inversion is the main mechanism of fault reactivation occurring at the Troms-Finnmark Fault Complex. The observed structural styles include folds associated with extensional faults, folded growth wedges and inverted depocentres. Localised inversion was intermittent with rifting during the Middle Jurassic-Early Cretaceous at the boundaries of the Troms-Finnmark Fault Complex to the Finnmark Platform. Additionally, tectonic inversion was more intense at the boundaries of the two fault complexes, affecting Middle Triassic to Early Cretaceous strata. Our study shows that localised folding is either a product of compressional forces or of lateral movements in the Troms-Finnmark Fault Complex. Regional stresses due to the uplift in the Loppa High and halokinesis in the Tromsø Basin are likely additional causes of inversion in the Troms-Finnmark Fault Complex.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

NASA Astrophysics Data System (ADS)

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants.

PubMed

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Negative Selection Algorithm for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Tectono-seismic characteristics of faults in the shallow portion of an accretionary prism

NASA Astrophysics Data System (ADS)

Hirono, Tetsuro; Ishikawa, Tsuyoshi

2018-01-01

To understand the tectono-seismic evolution of faults in the shallow part of a subduction-accretion system, we examined major faults in a fossil accretionary prism, the Emi Group (Hota Group), Boso Peninsula, Japan, by performing multiple structural, geochemical, and mineralogical analyses. Because the strata are relatively shallow (burial depth, 1-4 km), early stage deformation related to subduction, accretion, and uplifting processes is well preserved in three dominant fault zones. On the basis of both previous findings and our geochemical and mineralogical results, we inferred that early stage faulting in a near-trench setting under high pore fluid pressure and second stage faulting at relatively deep along subduction corresponded to aseismic deformations, as shown by velocity strengthening characteristics; and during late stage faulting, probably in association with accretion and uplift processes, a high-temperature fluid, revealed by a geochemical temperature proxy, triggered fault weakening by a thermal pressurization mechanism, and potentially led to the generation of a tsunami.

Rule-based fault diagnosis of hall sensors and fault-tolerant control of PMSM

NASA Astrophysics Data System (ADS)

Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin

2013-07-01

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Effectiveness of back-to-back testing

NASA Technical Reports Server (NTRS)

Vouk, Mladen A.; Mcallister, David F.; Eckhardt, David E.; Caglayan, Alper; Kelly, John P. J.

1987-01-01

Three models of back-to-back testing processes are described. Two models treat the case where there is no intercomponent failure dependence. The third model describes the more realistic case where there is correlation among the failure probabilities of the functionally equivalent components. The theory indicates that back-to-back testing can, under the right conditions, provide a considerable gain in software reliability. The models are used to analyze the data obtained in a fault-tolerant software experiment. It is shown that the expected gain is indeed achieved, and exceeded, provided the intercomponent failure dependence is sufficiently small. However, even with the relatively high correlation the use of several functionally equivalent components coupled with back-to-back testing may provide a considerable reliability gain. Implications of this finding are that the multiversion software development is a feasible and cost effective approach to providing highly reliable software components intended for fault-tolerant software systems, on condition that special attention is directed at early detection and elimination of correlated faults.

Antecedent rivers and early rifting: a case study from the Plio-Pleistocene Corinth rift, Greece

NASA Astrophysics Data System (ADS)

Hemelsdaël, Romain; Ford, Mary; Malartre, Fabrice

2016-04-01

Models of early rifting present syn-rift sedimentation as the direct response to the development of normal fault systems where footwall-derived drainage supplies alluvial to lacustrine sediments into hangingwall depocentres. These models often include antecedent rivers, diverted into active depocentres and with little impact on facies distributions. However, antecedent rivers can supply a high volume of sediment from the onset of rifting. What are the interactions between major antecedent rivers and a growing normal fault system? What are the implications for alluvial stratigraphy and facies distributions in early rifts? These questions are investigated by studying a Plio-Pleistocene fluvial succession on the southern margin of the Corinth rift (Greece). In the northern Peloponnese, early syn-rift deposits are preserved in a series of uplifted E-W normal fault blocks (10-15 km long, 3-7 km wide). Detailed sedimentary logging and high resolution mapping of the syn-rift succession (400 to 1300 m thick) define the architecture of the early rift alluvial system. Magnetostratigraphy and biostratigraphic markers are used to date and correlate the fluvial succession within and between fault blocks. The age of the succession is between 4.0 and 1.8 Ma. We present a new tectonostratigraphic model for early rift basins based on our reconstructions. The early rift depositional system was established across a series of narrow normal fault blocks. Palaeocurrent data show that the alluvial basin was supplied by one major sediment entry point. A low sinuosity braided river system flowed over 15 to 30 km to the NE. Facies evolved downstream from coarse conglomerates to fined-grained fluvial deposits. Other minor sediment entry points supply linked and isolated depocentres. The main river system terminated eastward where it built stacked small deltas into a shallow lake (5 to 15 m deep) that occupied the central Corinth rift. The main fluvial axis remained constant and controlled facies distribution throughout the early rift evolution. We show that the length scale of fluvial facies transitions is greater than and therefore not related to fault spacing. First order facies variations instead occur at the scale of the full antecedent fluvial system. Strike-parallel subsidence variations in individual fault blocks represent a second order controlling factor on stratigraphic architecture. As depocentres enlarged through time, sediments progressively filled palaeorelief, and formed a continuous alluvial plain above active faults. There was limited creation of footwall relief and thus no significant consequent drainage system developed. Here, instead of being diverted toward subsiding zones, the drainage system overfilled the whole rift from the onset of faulting. Moreover, the zones of maximum subsidence on individual faults are aligned across strike parallel to the persistent fluvial axis. This implies that long-term sediment loading influenced the growth of normal faults. We conclude that a major antecedent drainage system inherited from the Hellenide mountain belt supplied high volumes of coarse sediment from the onset of faulting in the western Corinth rift (around 4 Ma). These observations demonstrate that antecedent drainage systems can be important in the tectono-sedimentary evolution of rift basins.

Verification of an IGBT Fusing Switch for Over-current Protection of the SNS HVCM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benwell, Andrew; Kemp, Mark; Burkhart, Craig

2010-06-11

An IGBT based over-current protection system has been developed to detect faults and limit the damage caused by faults in high voltage converter modulators. During normal operation, an IGBT enables energy to be transferred from storage capacitors to a H-bridge. When a fault occurs, the over-current protection system detects the fault, limits the fault current and opens the IGBT to isolate the remaining stored energy from the fault. This paper presents an experimental verification of the over-current protection system under applicable conditions.

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

PubMed

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

PubMed Central

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate. PMID:24744774

Verifying Digital Components of Physical Systems: Experimental Evaluation of Test Quality

NASA Astrophysics Data System (ADS)

Laputenko, A. V.; López, J. E.; Yevtushenko, N. V.

2018-03-01

This paper continues the study of high quality test derivation for verifying digital components which are used in various physical systems; those are sensors, data transfer components, etc. We have used logic circuits b01-b010 of the package of ITC'99 benchmarks (Second Release) for experimental evaluation which as stated before, describe digital components of physical systems designed for various applications. Test sequences are derived for detecting the most known faults of the reference logic circuit using three different approaches to test derivation. Three widely used fault types such as stuck-at-faults, bridges, and faults which slightly modify the behavior of one gate are considered as possible faults of the reference behavior. The most interesting test sequences are short test sequences that can provide appropriate guarantees after testing, and thus, we experimentally study various approaches to the derivation of the so-called complete test suites which detect all fault types. In the first series of experiments, we compare two approaches for deriving complete test suites. In the first approach, a shortest test sequence is derived for testing each fault. In the second approach, a test sequence is pseudo-randomly generated by the use of an appropriate software for logic synthesis and verification (ABC system in our study) and thus, can be longer. However, after deleting sequences detecting the same set of faults, a test suite returned by the second approach is shorter. The latter underlines the fact that in many cases it is useless to spend `time and efforts' for deriving a shortest distinguishing sequence; it is better to use the test minimization afterwards. The performed experiments also show that the use of only randomly generated test sequences is not very efficient since such sequences do not detect all the faults of any type. After reaching the fault coverage around 70%, saturation is observed, and the fault coverage cannot be increased anymore. For deriving high quality short test suites, the approach that is the combination of randomly generated sequences together with sequences which are aimed to detect faults not detected by random tests, allows to reach the good fault coverage using shortest test sequences.

Fault detection in mechanical systems with friction phenomena: an online neural approximation approach.

PubMed

Papadimitropoulos, Adam; Rovithakis, George A; Parisini, Thomas

2007-07-01

In this paper, the problem of fault detection in mechanical systems performing linear motion, under the action of friction phenomena is addressed. The friction effects are modeled through the dynamic LuGre model. The proposed architecture is built upon an online neural network (NN) approximator, which requires only system's position and velocity. The friction internal state is not assumed to be available for measurement. The neural fault detection methodology is analyzed with respect to its robustness and sensitivity properties. Rigorous fault detectability conditions and upper bounds for the detection time are also derived. Extensive simulation results showing the effectiveness of the proposed methodology are provided, including a real case study on an industrial actuator.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nugraha, Andri Dian, E-mail: andridn104@gmail.com; Widiyantoro, Sri; Shiddiqi, Hasbi Ash

Indonesian archipelago region is located in active tectonic setting and high seismicity zone. During the last decade, Indonesian was experienced with destructive major earthquakes causing damage and victims. The information of precise earthquake location parameters are very important in partular for earthquake early warning to the society and for advance seismic studies. In this study, we attempted to improve hypocenter location compiled by BMKG for time periods of April, 2009 up to June, 2014 for about 22,000 earthquake events around Indonesian region. For the firts time, we applied teleseismic double-difference relocation algorithm (teletomoDD) to improve hypocenter region in Indonesia regionmore » combining regional and teleseismic stations. Hypocenter relocation was performed utilizing local, regional, and teleseismic P-wave arrival time data. Our relocation result show that travel-time RMS errors were greatly reduced compared to the BMKG catalog. Seismicity at shallower depth (less than 50 km) shows significantly improvement especially in depth, and refined shallow geological structures, e.g. trench and major strike slip faults. Clustered seismicity is also detected beneath volcanic region, and probably related volcano activities and also major faults nearby. In the Sunda arc region, seismicity at shallower depth centered at two major distributions parallel to the trench strike direction, i.e. around fore-arc and in mainland that related to major fault, e.g. the Sumatran fault, and volcanic fronts. Below Central Java region, relocated hypocenter result showed double seismic zone pattern. A seismic gap is detected around the Sunda-Banda transition zone where transition between oceanic subduction to continental crust collision of Australian plate occurs. In Eastern Indonesia region, shallow earthquakes are observed related to major strike slip faults, e.g. Sorong and Palu-Koro fault, volcanism, and shallow part of subduction and collision zones. We also compare our result in the Sunda Arc region with slab1.0 model and our relocated seismicity shows good agreement with the previous slab geometry. Horizontal position shift of relocated events are mostly perpendicular to the trench directions.« less

Seismicity and Crustal Anisotropy Beneath the Western Segment of the North Anatolian Fault: Results from a Dense Seismic Array

NASA Astrophysics Data System (ADS)

Turkelli, N.; Teoman, U.; Altuncu Poyraz, S.; Cambaz, D.; Mutlu, A. K.; Kahraman, M.; Houseman, G. A.; Rost, S.; Thompson, D. A.; Cornwell, D. G.; Utkucu, M.; Gülen, L.

2013-12-01

The North Anatolian Fault (NAF) is one of the major strike slip fault systems on Earth comparable to San Andreas Fault in some ways. Devastating earthquakes have occurred along this system causing major damage and casualties. In order to comprehensively investigate the shallow and deep crustal structure beneath the western segment of NAF, a temporary dense seismic network for North Anatolia (DANA) consisting of 73 broadband sensors was deployed in early May 2012 surrounding a rectangular grid of by 70 km and a nominal station spacing of 7 km with the aim of further enhancing the detection capability of this dense seismic array. This joint project involves researchers from University of Leeds, UK, Bogazici University Kandilli Observatory and Earthquake Research Institute (KOERI), and University of Sakarya and primarily focuses on upper crustal studies such as earthquake locations (especially micro-seismic activity), receiver functions, moment tensor inversions, shear wave splitting, and ambient noise correlations. To begin with, we obtained the hypocenter locations of local earthquakes that occured within the DANA network. The dense 2-D grid geometry considerably enhanced the earthquake detection capability which allowed us to precisely locate events with local magnitudes (Ml) less than 1.0. Accurate earthquake locations will eventually lead to high resolution images of the upper crustal structure beneath the northern and southern branches of NAF in Sakarya region. In order to put additional constraints on the active tectonics of the western part of NAF, we also determined fault plane solutions using Regional Moment Tensor Inversion (RMT) and P wave first motion methods. For the analysis of high quality fault plane solutions, data from KOERI and the DANA project were merged. Furthermore, with the aim of providing insights on crustal anisotropy, shear wave splitting parameters such as lag time and fast polarization direction were obtained for local events recorded within the seismic network with magnitudes larger than 2.5.

Growth faults and salt tectonics in Houston diapir province: relative timing and exploration significance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ewing, T.E.

1983-09-01

Oil and gas accumulation in Gulf Coast Tertiary strata is contolled mainly by regional growth faults and by salt-related structures. Salt forms the most prominent set of structures in the Houston diapir province of southeast Texas. Recent work in three study areas shows that the Tertiary growth-fault trends, so well displayed along strike to the south-west, continue through this salt basin as well, but they have been deformed by later salt movement. In the Katy area, seismic data disclose early (pre-Wilcox) salt pillows downdip of the Cretaceous reef trend. Salt stocks were injected upward from the pillows during Clayborne deposition,more » and were flanked by deep withdrawal basins and turtle structures. In Brazoria County, a major lower Frio growth-fault trend affecting the Houston delta system, was deformed by later salt domes, by a salt-withdrawal basin, and by a possible turtle structure at Chocolate Bayou. A productive geopressured aquifer exists in the salt-withdrawal basin bounded by the previously formed growth faults. In Jefferson County, in contrast, salt-tectonic activity and growth faulting appear to have been coeval. Early salt-cored ridges continued to rise throughout Frio deposition; growth faults occur both updip and downdip. Hydrocarbons accumulated over the salt domes in growth-fault anticlines and in stratigraphic traps. Recognition that shelf-margin growth faulting preceded the development of the present pattern of domes and basins has important implications for hydrocarbon exploration. Growth faults may be migration paths for hydrocarbons; furthermore, early formed traps, distorted by salt movement, may still be found to contain hydrocarbons.« less

An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

Future space explorations will require long term human presence in space. Space environments that provide working and living quarters for manned missions are becoming increasingly larger and more sophisticated. Monitor and control of the space environment subsystems by expert system software, which emulate human reasoning processes, could maintain the health of the subsystems and help reduce the human workload. The autonomous power expert (APEX) system was developed to emulate a human expert's reasoning processes used to diagnose fault conditions in the domain of space power distribution. APEX is a fault detection, isolation, and recovery (FDIR) system, capable of autonomous monitoring and control of the power distribution system. APEX consists of a knowledge base, a data base, an inference engine, and various support and interface software. APEX provides the user with an easy-to-use interactive interface. When a fault is detected, APEX will inform the user of the detection. The user can direct APEX to isolate the probable cause of the fault. Once a fault has been isolated, the user can ask APEX to justify its fault isolation and to recommend actions to correct the fault. APEX implementation and capabilities are discussed.

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Fault detection and diagnosis of diesel engine valve trains

NASA Astrophysics Data System (ADS)

Flett, Justin; Bone, Gary M.

2016-05-01

This paper presents the development of a fault detection and diagnosis (FDD) system for use with a diesel internal combustion engine (ICE) valve train. A novel feature is generated for each of the valve closing and combustion impacts. Deformed valve spring faults and abnormal valve clearance faults were seeded on a diesel engine instrumented with one accelerometer. Five classification methods were implemented experimentally and compared. The FDD system using the Naïve-Bayes classification method produced the best overall performance, with a lowest detection accuracy (DA) of 99.95% and a lowest classification accuracy (CA) of 99.95% for the spring faults occurring on individual valves. The lowest DA and CA values for multiple faults occurring simultaneously were 99.95% and 92.45%, respectively. The DA and CA results demonstrate the accuracy of our FDD system for diesel ICE valve train fault scenarios not previously addressed in the literature.

Sudden aseismic fault slip on the south flank of Kilauea volcano.

PubMed

Cervelli, Peter; Segall, Paul; Johnson, Kaj; Lisowski, Michael; Miklius, Asta

2002-02-28

One of the greatest hazards associated with oceanic volcanoes is not volcanic in nature, but lies with the potential for catastrophic flank failure. Such flank failure can result in devastating tsunamis and threaten not only the immediate vicinity, but coastal cities along the entire rim of an ocean basin. Kilauea volcano on the island of Hawaii, USA, is a potential source of such flank failures and has therefore been monitored by a network of continuously recording geodetic instruments, including global positioning system (GPS) receivers, tilt meters and strain meters. Here we report that, in early November 2000, this network recorded transient southeastward displacements, which we interpret as an episode of aseismic fault slip. The duration of the event was about 36 hours, it had an equivalent moment magnitude of 5.7 and a maximum slip velocity of about 6[?]cm per day. Inversion of the GPS data reveals a shallow-dipping thrust fault at a depth of 4.5[?]km that we interpret as the down-dip extension of the Hilina Pali--Holei Pali normal fault system. This demonstrates that continuously recording geodetic networks can detect accelerating slip, potentially leading to warnings of volcanic flank collapse.

System and method for bearing fault detection using stator current noise cancellation

DOEpatents

Zhou, Wei; Lu, Bin; Habetler, Thomas G.; Harley, Ronald G.; Theisen, Peter J.

2010-08-17

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to repeatedly receive real-time operating current data from the operating motor and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

Autonomous power expert system advanced development

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

The autonomous power expert (APEX) system is being developed at Lewis Research Center to function as a fault diagnosis advisor for a space power distribution test bed. APEX is a rule-based system capable of detecting faults and isolating the probable causes. APEX also has a justification facility to provide natural language explanations about conclusions reached during fault isolation. To help maintain the health of the power distribution system, additional capabilities were added to APEX. These capabilities allow detection and isolation of incipient faults and enable the expert system to recommend actions/procedure to correct the suspected fault conditions. New capabilities for incipient fault detection consist of storage and analysis of historical data and new user interface displays. After the cause of a fault is determined, appropriate recommended actions are selected by rule-based inferencing which provides corrective/extended test procedures. Color graphics displays and improved mouse-selectable menus were also added to provide a friendlier user interface. A discussion of APEX in general and a more detailed description of the incipient detection, recommended actions, and user interface developments during the last year are presented.

Fault detection and fault tolerance in robotics

NASA Technical Reports Server (NTRS)

Visinsky, Monica; Walker, Ian D.; Cavallaro, Joseph R.

1992-01-01

Robots are used in inaccessible or hazardous environments in order to alleviate some of the time, cost and risk involved in preparing men to endure these conditions. In order to perform their expected tasks, the robots are often quite complex, thus increasing their potential for failures. If men must be sent into these environments to repair each component failure in the robot, the advantages of using the robot are quickly lost. Fault tolerant robots are needed which can effectively cope with failures and continue their tasks until repairs can be realistically scheduled. Before fault tolerant capabilities can be created, methods of detecting and pinpointing failures must be perfected. This paper develops a basic fault tree analysis of a robot in order to obtain a better understanding of where failures can occur and how they contribute to other failures in the robot. The resulting failure flow chart can also be used to analyze the resiliency of the robot in the presence of specific faults. By simulating robot failures and fault detection schemes, the problems involved in detecting failures for robots are explored in more depth.

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2010-01-01

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed

Rifting mechanisms constrained by InSAR, seismicity, GPS, and surface rupture from the Karonga earthquake sequence in northern Lake Malawi (Nyasa)

NASA Astrophysics Data System (ADS)

Zheng, W.; Pritchard, M. E.; Henderson, S. T.; Gaherty, J. B.; Shillington, D. J.; Oliva, S. J.; Ebinger, C.; Nooner, S. L.; Elliott, J.; Saria, E.; Ntambila, D.; Chindandali, P. R. N.

2017-12-01

The Malawi rift is part of the archetypal East African rift where early-stage crustal extension is dominated by faulting. In the Karonga region of northern Malawi, a sequence of earthquakes in late 2009, with 15 teleseismically detected (Mw 4.5-6.0) over 13 days, provides a uniqueopportunity to evaluate faulting processes controlling present-day extension in an early-stage rift. We describe observations of this sequence including hundreds of aftershocks located by a temporary seismic array installed in 2010, ground deformation from satellite interferograms, and surface rupture from field surveys published by others. We use all of these data to model fault geometry and slip. The aftershocks from January-May 2010 suggest the involvement of multiple faults, and we test the extent that this can be resolved by the InSAR data. The InSAR and surface rupture both suggest that the major slip occurred at shallow depth (<5 km). Our preferred aftershock locations appear to correlate with this principal slip zone, although uncertainty in the shallow velocity structure can allow for a bulk of the events to fall down-dip of the geodetically constrained slip. Subsequent deformation, including that associated with a December 2014 Mw 5.1 earthquake, can be constrained from multidisciplinary data collected during the SEGMeNT (Study of Extension and maGmatism in Malawi aNd Tanzania) project, which includes the Karonga region and spans 2013-2015. We find 3 cm of potential ground movement at the location of the earthquake as determined by the SEGMeNT seismic array from Sentinel-1. Geodetic fault slip is consistent with the focal mechanism and depth determined by the local array. The location is at the northern end of the 2009-2010 aftershock zone, and aftershocks suggest some linkage with faults that slipped in 2009. InSAR observations do not provide any evidence for large aseismic slip or fluid movements during or after the 2014 sequence, which had <200 aftershocks above the network threshold. For example, we do not observe any deformation at Rungwe volcano above the 2 cm/yr detection threshold with InSAR time series from ALOS (2007-2010) or Sentinel-1 (10/2014 - 04/2017). The time series from SEGMeNT and other continuous GPS stations do not show transients related to the earthquakes, but are not optimally located in space or time.

Arc Fault Detection & Localization by Electromagnetic-Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Vasile, C.; Ioana, C.

2017-05-01

Electrical arc faults that occur in photovoltaic systems represent a danger due to their economic impact on production and distribution. In this paper we propose a complete system, with focus on the methodology, that enables the detection and localization of the arc fault, by the use of an electromagnetic-acoustic sensing system. By exploiting the multiple emissions of the arc fault, in conjunction with a real-time detection signal processing method, we ensure accurate detection and localization. In its final form, this present work will present in greater detail the complete system, the methods employed, results and performance, alongside further works that will be carried on.

Fast and accurate spectral estimation for online detection of partial broken bar in induction motors

NASA Astrophysics Data System (ADS)

Samanta, Anik Kumar; Naha, Arunava; Routray, Aurobinda; Deb, Alok Kanti

2018-01-01

In this paper, an online and real-time system is presented for detecting partial broken rotor bar (BRB) of inverter-fed squirrel cage induction motors under light load condition. This system with minor modifications can detect any fault that affects the stator current. A fast and accurate spectral estimator based on the theory of Rayleigh quotient is proposed for detecting the spectral signature of BRB. The proposed spectral estimator can precisely determine the relative amplitude of fault sidebands and has low complexity compared to available high-resolution subspace-based spectral estimators. Detection of low-amplitude fault components has been improved by removing the high-amplitude fundamental frequency using an extended-Kalman based signal conditioner. Slip is estimated from the stator current spectrum for accurate localization of the fault component. Complexity and cost of sensors are minimal as only a single-phase stator current is required. The hardware implementation has been carried out on an Intel i7 based embedded target ported through the Simulink Real-Time. Evaluation of threshold and detectability of faults with different conditions of load and fault severity are carried out with empirical cumulative distribution function.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

NASA Astrophysics Data System (ADS)

Raj, Nithin; Jagadanand, G.; George, Saly

2018-04-01

The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

A novel end-to-end fault detection and localization protocol for wavelength-routed WDM networks

NASA Astrophysics Data System (ADS)

Zeng, Hongqing; Vukovic, Alex; Huang, Changcheng

2005-09-01

Recently the wavelength division multiplexing (WDM) networks are becoming prevalent for telecommunication networks. However, even a very short disruption of service caused by network faults may lead to high data loss in such networks due to the high date rates, increased wavelength numbers and density. Therefore, the network survivability is critical and has been intensively studied, where fault detection and localization is the vital part but has received disproportional attentions. In this paper we describe and analyze an end-to-end lightpath fault detection scheme in data plane with the fault notification in control plane. The endeavor is focused on reducing the fault detection time. In this protocol, the source node of each lightpath keeps sending hello packets to the destination node exactly following the path for data traffic. The destination node generates an alarm once a certain number of consecutive hello packets are missed within a given time period. Then the network management unit collects all alarms and locates the faulty source based on the network topology, as well as sends fault notification messages via control plane to either the source node or all upstream nodes along the lightpath. The performance evaluation shows such a protocol can achieve fast fault detection, and at the same time, the overhead brought to the user data by hello packets is negligible.

Methods and apparatus using commutative error detection values for fault isolation in multiple node computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almasi, Gheorghe; Blumrich, Matthias Augustin; Chen, Dong

Methods and apparatus perform fault isolation in multiple node computing systems using commutative error detection values for--example, checksums--to identify and to isolate faulty nodes. When information associated with a reproducible portion of a computer program is injected into a network by a node, a commutative error detection value is calculated. At intervals, node fault detection apparatus associated with the multiple node computer system retrieve commutative error detection values associated with the node and stores them in memory. When the computer program is executed again by the multiple node computer system, new commutative error detection values are created and stored inmore » memory. The node fault detection apparatus identifies faulty nodes by comparing commutative error detection values associated with reproducible portions of the application program generated by a particular node from different runs of the application program. Differences in values indicate a possible faulty node.« less

Tunable architecture for aircraft fault detection

NASA Technical Reports Server (NTRS)

Ganguli, Subhabrata (Inventor); Papageorgiou, George (Inventor); Glavaski-Radovanovic, Sonja (Inventor)

2012-01-01

A method for detecting faults in an aircraft is disclosed. The method involves predicting at least one state of the aircraft and tuning at least one threshold value to tightly upper bound the size of a mismatch between the at least one predicted state and a corresponding actual state of the non-faulted aircraft. If the mismatch between the at least one predicted state and the corresponding actual state is greater than or equal to the at least one threshold value, the method indicates that at least one fault has been detected.

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Zakajsek, James J.

1994-01-01

This paper presents a summary of the transmission diagnostics research work conducted at NASA Lewis Research Center over the last four years. In 1990, the Transmission Health and Usage Monitoring Research Team at NASA Lewis conducted a survey to determine the critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods, improved fault detection in planetary systems, and damage magnitude assessment and prognostics research were all critical to a highly reliable health and usage monitoring system. In response to this, a variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue data include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Overall results indicate that, of the gear fault detection techniques, no one method can successfully detect all possible failure modes. The more successful methods need to be integrated into a single more reliable detection technique. A recently developed method, NA4, in addition to being one of the more successful gear fault detection methods, was also found to exhibit damage magnitude estimation capabilities.

Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

PubMed Central

Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

2017-01-01

Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

S&T converging trends in dealing with disaster: A review on AI tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, Abu Bakar, E-mail: abakarh@usim.edu.my; Isa, Mohd Hafez Mohd.

Science and Technology (S&T) has been able to help mankind to solve or minimize problems when arise. Different methodologies, techniques and tools were developed or used for specific cases by researchers, engineers, scientists throughout the world, and numerous papers and articles have been written by them. Nine selected cases such as flash flood, earthquakes, workplace accident, fault in aircraft industry, seismic vulnerability, disaster mitigation and management, and early fault detection in nuclear industry have been studied. This paper looked at those cases, and their results showed nearly 60% uses artificial intelligence (AI) as a tool. This paper also did somemore » review that will help young researchers in deciding the types of AI tools to be selected; thus proving the future trends in S&T.« less

S&T converging trends in dealing with disaster: A review on AI tools

NASA Astrophysics Data System (ADS)

Hasan, Abu Bakar; Isa, Mohd. Hafez Mohd.

2016-01-01

Science and Technology (S&T) has been able to help mankind to solve or minimize problems when arise. Different methodologies, techniques and tools were developed or used for specific cases by researchers, engineers, scientists throughout the world, and numerous papers and articles have been written by them. Nine selected cases such as flash flood, earthquakes, workplace accident, fault in aircraft industry, seismic vulnerability, disaster mitigation and management, and early fault detection in nuclear industry have been studied. This paper looked at those cases, and their results showed nearly 60% uses artificial intelligence (AI) as a tool. This paper also did some review that will help young researchers in deciding the types of AI tools to be selected; thus proving the future trends in S&T.

Sensor Selection and Data Validation for Reliable Integrated System Health Management

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Melcher, Kevin J.

2008-01-01

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Algorithm-Based Fault Tolerance for Numerical Subroutines

NASA Technical Reports Server (NTRS)

Tumon, Michael; Granat, Robert; Lou, John

2007-01-01

A software library implements a new methodology of detecting faults in numerical subroutines, thus enabling application programs that contain the subroutines to recover transparently from single-event upsets. The software library in question is fault-detecting middleware that is wrapped around the numericalsubroutines. Conventional serial versions (based on LAPACK and FFTW) and a parallel version (based on ScaLAPACK) exist. The source code of the application program that contains the numerical subroutines is not modified, and the middleware is transparent to the user. The methodology used is a type of algorithm- based fault tolerance (ABFT). In ABFT, a checksum is computed before a computation and compared with the checksum of the computational result; an error is declared if the difference between the checksums exceeds some threshold. Novel normalization methods are used in the checksum comparison to ensure correct fault detections independent of algorithm inputs. In tests of this software reported in the peer-reviewed literature, this library was shown to enable detection of 99.9 percent of significant faults while generating no false alarms.

A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus,more » the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.« less

A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

NASA Astrophysics Data System (ADS)

Zhang, Yumin; Wang, Qing-Guo; Lum, Kai-Yew

2009-03-01

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

PubMed

Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

2016-12-08

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines

PubMed Central

Swetapadma, Aleena; Yadav, Anamika

2015-01-01

Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance. PMID:26413088

A Three-Dimensional Receiver Operator Characteristic Surface Diagnostic Metric

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2011-01-01

Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system s True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent of the applied detection threshold. While the standard ROC curve is well suited for quantifying binary fault detection performance, it is not suitable for quantifying the classification performance of multi-fault classification problems. Furthermore, it does not provide a measure of diagnostic latency. To address these shortcomings, a novel three-dimensional receiver operator characteristic (3D ROC) surface metric has been developed. This is done by generating and applying two separate curves: the standard ROC curve reflecting fault detection performance, and a second curve reflecting fault classification performance. A third dimension, diagnostic latency, is added giving rise to 3D ROC surfaces. Applying numerical integration techniques, the volumes under and between the surfaces are calculated to produce metrics of the diagnostic system s detection and classification performance. This paper will describe the 3D ROC surface metric in detail, and present an example of its application for quantifying the performance of aircraft engine gas path diagnostic methods. Metric limitations and potential enhancements are also discussed

Generic, scalable and decentralized fault detection for robot swarms.

PubMed

Tarapore, Danesh; Christensen, Anders Lyhne; Timmis, Jon

2017-01-01

Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system's capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.

Generic, scalable and decentralized fault detection for robot swarms

PubMed Central

Christensen, Anders Lyhne; Timmis, Jon

2017-01-01

Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation. PMID:28806756

Early Proterozoic activity on Archean faults in the western Superior province - evidence from pseudotachylite

USGS Publications Warehouse

Peterman, Z.E.; Day, W.

1989-01-01

Major transcurrent faults in the Superior province developed in the Late Archean at the close of the Kenoran orogeny. Reactivation of some of these faults late in the Early Proterozoic is indicated by Rb-Sr analyses of pseudotachylite from the Rainy Lake-Seine River and Quetico faults in the Rainy Lake region of Minnesota and Ontario. Fault veins of pseudotachylite and immediately adjacent country rock at two localities yielded subparallel isochrons that are pooled for an age of 1947??23 Ma. K-Ar and Rb-Sr biotite ages register earlier regional cooling of the terrane at about 2500 Ma with no evidence of younger thermal overprinting at temperatures exceeding 300??C. Accordingly, the 1947??23 Ma age is interpreted as dating the formation of the pseudotachylite. Reactivation of existing faults at this time was caused by stresses transmitted from margins of the Superior province where compressional tectonic events were occurring. -Authors

Comparative Study of Fault Diagnostic Methods in Voltage Source Inverter Fed Three Phase Induction Motor Drive

NASA Astrophysics Data System (ADS)

Dhumale, R. B.; Lokhande, S. D.

2017-05-01

Three phase Pulse Width Modulation inverter plays vital role in industrial applications. The performance of inverter demeans as several types of faults take place in it. The widely used switching devices in power electronics are Insulated Gate Bipolar Transistors (IGBTs) and Metal Oxide Field Effect Transistors (MOSFET). The IGBTs faults are broadly classified as base or collector open circuit fault, misfiring fault and short circuit fault. To develop consistency and performance of inverter, knowledge of fault mode is extremely important. This paper presents the comparative study of IGBTs fault diagnosis. Experimental set up is implemented for data acquisition under various faulty and healthy conditions. Recent methods are executed using MATLAB-Simulink and compared using key parameters like average accuracy, fault detection time, implementation efforts, threshold dependency, and detection parameter, resistivity against noise and load dependency.

ASCS online fault detection and isolation based on an improved MPCA

NASA Astrophysics Data System (ADS)

Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

2014-09-01

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

Application of Collocated GPS and Seismic Sensors to Earthquake Monitoring and Early Warning

PubMed Central

Li, Xingxing; Zhang, Xiaohong; Guo, Bofeng

2013-01-01

We explore the use of collocated GPS and seismic sensors for earthquake monitoring and early warning. The GPS and seismic data collected during the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes are analyzed by using a tightly-coupled integration. The performance of the integrated results is validated by both time and frequency domain analysis. We detect the P-wave arrival and observe small-scale features of the movement from the integrated results and locate the epicenter. Meanwhile, permanent offsets are extracted from the integrated displacements highly accurately and used for reliable fault slip inversion and magnitude estimation. PMID:24284765

Soft-Fault Detection Technologies Developed for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Button, Robert M.

2004-01-01

The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

Temperature and composition of carbonate cements record early structural control on cementation in a nascent deformation band fault zone: Moab Fault, Utah, USA

NASA Astrophysics Data System (ADS)

Hodson, Keith R.; Crider, Juliet G.; Huntington, Katharine W.

2016-10-01

Fluid-driven cementation and diagenesis within fault zones can influence host rock permeability and rheology, affecting subsequent fluid migration and rock strength. However, there are few constraints on the feedbacks between diagenetic conditions and structural deformation. We investigate the cementation history of a fault-intersection zone on the Moab Fault, a well-studied fault system within the exhumed reservoir rocks of the Paradox Basin, Utah, USA. The fault zone hosts brittle structures recording different stages of deformation, including joints and two types of deformation bands. Using stable isotopes of carbon and oxygen, clumped isotope thermometry, and cathodoluminescence, we identify distinct source fluid compositions for the carbonate cements within the fault damage zone. Each source fluid is associated with different carbonate precipitation temperatures, luminescence characteristics, and styles of structural deformation. Luminescent carbonates appear to be derived from meteoric waters mixing with an organic-rich or magmatic carbon source. These cements have warm precipitation temperatures and are closely associated with jointing, capitalizing on increases in permeability associated with fracturing during faulting and subsequent exhumation. Earlier-formed non-luminescent carbonates have source fluid compositions similar to marine waters, low precipitation temperatures, and are closely associated with deformation bands. The deformation bands formed at shallow depths very early in the burial history, preconditioning the rock for fracturing and associated increases in permeability. Carbonate clumped isotope temperatures allow us to associate structural and diagenetic features with burial history, revealing that structural controls on fluid distribution are established early in the evolution of the host rock and fault zone, before the onset of major displacement.

Fault detection in rotor bearing systems using time frequency techniques

NASA Astrophysics Data System (ADS)

Chandra, N. Harish; Sekhar, A. S.

2016-05-01

Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.

Usage of Fault Detection Isolation & Recovery (FDIR) in Constellation (CxP) Launch Operations

NASA Technical Reports Server (NTRS)

Ferrell, Rob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Spirkovska, Lilly; Hall, David; Brown, Barbara

2010-01-01

This paper will explore the usage of Fault Detection Isolation & Recovery (FDIR) in the Constellation Exploration Program (CxP), in particular Launch Operations at Kennedy Space Center (KSC). NASA's Exploration Technology Development Program (ETDP) is currently funding a project that is developing a prototype FDIR to demonstrate the feasibility of incorporating FDIR into the CxP Ground Operations Launch Control System (LCS). An architecture that supports multiple FDIR tools has been formulated that will support integration into the CxP Ground Operation's Launch Control System (LCS). In addition, tools have been selected that provide fault detection, fault isolation, and anomaly detection along with integration between Flight and Ground elements.

Development and evaluation of a Fault-Tolerant Multiprocessor (FTMP) computer. Volume 3: FTMP test and evaluation

NASA Technical Reports Server (NTRS)

Lala, J. H.; Smith, T. B., III

1983-01-01

The experimental test and evaluation of the Fault-Tolerant Multiprocessor (FTMP) is described. Major objectives of this exercise include expanding validation envelope, building confidence in the system, revealing any weaknesses in the architectural concepts and in their execution in hardware and software, and in general, stressing the hardware and software. To this end, pin-level faults were injected into one LRU of the FTMP and the FTMP response was measured in terms of fault detection, isolation, and recovery times. A total of 21,055 stuck-at-0, stuck-at-1 and invert-signal faults were injected in the CPU, memory, bus interface circuits, Bus Guardian Units, and voters and error latches. Of these, 17,418 were detected. At least 80 percent of undetected faults are estimated to be on unused pins. The multiprocessor identified all detected faults correctly and recovered successfully in each case. Total recovery time for all faults averaged a little over one second. This can be reduced to half a second by including appropriate self-tests.

All-to-all sequenced fault detection system

DOEpatents

Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

2010-11-02

An apparatus, program product and method enable nodal fault detection by sequencing communications between all system nodes. A master node may coordinate communications between two slave nodes before sequencing to and initiating communications between a new pair of slave nodes. The communications may be analyzed to determine the nodal fault.

Discrete Wavelet Transform for Fault Locations in Underground Distribution System

NASA Astrophysics Data System (ADS)

Apisit, C.; Ngaopitakkul, A.

2010-10-01

In this paper, a technique for detecting faults in underground distribution system is presented. Discrete Wavelet Transform (DWT) based on traveling wave is employed in order to detect the high frequency components and to identify fault locations in the underground distribution system. The first peak time obtained from the faulty bus is employed for calculating the distance of fault from sending end. The validity of the proposed technique is tested with various fault inception angles, fault locations and faulty phases. The result is found that the proposed technique provides satisfactory result and will be very useful in the development of power systems protection scheme.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Various Indices for Diagnosis of Air-gap Eccentricity Fault in Induction Motor-A Review

NASA Astrophysics Data System (ADS)

Nikhil; Mathew, Lini, Dr.; Sharma, Amandeep

2018-03-01

From the past few years, research has gained an ardent pace in the field of fault detection and diagnosis in induction motors. In the current scenario, software is being introduced with diagnostic features to improve stability and reliability in fault diagnostic techniques. Human involvement in decision making for fault detection is slowly being replaced by Artificial Intelligence techniques. In this paper, a brief introduction of eccentricity fault is presented along with their causes and effects on the health of induction motors. Various indices used to detect eccentricity are being introduced along with their boundary conditions and their future scope of research. At last, merits and demerits of all indices are discussed and a comparison is made between them.

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

NASA Astrophysics Data System (ADS)

Habbi, Hacene; Kidouche, Madjid; Kinnaert, Michel; Zelmat, Mimoun

2011-04-01

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

Soft Computing Application in Fault Detection of Induction Motor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

Weak fault detection and health degradation monitoring using customized standard multiwavelets

NASA Astrophysics Data System (ADS)

Yuan, Jing; Wang, Yu; Peng, Yizhen; Wei, Chenjun

2017-09-01

Due to the nonobvious symptoms contaminated by a large amount of background noise, it is challenging to beforehand detect and predictively monitor the weak faults for machinery security assurance. Multiwavelets can act as adaptive non-stationary signal processing tools, potentially viable for weak fault diagnosis. However, the signal-based multiwavelets suffer from such problems as the imperfect properties missing the crucial orthogonality, the decomposition distortion impossibly reflecting the relationships between the faults and signatures, the single objective optimization and independence for fault prognostic. Thus, customized standard multiwavelets are proposed for weak fault detection and health degradation monitoring, especially the weak fault signature quantitative identification. First, the flexible standard multiwavelets are designed using the construction method derived from scalar wavelets, seizing the desired properties for accurate detection of weak faults and avoiding the distortion issue for feature quantitative identification. Second, the multi-objective optimization combined three dimensionless indicators of the normalized energy entropy, normalized singular entropy and kurtosis index is introduced to the evaluation criterions, and benefits for selecting the potential best basis functions for weak faults without the influence of the variable working condition. Third, an ensemble health indicator fused by the kurtosis index, impulse index and clearance index of the original signal along with the normalized energy entropy and normalized singular entropy by the customized standard multiwavelets is achieved using Mahalanobis distance to continuously monitor the health condition and track the performance degradation. Finally, three experimental case studies are implemented to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed method can quantitatively identify the fault signature of a slight rub on the inner race of a locomotive bearing, effectively detect and locate the potential failure from a complicated epicyclic gear train and successfully reveal the fault development and performance degradation of a test bearing in the lifetime.

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2004-01-01

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

Fault detection of gearbox using time-frequency method

NASA Astrophysics Data System (ADS)

Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

2017-04-01

This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

Controls on Early-Rift Geometry: New Perspectives From the Bilila-Mtakataka Fault, Malawi

NASA Astrophysics Data System (ADS)

Hodge, M.; Fagereng, Å.; Biggs, J.; Mdala, H.

2018-05-01

We use the ˜110-km long Bilila-Mtakataka fault in the amagmatic southern East African Rift, Malawi, to investigate the controls on early-rift geometry at the scale of a major border fault. Morphological variations along the 14 ± 8-m high scarp define six 10- to 40-km long segments, which are either foliation parallel or oblique to both foliation and the current regional extension direction. As the scarp is neither consistently parallel to foliation nor well oriented for the current regional extension direction, we suggest that the segmented surface expression is related to the local reactivation of well-oriented weak shallow fabrics above a broadly continuous structure at depth. Using a geometrical model, the geometry of the best fitting subsurface structure is consistent with the local strain field from recent seismicity. In conclusion, within this early-rift, preexisting weaknesses only locally control border fault geometry at subsurface.

Using the Bongwana natural CO2 release to understand leakage processes and develop monitoring

NASA Astrophysics Data System (ADS)

Jones, David; Johnson, Gareth; Hicks, Nigel; Bond, Clare; Gilfillan, Stuart; Kremer, Yannick; Lister, Bob; Nkwane, Mzikayise; Maupa, Thulani; Munyangane, Portia; Robey, Kate; Saunders, Ian; Shipton, Zoe; Pearce, Jonathan; Haszeldine, Stuart

2016-04-01

Natural CO2 leakage along the Bongwana Fault in South Africa is being studied to help understand processes of CO2 leakage and develop monitoring protocols. The Bongwana Fault crops out over approximately 80 km in KwaZulu-Natal province, South Africa. In outcrop the fault is expressed as a broad fracture corridor in Dwyka Tillite, with fractures oriented approximately N-S. Natural emissions of CO2 occur at various points along the fault, manifest as travertine cones and terraces, bubbling in the rivers and as gas fluxes through soil. Exposed rock outcrop shows evidence for Fe-staining around fractures and is locally extensively kaolinitised. The gas has also been released through a shallow water well, and was exploited commercially in the past. Preliminary studies have been carried out to better document the surface emissions using near surface gas monitoring, understand the origin of the gas through major gas composition and stable and noble gas isotopes and improve understanding of the structural controls on gas leakage through mapping. In addition the impact of the leaking CO2 on local water sources (surface and ground) is being investigated, along with the seismic activity of the fault. The investigation will help to build technical capacity in South Africa and to develop monitoring techniques and plans for a future CO2 storage pilot there. Early results suggest that CO2 leakage is confined to a relatively small number of spatially-restricted locations along the weakly seismically active fault. Fracture permeability appears to be the main method by which the CO2 migrates to the surface. The bulk of the CO2 is of deep origin with a minor contribution from near surface biogenic processes as determined by major gas composition. Water chemistry, including pH, DO and TDS is notably different between CO2-rich and CO2-poor sites. Soil gas content and flux effectively delineates the fault trace in active leakage sites. The fault provides an effective testing ground for field-based monitoring with results to date indicating the methods and technologies tested successfully detect leaking CO2. Further work will investigate the source of the CO2 and attempt to quantify CO2 flux rates and detection thresholds.

Power plant fault detection using artificial neural network

NASA Astrophysics Data System (ADS)

Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

2018-02-01

The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm.

PubMed

Wang, Tianyang; Chu, Fulei; Han, Qinkai

2017-03-01

Identifying the differences between the spectra or envelope spectra of a faulty signal and a healthy baseline signal is an efficient planetary gearbox local fault detection strategy. However, causes other than local faults can also generate the characteristic frequency of a ring gear fault; this may further affect the detection of a local fault. To address this issue, a new filtering algorithm based on the meshing resonance phenomenon is proposed. In detail, the raw signal is first decomposed into different frequency bands and levels. Then, a new meshing index and an MRgram are constructed to determine which bands belong to the meshing resonance frequency band. Furthermore, an optimal filter band is selected from this MRgram. Finally, the ring gear fault can be detected according to the envelope spectrum of the band-pass filtering result. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

System and method for motor fault detection using stator current noise cancellation

DOEpatents

Zhou, Wei; Lu, Bin; Nowak, Michael P.; Dimino, Steven A.

2010-12-07

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

NASA Astrophysics Data System (ADS)

Chen, Jian; Randall, Robert Bond; Peeters, Bart

2016-06-01

Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

2001-01-01

This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

Palinspastic reconstruction of southeastern California and southwestern Arizona for the middle Miocene

NASA Technical Reports Server (NTRS)

Richard, Stephen M.

1992-01-01

A paleogeographic reconstruction of southeastern California and southwestern Arizona at 10 Ma was made based on available geologic and geophysical data. Clockwise rotation of 39 deg was reconstructed in the eastern Transverse Ranges, consistent with paleomagnetic data from late Miocene volcanic rocks, and with slip estimates for left-lateral faults within the eastern Transverse Ranges and NW-trending right lateral faults in the Mojave Desert. This domain of rotated rocks is bounded by the Pinto Mountain fault on the north. In the absence of evidence for rotation of the San Bernardino Mountains or for significant right slip faults within the San Bernardino Mountains, the model requires that the late Miocene Pinto Mountain fault become a thrust fault gaining displacement to the west. The Squaw Peak thrust system of Meisling and Weldon may be a western continuation of this fault system. The Sheep Hole fault bounds the rotating domain on the east. East of this fault an array of NW-trending right slip faults and south-trending extensional transfer zones has produced a basin and range physiography while accumulating up to 14 km of right slip. This maximum is significantly less than the 37.5 km of right slip required in this region by a recent reconstruction of the central Mojave Desert. Geologic relations along the southern boundary of the rotating domain are poorly known, but this boundary is interpreted to involve a series of curved strike slip faults and non-coaxial extension, bounded on the southeast by the Mammoth Wash and related faults in the eastern Chocolate Mountains. Available constraints on timing suggest that Quaternary movement on the Pinto Mountain and nearby faults is unrelated to the rotation of the eastern Transverse Ranges, and was preceded by a hiatus during part of Pliocene time which followed the deformation producing the rotation. The reconstructed Clemens Well fault in the Orocopia Mountains, proposed as a major early Miocene strand of the San Andreas fault, projects eastward towards Arizona, where early Miocene rocks and structures are continuous across its trace. The model predicts a 14 deg clockwise rotation and 55 km extension along the present trace of the San Andreas fault during late Miocene and early Pliocene time. Palinspastic reconstructions of the San Andreas system based on this proposed reconstruction may be significantly modified from current models.

Artificial Neural Network Based Fault Diagnostics of Rotating Machinery Using Wavelet Transforms as a Preprocessor

NASA Astrophysics Data System (ADS)

Paya, B. A.; Esat, I. I.; Badi, M. N. M.

1997-09-01

The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machinery, in order to provide a significant improvement in plant economy, reduce operational and maintenance costs and improve the level of safety. The condition of a model drive-line, consisting of various interconnected rotating parts, including an actual vehicle gearbox, two bearing housings, and an electric motor, all connected via flexible couplings and loaded by a disc brake, was investigated. This model drive-line was run in its normal condition, and then single and multiple faults were introduced intentionally to the gearbox, and to the one of the bearing housings. These single and multiple faults studied on the drive-line were typical bearing and gear faults which may develop during normal and continuous operation of this kind of rotating machinery. This paper presents the investigation carried out in order to study both bearing and gear faults introduced first separately as a single fault and then together as multiple faults to the drive-line. The real time domain vibration signals obtained for the drive-line were preprocessed by wavelet transforms for the neural network to perform fault detection and identify the exact kinds of fault occurring in the model drive-line. It is shown that by using multilayer artificial neural networks on the sets of preprocessed data by wavelet transforms, single and multiple faults were successfully detected and classified into distinct groups.

Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

NASA Technical Reports Server (NTRS)

Balaban, Edward; Saxena, Abhinav; Bansal, Prasun; Goebel, Kai F.; Curran, Simon

2009-01-01

Sensor faults continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult to seed particular sensor faults. Therefore, research is underway to better understand the different fault modes seen in sensors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper illustrates the work with data collected from an electro-mechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy to severity of fault conditions.

Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-07-01

This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

Fault Growth and Propagation and its Effect on Surficial Processes within the Incipient Okavango Rift Zone, Northwest Botswana, Africa (Invited)

NASA Astrophysics Data System (ADS)

Atekwana, E. A.

2010-12-01

The Okavango Rift Zone (ORZ) is suggested to be a zone of incipient continental rifting occuring at the distal end of the southwestern branch of the East African Rift System (EARS), therefore providing a unique opportunity to investigate neotectonic processes during the early stages of rifting. We used geophysical (aeromagnetic, magnetotelluric), Shuttle Radar Tomography Mission, Digital Elevation Model (SRTM-DEM), and sedimentological data to characterize the growth and propagation of faults associated with continental extension in the ORZ, and to elucidate the interplay between neotectonics and surficial processes. The results suggest that: (1) fault growth occurs by along axis linkage of fault segments, (2) an immature border fault is developing through the process of “Fault Piracy” by fault-linkages between major fault systems, (3) significant discrepancies exits between the height of fault scarps and the throws across the faults compared to their lengths in the basement, (4) utilization of preexisting zones of weakness allowed the development of very long faults (> 25-100 km) at a very early stage of continental rifting, explaining the apparent paradox between the fault length versus throw for this young rift, (5) active faults are characterized by conductive anomalies resulting from fluids, whereas, inactive faults show no conductivity anomaly; and 6) sedimentlogical data reveal a major perturbation in lake sedimentation between 41 ka and 27 ka. The sedimentation perturbation is attributed to faulting associated with the rifting and may have resulted in the alteration of hydrology forming the modern day Okavango delta. We infer that this time period may represent the age of the latest rift reactivation and fault growth and propagation within the ORZ.

Peripheral structures of the Rio Grande Rift in the Sangre de Cristo Mountains, around the Colorado-New Mexico border

NASA Astrophysics Data System (ADS)

Fridrich, C. J.; Workman, J. B.

2009-12-01

Recently active faults of the Rio Grande rift near the Colorado-New Mexico border are almost entirely limited to the San Luis basin. In contrast, the early (≈26 to ≈10 Ma) structure of the rift in this area is significantly broader. A wide zone of abandoned, peripheral extensional structures is exposed on the eastern flank of the San Luis basin—in the west half of the Sangre de Cristo Mountains, known in this area as the southern Culebra and northern Taos Ranges. New detailed mapping shows that the eastern limit of the zone of early peripheral extension is marked by an aligned series of north-trending grabens, including the Devil’s Park, Valle Vidal, and Moreno Valley basins. Master faults of these intermontaine basins are partly localized along, and evidently reactivated moderate- to high-angle Laramide (≈70 to ≈40 Ma) reverse faults of the Sangre de Cristo Mountains. Between these grabens and the San Luis basin lies a structural zone that varies in style from block faulting, in the north, to more closely spaced tilted-domino-style faulting in the Latir volcanic field, to the south. Additional early rift structures include several long northwest-striking faults, the largest of which are interpreted to have accommodated significant right-lateral strike-slip, based on abrupt southwestward increase in the magnitude of extension across them. These faults evidently transferred strain from the axial part of the rift into the zone of early peripheral extension, and accommodated lateral changes in structural style. Throughout the area of early peripheral extension, there is a correlation between the magnitude of local volcanism and the degree of extension; however, it is unclear if extension drove volcanism—via mantle upwelling, or if extension was maximized where the crust was weakest, owing to the presence of magma and hot rock at shallow depths.

Tectonics of the Andaman Sea Region

NASA Astrophysics Data System (ADS)

Curray, J. R.

2005-12-01

The Andaman Sea is an active backarc basin lying above and behind the Sunda subduction zone where convergence between the overriding Eurasian, Sunda or Southeast Asian plate and the subducting Indian and Australian plates is highly oblique. The effect of the oblique convergence has been formation of a sliver plate between the subduction zone and a complex right lateral fault system. The late Paleocene collision of Greater India and Asia with approximately normal convergence started clockwise rotation and bending of the northern and western Sunda Arc. The initial sliver fault, which probably started in the Eocene, extended through the outer arc ridge offshore from Sumatra, through the present region of the Andaman Sea into the Sagaing fault in Myanmar. With more oblique convergence due to the rotation, the rate of strike slip motion increased and a series of extensional basins opened obliquely by the combination of backarc extension and the strike slip motion. These basins in sequence are the Mergui Basin starting in early Oligocene, the conjoined Alcock and Sewell Rises starting in early Miocene, East Basin separating the rises from the foot of the continental slope starting at the end of early Miocene; and finally in early Pliocene at ~ 4 Ma, the present sliver plate edge was formed, Alcock and Sewell Rises were separated by formation of the Central Andaman Basin, and the faulting moved onshore from the Mentawai Fault to the Sumatra Fault System bisecting Sumatra. The opening of each basin can be expressed in vectors with north and west components. The total of the north component vectors may be the total offset of the Sagaing Fault since early Oligocene, and the total of the west component vectors may explain the outward bulge in the alignment of the northwestern Sunda Arc. The present average convergence rate of the Andaman-Nicobar Ridge and India is about 28 to 38 mm/yr.

Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines

NASA Astrophysics Data System (ADS)

Singh, Dheeraj Sharan; Zhao, Qing

2016-12-01

This paper presents a novel data driven technique for the detection and isolation of faults, which generate impacts in a rotating equipment. The technique is built upon the principles of empirical mode decomposition (EMD), envelope analysis and pseudo-fault signal for fault separation. Firstly, the most dominant intrinsic mode function (IMF) is identified using EMD of a raw signal, which contains all the necessary information about the faults. The envelope of this IMF is often modulated with multiple vibration sources and noise. A second level decomposition is performed by applying pseudo-fault signal (PFS) assisted EMD on the envelope. A pseudo-fault signal is constructed based on the known fault characteristic frequency of the particular machine. The objective of using external (pseudo-fault) signal is to isolate different fault frequencies, present in the envelope . The pseudo-fault signal serves dual purposes: (i) it solves the mode mixing problem inherent in EMD, (ii) it isolates and quantifies a particular fault frequency component. The proposed technique is suitable for real-time implementation, which has also been validated on simulated fault and experimental data corresponding to a bearing and a gear-box set-up, respectively.

Magnetotelluric Studies of Fault Zones Surrounding the 2016 Pawnee, Oklahoma Earthquake

NASA Astrophysics Data System (ADS)

Evans, R. L.; Key, K.; Atekwana, E. A.

2016-12-01

Since 2008, there has been a dramatic increase in earthquake activity in the central United States in association with major oil and gas operations. Oklahoma is now considered one the most seismically active states. Although seismic networks are able to detect activity and map its locus, they are unable to image the distribution of fluids in the fault responsible for triggering seismicity. Electrical geophysical methods are ideally suited to image fluid bearing faults since the injected waste-waters are highly saline and hence have a high electrical conductivity. To date, no study has imaged the fluids in the faults in Oklahoma and made a direct link to the seismicity. The 2016 M5.8 Pawnee, Oklahoma earthquake provides an unprecedented opportunity for scientists to provide that link. Several injection wells are located within a 20 km radius of the epicenter; and studies have suggested that injection of fluids in high-volume wells can trigger earthquakes as far away as 30 km. During late October to early November, 2016, we are collecting magnetotelluric (MT) data with the aim of constraining the distribution of fluids in the fault zone. The MT technique uses naturally occurring electric and magnetic fields measured at Earth's surface to measure conductivity structure. We plan to carry out a series of short two-dimensional (2D) profiles of wideband MT acquisition located through areas where the fault recently ruptured and seismic activity is concentrated and also across the faults in the vicinity that did not rupture. The integration of our results and ongoing seismic studies will lead to a better understanding of the links between fluid injection and seismicity.

Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

PubMed

Yi, Qu; Zhan-ming, Li; Er-chao, Li

2012-11-01

A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: (1) anomaly detection, (2) component fault detection, and (3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

Tectonic evolution of the northeastern part of the African continental margin, Egypt

NASA Astrophysics Data System (ADS)

Hussein, I. M.; Abd-Allah, A. M. A.

2001-07-01

The area between Manzalah Lake and the southern Galala Plateau in northeast Egypt constitutes the Galalas, Cairo-Suez, southern Nile Delta and northern Nile Delta structural provinces. The northern Galala Fault separates the Galalas Province from the Cairo-Suez Province and is considered to be the westward extension of the Themed Fault in central Sinai. The pre-Eocene rocks are affected by northeast to east-northeast-orientated folds and reverse faults, as well as east-west-orientated oblique-slip faults with dextral and normal components. Some folds and reverse faults are interpreted to have been formed by northwest to north-northwest-orientated compression related to the Syrian Arc movement, whereas the others by the secondary northwest orientated shortening, which accompanied dextral strike-slip component along the planes of the east-west-orientated faults. The east-west-orientated faults were initially formed during the Late Triassic/Early Jurassic extension related to the drifting of the African/Arabian Plate away from the Eurasian Plate as a result of opening of the Neotethyan Sea. The Neotethyan began to close due to convergence between the two plates, leading to the Syrian Arc deformation. This deformation mildly started in Late Cenomanian and followed by a more intensive phase in Conacian/Santonian. It mildly continued in the Maastrichtian, Early Palæocene and Late Palæocene/Early Eocene. The southward thinning of the pre-Eocene rocks controlled the intensity and style of deformation. Two deformational mechanisms are proposed for the Nile Delta hinge zone. The first is related to Late Oligocene—Early Miocene north-northwest-orientated Alpine compression. The second is related to northward gravitational sliding of the post-Oligocene shale and sandstone over Cretaceous-Eocene carbonates.

Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

An Integrated FDD System for HVAC&R Based on Virtual Sensors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Woohyun

According to the U.S Department of Energy, space heating, ventilation and air conditioning system account for 40% of residential primary energy use and for 30% of primary energy use in commercial buildings. A study released by the Energy Information Administration indicated that packaged air conditioners are widely used in 46% of all commercial buildings in the U.S. This study indicates that the annual cooling energy consumption related to the packaged air conditioner is about 160 trillion Btus. Therefore, an automated FDD system that can automatically detect and diagnose faults and evaluate fault impacts has the potential for improving energy efficiencymore » along with reducing service costs and comfort complaints. The primary bottlenecks to diagnostic implementation in the field are the high initial costs of additional sensors. To prevent those limitations, virtual sensors with low cost measurements and simple models are developed to estimate quantities that would be expensive and or difficult to measure directly. The use of virtual sensors can reduce costs compared to the use of real sensors and provide additional information for economic assessment. The virtual sensor can be embedded in a permanently installed control or monitoring system and continuous monitoring potentially leads to early detection of faults. The virtual sensors of individual equipment components can be integrated to estimate overall diagnostic information using the output of each virtual sensor.« less

Comment on "Localized water reverberation phases and its impact on back-projection images" by Yue et al. [2017

NASA Astrophysics Data System (ADS)

Fan, W.; Shearer, P. M.

2017-12-01

Fan and Shearer [2016] analyzed the 2012 Mw 7.2 Sumatra earthquake and reported that the earthquake dynamically triggered early aftershock/aftershocks 150 km away from the mainshock and 50 s later. The early aftershock/aftershocks were detected with teleseismic P-wave back-projection, coincided with passing surface waves, and showed observable seismic waveforms in a wide frequency range (0.02—5 Hz). Recently, however, Yue et al. [2017] interpreted these coda arrivals as water reverberations from the mainshock, based mostly on EGF analysis of a nearby M6 earthquake and a water-phase synthetic test. Here, we show detailed back-projection and waveform analysis of three M6 earthquakes within 100km of the Mw 7.2 earthquake, including the EGF event analyzed in Yue et al. [2017]. In addition, we examine the waveforms of three M5.5 reverse faulting earthquakes close to our detected early aftershock landward of the trench. Our results show that the coda energy in question is more likely caused by a separate earthquake near the trench than by a mainshock water reverberation phase, thus supporting our earlier conclusion that the detected coherent radiators are likely to be dynamically triggered early aftershock/aftershocks.

Error Mitigation of Point-to-Point Communication for Fault-Tolerant Computing

NASA Technical Reports Server (NTRS)

Akamine, Robert L.; Hodson, Robert F.; LaMeres, Brock J.; Ray, Robert E.

2011-01-01

Fault tolerant systems require the ability to detect and recover from physical damage caused by the hardware s environment, faulty connectors, and system degradation over time. This ability applies to military, space, and industrial computing applications. The integrity of Point-to-Point (P2P) communication, between two microcontrollers for example, is an essential part of fault tolerant computing systems. In this paper, different methods of fault detection and recovery are presented and analyzed.

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-01-01

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Astrophysics Data System (ADS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-11-01

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach

PubMed Central

2017-01-01

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system. PMID:28946673

Intermittent/transient fault phenomena in digital systems

NASA Technical Reports Server (NTRS)

Masson, G. M.

1977-01-01

An overview of the intermittent/transient (IT) fault study is presented. An interval survivability evaluation of digital systems for IT faults is discussed along with a method for detecting and diagnosing IT faults in digital systems.

Geometry and kinematics of the eastern Lake Mead fault system in the Virgin Mountains, Nevada and Arizona

USGS Publications Warehouse

Beard, Sue; Campagna, David J.; Anderson, R. Ernest

2010-01-01

The Lake Mead fault system is a northeast-striking, 130-km-long zone of left-slip in the southeast Great Basin, active from before 16 Ma to Quaternary time. The northeast end of the Lake Mead fault system in the Virgin Mountains of southeast Nevada and northwest Arizona forms a partitioned strain field comprising kinematically linked northeast-striking left-lateral faults, north-striking normal faults, and northwest-striking right-lateral faults. Major faults bound large structural blocks whose internal strain reflects their position within a left step-over of the left-lateral faults. Two north-striking large-displacement normal faults, the Lakeside Mine segment of the South Virgin–White Hills detachment fault and the Piedmont fault, intersect the left step-over from the southwest and northeast, respectively. The left step-over in the Lake Mead fault system therefore corresponds to a right-step in the regional normal fault system.Within the left step-over, displacement transfer between the left-lateral faults and linked normal faults occurs near their junctions, where the left-lateral faults become oblique and normal fault displacement decreases away from the junction. Southward from the center of the step-over in the Virgin Mountains, down-to-the-west normal faults splay northward from left-lateral faults, whereas north and east of the center, down-to-the-east normal faults splay southward from left-lateral faults. Minimum slip is thus in the central part of the left step-over, between east-directed slip to the north and west-directed slip to the south. Attenuation faults parallel or subparallel to bedding cut Lower Paleozoic rocks and are inferred to be early structures that accommodated footwall uplift during the initial stages of extension.Fault-slip data indicate oblique extensional strain within the left step-over in the South Virgin Mountains, manifested as east-west extension; shortening is partitioned between vertical for extension-dominated structural blocks and south-directed for strike-slip faults. Strike-slip faults are oblique to the extension direction due to structural inheritance from NE-striking fabrics in Proterozoic crystalline basement rocks.We hypothesize that (1) during early phases of deformation oblique extension was partitioned to form east-west–extended domains bounded by left-lateral faults of the Lake Mead fault system, from ca. 16 to 14 Ma. (2) Beginning ca. 13 Ma, increased south-directed shortening impinged on the Virgin Mountains and forced uplift, faulting, and overturning along the north and west side of the Virgin Mountains. (3) By ca. 10 Ma, initiation of the younger Hen Spring to Hamblin Bay fault segment of the Lake Mead fault system accommodated westward tectonic escape, and the focus of south-directed shortening transferred to the western Lake Mead region. The shift from early partitioned oblique extension to south-directed shortening may have resulted from initiation of right-lateral shear of the eastern Walker Lane to the west coupled with left-lateral shear along the eastern margin of the Great Basin.

The detection error of thermal test low-frequency cable based on M sequence correlation algorithm

NASA Astrophysics Data System (ADS)

Wu, Dongliang; Ge, Zheyang; Tong, Xin; Du, Chunlin

2018-04-01

The problem of low accuracy and low efficiency of off-line detecting on thermal test low-frequency cable faults could be solved by designing a cable fault detection system, based on FPGA export M sequence code(Linear feedback shift register sequence) as pulse signal source. The design principle of SSTDR (Spread spectrum time-domain reflectometry) reflection method and hardware on-line monitoring setup figure is discussed in this paper. Testing data show that, this detection error increases with fault location of thermal test low-frequency cable.

An intelligent control system for failure detection and controller reconfiguration

NASA Technical Reports Server (NTRS)

Biswas, Saroj K.

1994-01-01

We present an architecture of an intelligent restructurable control system to automatically detect failure of system components, assess its impact on system performance and safety, and reconfigure the controller for performance recovery. Fault detection is based on neural network associative memories and pattern classifiers, and is implemented using a multilayer feedforward network. Details of the fault detection network along with simulation results on health monitoring of a dc motor have been presented. Conceptual developments for fault assessment using an expert system and controller reconfiguration using a neural network are outlined.

Sensor fault detection and recovery in satellite attitude control

NASA Astrophysics Data System (ADS)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

Method and apparatus for in-situ detection and isolation of aircraft engine faults

NASA Technical Reports Server (NTRS)

Bonanni, Pierino Gianni (Inventor); Brunell, Brent Jerome (Inventor)

2007-01-01

A method for performing a fault estimation based on residuals of detected signals includes determining an operating regime based on a plurality of parameters, extracting predetermined noise standard deviations of the residuals corresponding to the operating regime and scaling the residuals, calculating a magnitude of a measurement vector of the scaled residuals and comparing the magnitude to a decision threshold value, extracting an average, or mean direction and a fault level mapping for each of a plurality of fault types, based on the operating regime, calculating a projection of the measurement vector onto the average direction of each of the plurality of fault types, determining a fault type based on which projection is maximum, and mapping the projection to a continuous-valued fault level using a lookup table.

In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

NASA Technical Reports Server (NTRS)

Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

2005-01-01

In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

Advanced model-based FDIR techniques for aerospace systems: Today challenges and opportunities

NASA Astrophysics Data System (ADS)

Zolghadri, Ali

2012-08-01

This paper discusses some trends and recent advances in model-based Fault Detection, Isolation and Recovery (FDIR) for aerospace systems. The FDIR challenges range from pre-design and design stages for upcoming and new programs, to improvement of the performance of in-service flying systems. For space missions, optimization of flight conditions and safe operation is intrinsically related to GNC (Guidance, Navigation & Control) system of the spacecraft and includes sensors and actuators monitoring. Many future space missions will require autonomous proximity operations including fault diagnosis and the subsequent control and guidance recovery actions. For upcoming and future aircraft, one of the main issues is how early and robust diagnosis of some small and subtle faults could contribute to the overall optimization of aircraft design. This issue would be an important factor for anticipating the more and more stringent requirements which would come in force for future environmentally-friendlier programs. The paper underlines the reasons for a widening gap between the advanced scientific FDIR methods being developed by the academic community and technological solutions demanded by the aerospace industry.

Automated diagnosis of rolling bearings using MRA and neural networks

NASA Astrophysics Data System (ADS)

Castejón, C.; Lara, O.; García-Prada, J. C.

2010-01-01

Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in productive processes are caused by bearings. They begin to deteriorate from early stages of their functional life, also called the incipient level. This manuscript develops an automated diagnosis of rolling bearings based on the analysis and classification of signature vibrations. The novelty of this work is the application of the methodology proposed for data collected from a quasi-real industrial machine, where rolling bearings support the radial and axial loads the bearings are designed for. Multiresolution analysis (MRA) is used in a first stage in order to extract the most interesting features from signals. Features will be used in a second stage as inputs of a supervised neural network (NN) for classification purposes. Experimental results carried out in a real system show the soundness of the method which detects four bearing conditions (normal, inner race fault, outer race fault and ball fault) in a very incipient stage.

Jurassic faults of southwest Alabama and offshore areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mink, R.M.; Tew, B.H.; Bearden, B.L.

1991-03-01

Four fault groups affecting Jurassic strata occur in the southwest and offshore Alabama areas. They include the regional basement rift trend, the regional peripheral fault trend, the Mobile graben fault system, and the Lower Mobile Bay fault system. The regional basement system rift and regional peripheral fault trends are distinct and rim the inner margin of the eastern Gulf Coastal Plain. The regional basement rift trend is genetically related to the breakup of Pangea and the opening of the Gulf of Mexico in the Late Triassic-Early Jurassic. This fault trend is thought to have formed contemporaneously with deposition of Latemore » Triassic-Early Jurassic Eagle Mills Formation and to displace pre-Mesozoic rocks. The regional peripheral fault trend consists of a group of en echelon extensional faults that are parallel or subparallel to regional strike of Gulf Coastal Plain strata and correspond to the approximate updip limit of thick Louann Salt. Nondiapiric salt features are associated with the trend and maximum structural development is exhibited in the Haynesville-Smackover section. No hydrocarbon accumulations have been documented in the pre-Jurassic strata of southwest and offshore Alabama. Productive hydrocarbon reservoirs occur in Jurassic strata along the trends of the fault groups, suggesting a significant relationship between structural development in the Jurassic and hydrocarbon accumulation. Hydrocarbon traps are generally structural or contain a major structural component and include salt anticlines, faulted salt anticlines, and extensional fault traps. All of the major hydrocarbon accumulations are associated with movement of the Louann Salt along the regional peripheral fault trend, the Mobile graben fault system, or the Lower Mobile Bay fault system.« less

Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

PubMed Central

Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

2011-01-01

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

Distributed Fault-Tolerant Control of Networked Uncertain Euler-Lagrange Systems Under Actuator Faults.

PubMed

Chen, Gang; Song, Yongduan; Lewis, Frank L

2016-05-03

This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node being the active target system. The proposed algorithm is capable of compensating for the actuator bias fault, the partial loss of effectiveness actuation fault, the communication link fault, the model uncertainty, and the external disturbance simultaneously. The control scheme does not use any fault detection and isolation mechanism to detect, separate, and identify the actuator faults online, which largely reduces the online computation and expedites the responsiveness of the controller. To validate the effectiveness of the proposed method, a test-bed of multiple robot-arm cooperative control system is developed for real-time verification. Experiments on the networked robot-arms are conduced and the results confirm the benefits and the effectiveness of the proposed distributed fault-tolerant control algorithms.

Wavelet Based Protection Scheme for Multi Terminal Transmission System with PV and Wind Generation

NASA Astrophysics Data System (ADS)

Manju Sree, Y.; Goli, Ravi kumar; Ramaiah, V.

2017-08-01

A hybrid generation is a part of large power system in which number of sources usually attached to a power electronic converter and loads are clustered can operate independent of the main power system. The protection scheme is crucial against faults based on traditional over current protection since there are adequate problems due to fault currents in the mode of operation. This paper adopts a new approach for detection, discrimination of the faults for multi terminal transmission line protection in presence of hybrid generation. Transient current based protection scheme is developed with discrete wavelet transform. Fault indices of all phase currents at all terminals are obtained by analyzing the detail coefficients of current signals using bior 1.5 mother wavelet. This scheme is tested for different types of faults and is found effective for detection and discrimination of fault with various fault inception angle and fault impedance.

A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

NASA Astrophysics Data System (ADS)

Kim, Pyung Soo

2017-04-01

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

Selection of test paths for solder joint intermittent connection faults under DC stimulus

NASA Astrophysics Data System (ADS)

Huakang, Li; Kehong, Lv; Jing, Qiu; Guanjun, Liu; Bailiang, Chen

2018-06-01

The test path of solder joint intermittent connection faults under direct-current stimulus is examined in this paper. According to the physical structure of the circuit, a network model is established first. A network node is utilised to represent the test node. The path edge refers to the number of intermittent connection faults in the path. Then, the selection criteria of the test path based on the node degree index are proposed and the solder joint intermittent connection faults are covered using fewer test paths. Finally, three circuits are selected to verify the method. To test if the intermittent fault is covered by the test paths, the intermittent fault is simulated by a switch. The results show that the proposed method can detect the solder joint intermittent connection fault using fewer test paths. Additionally, the number of detection steps is greatly reduced without compromising fault coverage.

Protection of Renewable-dominated Microgrids: Challenges and Potential Solutions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elkhatib, Mohamed; Ellis, Abraham; Milan Biswal

keywords : Microgrid Protection, Impedance Relay, Signal Processing-based Fault Detec- tion, Networked Microgrids, Communication-Assisted Protection In this report we address the challenge of designing efficient protection system for inverter- dominated microgrids. These microgrids are characterised with limited fault current capacity as a result of current-limiting protection functions of inverters. Typically, inverters limit their fault contribution in sub-cycle time frame to as low as 1.1 per unit. As a result, overcurrent protection could fail completely to detect faults in inverter-dominated microgrids. As part of this project a detailed literature survey of existing and proposed microgrid protection schemes were conducted. The surveymore » concluded that there is a gap in the available microgrid protection methods. The only credible protection solution available in literature for low- fault inverter-dominated microgrids is the differential protection scheme which represents a robust transmission-grade protection solution but at a very high cost. Two non-overcurrent protection schemes were investigated as part of this project; impedance-based protection and transient-based protection. Impedance-based protection depends on monitoring impedance trajectories at feeder relays to detect faults. Two communication-based impedance-based protection schemes were developed. the first scheme utilizes directional elements and pilot signals to locate the fault. The second scheme depends on a Central Protection Unit that communicates with all feeder relays to locate the fault based on directional flags received from feeder relays. The later approach could potentially be adapted to protect networked microgrids and dynamic topology microgrids. Transient-based protection relies on analyzing high frequency transients to detect and locate faults. This approach is very promising but its implementation in the filed faces several challenges. For example, high frequency transients due to faults can be confused with transients due to other events such as capacitor switching. Additionally, while detecting faults by analyzing transients could be doable, locating faults based on analyzing transients is still an open question.« less

Role of extensional structures on the location of folds and thrusts during tectonic inversion (northern Iberian Chain, Spain)

NASA Astrophysics Data System (ADS)

Cortés, Angel L.; Liesa, Carlos L.; Soria, Ana R.; Meléndez, Alfonso

1999-03-01

The Aguilón Subbasin (NE Spain) was originated daring the Late Jurassic-Early Cretaceous rifting due to the action of large normal faults, probably inherited from Late Variscan fracturing. WNW-ESE normal faults limit two major troughs filled by continental deposits (Valanginian to Early Barremian). NE-SW faults control the location of subsidiary depocenters within these troughs. These basins were weakly inverted during the Tertiary with folds and thrusts striking E-W to WNW-ESE involving the Mesozoic-Tertiary cover with a maximum estimated shortening of about 12 %. Tertiary compression did not produce the total inversion of the Mesozoic basin but extensional structures are responsible for the location of major Tertiary folds. Shortening of the cover during the Tertiary involved both reactivation of some normal faults and development of folds and thrusts nucleated on basement extensional steps. The inversion style depends mainly on the occurrence and geometry of normal faults limiting the basin. Steep normal faults were not reactivated but acted as buttresses to the cover translation. Around these faults, affecting both basement and cover, folds and thrusts were nucleated due to the stress rise in front of major faults. Within the cover, the buttressing against normal faults consists of folding and faulting implying little shortening without development of ceavage or other evidence of internal deformation.

Kinematics of polygonal fault systems: observations from the northern North Sea

NASA Astrophysics Data System (ADS)

Wrona, Thilo; Magee, Craig; Jackson, Christopher A.-L.; Huuse, Mads; Taylor, Kevin G.

2017-12-01

Layer-bound, low-displacement normal faults, arranged into a broadly polygonal pattern, are common in many sedimentary basins. Despite having constrained their gross geometry, we have a relatively poor understanding of the processes controlling the nucleation and growth (i.e. the kinematics) of polygonal fault systems. In this study we use high-resolution 3-D seismic reflection and borehole data from the northern North Sea to undertake a detailed kinematic analysis of faults forming part of a seismically well-imaged polygonal fault system hosted within the up to 1000 m thick, Early Palaeocene-to-Middle Miocene mudstones of the Hordaland Group. Growth strata and displacement-depth profiles indicate faulting commenced during the Eocene to early Oligocene, with reactivation possibly occurring in the late Oligocene to middle Miocene. Mapping the position of displacement maxima on 137 polygonal faults suggests that the majority (64%) nucleated in the lower 500 m of the Hordaland Group. The uniform distribution of polygonal fault strikes in the area indicates that nucleation and growth were not driven by gravity or far-field tectonic extension as has previously been suggested. Instead, fault growth was likely facilitated by low coefficients of residual friction on existing slip surfaces, and probably involved significant layer-parallel contraction (strains of 0.01-0.19) of the host strata. To summarize, our kinematic analysis provides new insights into the spatial and temporal evolution of polygonal fault systems.

Surface Morphology of Active Normal Faults in Hard Rock: Implications for the Mechanics of the Asal Rift, Djibouti

NASA Astrophysics Data System (ADS)

Pinzuti, P.; Mignan, A.; King, G. C.

2009-12-01

Mechanical stretching models have been previously proposed to explain the process of continental break-up through the example of the Asal Rift, Djibouti, one of the few places where the early stages of seafloor spreading can be observed. In these models, deformation is distributed starting at the base of a shallow seismogenic zone, in which sub-vertical normal faults are responsible for subsidence whereas cracks accommodate extension. Alternative models suggest that extension results from localized magma injection, with normal faults accommodating extension and subsidence above the maximum reach of the magma column. In these magmatic intrusion models, normal faults have dips of 45-55° and root into dikes. Using mechanical and kinematics concepts and vertical profiles of normal fault scarps from an Asal Rift campaign, where normal faults are sub-vertical on surface level, we discuss the creation and evolution of normal faults in massive fractured rocks (basalt). We suggest that the observed fault scarps correspond to sub-vertical en echelon structures and that at greater depth, these scarps combine and give birth to dipping normal faults. Finally, the geometry of faulting between the Fieale volcano and Lake Asal in the Asal Rift can be simply related to the depth of diking, which in turn can be related to magma supply. This new view supports the magmatic intrusion model of early stages of continental breaking.

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis.

PubMed

Liu, Jinjun; Leng, Yonggang; Lai, Zhihui; Fan, Shengbo

2018-04-25

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method.

Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems.

PubMed

Mekki, Hemza; Benzineb, Omar; Boukhetala, Djamel; Tadjine, Mohamed; Benbouzid, Mohamed

2015-07-01

The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Periodic Application of Concurrent Error Detection in Processor Array Architectures. PhD. Thesis -

NASA Technical Reports Server (NTRS)

Chen, Paul Peichuan

1993-01-01

Processor arrays can provide an attractive architecture for some applications. Featuring modularity, regular interconnection and high parallelism, such arrays are well-suited for VLSI/WSI implementations, and applications with high computational requirements, such as real-time signal processing. Preserving the integrity of results can be of paramount importance for certain applications. In these cases, fault tolerance should be used to ensure reliable delivery of a system's service. One aspect of fault tolerance is the detection of errors caused by faults. Concurrent error detection (CED) techniques offer the advantage that transient and intermittent faults may be detected with greater probability than with off-line diagnostic tests. Applying time-redundant CED techniques can reduce hardware redundancy costs. However, most time-redundant CED techniques degrade a system's performance.

Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Peter R.; Laughman, C R.; Leeb, S B.

Non-intrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic contents to concise ''signatures''. Changes in these signatures can be used to detect, and in many cases directly diagnose, equipment and component faults associated with roof-top cooling units. Use of the NILM for fault detection and diagnosis (FDD) is important because (1) it complements other FDD schemes that are based on thermo-fluid sensors and analyses and (2) it is minimally intrusive (one measuring point in the relatively protected confines of the control panel) and therefore inherently reliable. Thismore » paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection.« less

Detecting the transition to failure: wavelet analysis of multi-scale crack patterns at different confining pressures

NASA Astrophysics Data System (ADS)

Rizzo, R. E.; Healy, D.; Farrell, N. J.

2017-12-01

Numerous laboratory brittle deformation experiments have shown that a rapid transition exists in the behaviour of porous materials under stress: at a certain point, early formed tensile cracks interact and coalesce into a `single' narrow zone, the shear plane, rather than remaining distributed throughout the material. In this work, we present and apply a novel image processing tool which is able to quantify this transition between distributed (`stable') damage accumulation and localised (`unstable') deformation, in terms of size, density, and orientation of cracks at the point of failure. Our technique, based on a two-dimensional (2D) continuous Morlet wavelet analysis, can recognise, extract and visually separate the multi-scale changes occurring in the fracture network during the deformation process. We have analysed high-resolution SEM-BSE images of thin sections of Hopeman Sandstone (Scotland, UK) taken from core plugs deformed under triaxial conditions, with increasing confining pressure. Through this analysis, we can determine the relationship between the initial orientation of tensile microcracks and the final geometry of the through-going shear fault, exploiting the total areal coverage of the analysed image. In addition, by comparing patterns of fractures in thin sections derived from triaxial (σ1>σ2=σ3=Pc) laboratory experiments conducted at different confining pressures (Pc), we can quantitatively explore the relationship between the observed geometry and the inferred mechanical processes. The methodology presented here can have important implications for larger-scale mechanical problems related to major fault propagation. Just as a core plug scale fault localises through extension and coalescence of microcracks, larger faults also grow by extension and coalescence of segments in a multi-scale process by which microscopic cracks can ultimately lead to macroscopic faulting. Consequently, wavelet analysis represents a useful tool for fracture pattern recognition, applicable to the detection of the transitions occurring at the time of catastrophic rupture.

Fault detection and classification in electrical power transmission system using artificial neural network.

PubMed

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

Wind turbine fault detection and classification by means of image texture analysis

NASA Astrophysics Data System (ADS)

Ruiz, Magda; Mujica, Luis E.; Alférez, Santiago; Acho, Leonardo; Tutivén, Christian; Vidal, Yolanda; Rodellar, José; Pozo, Francesc

2018-07-01

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5 MW offshore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

Exhumation History of an Oblique Plate Boundary: Investigating Kaikoura Mountain-building within the Marlborough Fault System, NE South Island New Zealand

NASA Astrophysics Data System (ADS)

Collett, C.; Duvall, A. R.; Flowers, R. M.; Tucker, G. E.

2015-12-01

The Kaikoura Mountains stand high as topographic anomalies in the oblique Pacific-Australian plate boundary zone known as the Marlborough Fault System (MFS), NE South Island New Zealand. The base of both the Inland and Seaward Kaikoura Ranges are bound on the SE by major, steeply NW-dipping, right lateral, active strike-slips (Clarence and Hope faults of the MFS, respectively). Previous geologic mapping, observations of predominantly horizontal fault slip at the surface from GPS and offset Quaternary deposits, and uplift of marine terraces, provide evidence for shortening and mountain-building via distributed deformation off of the main MFS strike-slip faults. However, quantitative estimates of the magnitude and spatial patterns of exhumation and of the timing of mountain-building in the Kaikouras are needed to understand more fully the nature of oblique deformation in the MFS. We present new apatite and zircon (U-Th)/He ages from opposite sides of the Hope and Clarence faults, spanning over 2 km of relief within the Kaikoura Mountains to identify spatial and temporal changes in exhumation rates in relation to the adjacent faults. Young (~3 Ma) apatite He ages and rapid (potentially > 1 mm/yr) exhumation rates from opposite sides of the faults are consistent with previously mentioned evidence of recent, regional, distributed deformation off of the main MFS faults. Moreover, early Miocene zircon He ages imply that parts of this region experienced an earlier phase of fault-related exhumation. Large changes in zircon He ages across the faults from ~20 Ma to > 100 Ma support hypotheses that portions of the Marlborough Faults may be re-activated, early Miocene thrusts. The zircon data are also consistent with the hypothesis of an early Miocene initiation of the oblique Pacific-Australian plate boundary in this region. Evidence for this comes from a change in sedimentation during this time from fine marine sediments to coarse, terrigenous conglomerates. Observing more than one phase of deformation in this active, oblique tectonic setting provides a new quantitative assessment of the evolution of the Pacific-Australian plate boundary in this region and how the accommodation of deformation may change over time.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

PubMed

Jeon, Namju; Lee, Hyeongcheol

2016-12-12

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

Series and parallel arc-fault circuit interrupter tests.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Fresquez, Armando J.; Gudgel, Bob

2013-07-01

While the 2011 National Electrical Codeª (NEC) only requires series arc-fault protection, some arc-fault circuit interrupter (AFCI) manufacturers are designing products to detect and mitigate both series and parallel arc-faults. Sandia National Laboratories (SNL) has extensively investigated the electrical differences of series and parallel arc-faults and has offered possible classification and mitigation solutions. As part of this effort, Sandia National Laboratories has collaborated with MidNite Solar to create and test a 24-string combiner box with an AFCI which detects, differentiates, and de-energizes series and parallel arc-faults. In the case of the MidNite AFCI prototype, series arc-faults are mitigated by openingmore » the PV strings, whereas parallel arc-faults are mitigated by shorting the array. A range of different experimental series and parallel arc-fault tests with the MidNite combiner box were performed at the Distributed Energy Technologies Laboratory (DETL) at SNL in Albuquerque, NM. In all the tests, the prototype de-energized the arc-faults in the time period required by the arc-fault circuit interrupt testing standard, UL 1699B. The experimental tests confirm series and parallel arc-faults can be successfully mitigated with a combiner box-integrated solution.« less

Integrated analysis of error detection and recovery

NASA Technical Reports Server (NTRS)

Shin, K. G.; Lee, Y. H.

1985-01-01

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2015-01-01

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2014-01-01

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

2015-12-31

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

The Rome trough and evolution of the Iapetean margin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walker, D.; Hamilton-Smith, T.; Drahovzal, J.A.

1991-08-01

Recent structural mapping of the Rome trough suggests a complex structure very different from the symmetrical and laterally continuous graben commonly depicted. Early and Middle Cambrian extension in the Rome trough of eastern Kentucky and adjacent areas resulted in a series of alternately facing half-grabens with variable displacement. These half-grabens are bounded by southwest-northeast-trending normal faults (e.g., Kentucky River and Warfield faults), which are laterally continuous only on the order to tens of kilometers. The Rome trough is laterally segmented by north-south-trending faults (e.g., Lexington fault) commonly expressed as flexures in younger rocks (e.g., Burning Springs anticline and Floyd Countymore » channel). Many of these north-south-trending faults have significant left-lateral displacement, and probably represent reactivated thrust faults of the Grenville tectonic front. The Rome trough and the associated Mississippi Valley, Rough Creek, and Birmingham fault systems were initiated during an Early Cambrian shift in sea-floor spreading from the Blue Ridge-Pine Mountain rift to the Ouachita rift along the Alabama-Oklahoma transform fault. These fault systems have been proposed as having originated from extensional stress propagated northward from the Ouachita rift across the transform fault. In the alternate model proposed here, faulting was brittle, extensional failure resulting form subsidence and flexure of the continental margin to the east. Following initiation of sea-floor spreading at the Blue Ridge-Pine Mountain rift in the latest Proterozoic, margin subsidence in the presence of the Alabama-Oklahoma transform boundary and the inherited Grenville tectonic front resulted in this interior cratonic fault system.« less

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

PubMed

Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping

2018-05-16

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Health Monitoring of a Satellite System

NASA Technical Reports Server (NTRS)

Chen, Robert H.; Ng, Hok K.; Speyer, Jason L.; Guntur, Lokeshkumar S.; Carpenter, Russell

2004-01-01

A health monitoring system based on analytical redundancy is developed for satellites on elliptical orbits. First, the dynamics of the satellite including orbital mechanics and attitude dynamics is modelled as a periodic system. Then, periodic fault detection filters are designed to detect and identify the satellite's actuator and sensor faults. In addition, parity equations are constructed using the algebraic redundant relationship among the actuators and sensors. Furthermore, a residual processor is designed to generate the probability of each of the actuator and sensor faults by using a sequential probability test. Finally, the health monitoring system, consisting of periodic fault detection lters, parity equations and residual processor, is evaluated in the simulation in the presence of disturbances and uncertainty.

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

NASA Astrophysics Data System (ADS)

Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart; Thomas, Sobi; Andreasen, Søren Juhl; Kær, Søren Knudsen

2017-08-01

This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%.

An Extension to the Kalman Filter for an Improved Detection of Unknown Behavior

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Narasimhan, Sriram

2005-01-01

The use of Kalman filter (KF) interferes with fault detection algorithms based on the residual between estimated and measured variables, since the measured values are used to update the estimates. This feedback results in the estimates being pulled closer to the measured values, influencing the residuals in the process. Here we present a fault detection scheme for systems that are being tracked by a KF. Our approach combines an open-loop prediction over an adaptive window and an information-based measure of the deviation of the Kalman estimate from the prediction to improve fault detection.

Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

NASA Astrophysics Data System (ADS)

Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin

2018-03-01

In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.

Protection of Renewable-dominated Microgrids: Challenges and Potential Solutions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elkhatib, Mohamed; Ellis, Abraham; Biswal, Milan

In this report we address the challenge of designing efficient protection system for inverter- dominated microgrids. These microgrids are characterised with limited fault current capacity as a result of current-limiting protection functions of inverters. Typically, inverters limit their fault contribution in sub-cycle time frame to as low as 1.1 per unit. As a result, overcurrent protection could fail completely to detect faults in inverter-dominated microgrids. As part of this project a detailed literature survey of existing and proposed microgrid protection schemes were conducted. The survey concluded that there is a gap in the available microgrid protection methods. The only crediblemore » protection solution available in literature for low- fault inverter-dominated microgrids is the differential protection scheme which represents a robust transmission-grade protection solution but at a very high cost. Two non-overcurrent protection schemes were investigated as part of this project; impedance-based protection and transient-based protection. Impedance-based protection depends on monitoring impedance trajectories at feeder relays to detect faults. Two communication-based impedance-based protection schemes were developed. the first scheme utilizes directional elements and pilot signals to locate the fault. The second scheme depends on a Central Protection Unit that communicates with all feeder relays to locate the fault based on directional flags received from feeder relays. The later approach could potentially be adapted to protect networked microgrids and dynamic topology microgrids. Transient-based protection relies on analyzing high frequency transients to detect and locate faults. This approach is very promising but its implementation in the filed faces several challenges. For example, high frequency transients due to faults can be confused with transients due to other events such as capacitor switching. Additionally, while detecting faults by analyzing transients could be doable, locating faults based on analyzing transients is still an open question.« less

A probabilistic method to diagnose faults of air handling units

NASA Astrophysics Data System (ADS)

Dey, Debashis

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration which can result in hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon sensor data and control signals that are commonly available in these systems. Although APAR has many advantages over other methods, for example no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. For instance, a fault on temperature sensor could be fixed bias, drifting bias, inappropriate location, complete failure. Also a fault in mixing box can be return and outdoor damper leak or stuck. In addition, when multiple rules are satisfied the list of faults increases. There is no proper way to have the correct diagnosis for rule based fault detection system. To overcome this limitation we proposed Bayesian Belief Network (BBN) as a diagnostic tool. BBN can be used to simulate diagnostic thinking of FDD experts through a probabilistic way. In this study we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief network. Bayesian Belief Network is used as a decision support tool for rule based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. Also it can get information from previous AHU operating conditions and maintenance records to provide proper diagnosis. The proposed model is validated with real time measured data of a campus building at University of Texas at San Antonio (UTSA).The results show that BBN is correctly able to prioritize faults which can be verified by manual investigation.

Neogene contraction between the San Andreas fault and the Santa Clara Valley, San Francisco Bay region, California

USGS Publications Warehouse

McLaughlin, R.J.; Langenheim, V.E.; Schmidt, K.M.; Jachens, R.C.; Stanley, R.G.; Jayko, A.S.; McDougall, K.A.; Tinsley, J.C.; Valin, Z.C.

1999-01-01

In the southern San Francisco Bay region of California, oblique dextral reverse faults that verge northeastward from the San Andreas fault experienced triggered slip during the 1989 M7.1 Loma Prieta earthquake. The role of these range-front thrusts in the evolution of the San Andreas fault system and the future seismic hazard that they may pose to the urban Santa Clara Valley are poorly understood. Based on recent geologic mapping and geophysical investigations, we propose that the range-front thrust system evolved in conjunction with development of the San Andreas fault system. In the early Miocene, the region was dominated by a system of northwestwardly propagating, basin-bounding, transtensional faults. Beginning as early as middle Miocene time, however, the transtensional faulting was superseded by transpressional NE-stepping thrust and reverse faults of the range-front thrust system. Age constraints on the thrust faults indicate that the locus of contraction has focused on the Monte Vista, Shannon, and Berrocal faults since about 4.8 Ma. Fault slip and fold reconstructions suggest that crustal shortening between the San Andreas fault and the Santa Clara Valley within this time frame is ~21%, amounting to as much as 3.2 km at a rate of 0.6 mm/yr. Rates probably have not remained constant; average rates appear to have been much lower in the past few 100 ka. The distribution of coseismic surface contraction during the Loma Prieta earthquake, active seismicity, late Pleistocene to Holocene fluvial terrace warping, and geodetic data further suggest that the active range-front thrust system includes blind thrusts. Critical unresolved issues include information on the near-surface locations of buried thrusts, the timing of recent thrust earthquake events, and their recurrence in relation to earthquakes on the San Andreas fault.

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-01-01

ADEPT is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system, and is designed for two modes of operation: real-time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a Laser printer. This system consists of a simulated Space Station power module using direct-current power supplies for Solar arrays on three power busses. For tests of the system's ability to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three busses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modelling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base. A load scheduler and a fault recovery system are currently under development to support both modes of operation.

RCS propulsion functional path analysis for performance monitoring fault detection and annunciation

NASA Technical Reports Server (NTRS)

Keesler, E. L.

1974-01-01

The operational flight instrumentation required for performance monitoring and fault detection are presented. Measurements by the burn through monitors are presented along with manifold and helium source pressures.

On damage detection in wind turbine gearboxes using outlier analysis

NASA Astrophysics Data System (ADS)

Antoniadou, Ifigeneia; Manson, Graeme; Dervilis, Nikolaos; Staszewski, Wieslaw J.; Worden, Keith

2012-04-01

The proportion of worldwide installed wind power in power systems increases over the years as a result of the steadily growing interest in renewable energy sources. Still, the advantages offered by the use of wind power are overshadowed by the high operational and maintenance costs, resulting in the low competitiveness of wind power in the energy market. In order to reduce the costs of corrective maintenance, the application of condition monitoring to gearboxes becomes highly important, since gearboxes are among the wind turbine components with the most frequent failure observations. While condition monitoring of gearboxes in general is common practice, with various methods having been developed over the last few decades, wind turbine gearbox condition monitoring faces a major challenge: the detection of faults under the time-varying load conditions prevailing in wind turbine systems. Classical time and frequency domain methods fail to detect faults under variable load conditions, due to the temporary effect that these faults have on vibration signals. This paper uses the statistical discipline of outlier analysis for the damage detection of gearbox tooth faults. A simplified two-degree-of-freedom gearbox model considering nonlinear backlash, time-periodic mesh stiffness and static transmission error, simulates the vibration signals to be analysed. Local stiffness reduction is used for the simulation of tooth faults and statistical processes determine the existence of intermittencies. The lowest level of fault detection, the threshold value, is considered and the Mahalanobis squared-distance is calculated for the novelty detection problem.

Common faults and their impacts for rooftop air conditioners

DOE Office of Scientific and Technical Information (OSTI.GOV)

Breuker, M.S.; Braun, J.E.

This paper identifies important faults and their performance impacts for rooftop air conditioners. The frequencies of occurrence and the relative costs of service for different faults were estimated through analysis of service records. Several of the important and difficult to diagnose refrigeration cycle faults were simulated in the laboratory. Also, the impacts on several performance indices were quantified through transient testing for a range of conditions and fault levels. The transient test results indicated that fault detection and diagnostics could be performed using methods that incorporate steady-state assumptions and models. Furthermore, the fault testing led to a set of genericmore » rules for the impacts of faults on measurements that could be used for fault diagnoses. The average impacts of the faults on cooling capacity and coefficient of performance (COP) were also evaluated. Based upon the results, all of the faults are significant at the levels introduced, and should be detected and diagnosed by an FDD system. The data set obtained during this work was very comprehensive, and was used to design and evaluate the performance of an FDD method that will be reported in a future paper.« less

Learning and diagnosing faults using neural networks

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

1990-01-01

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Methodology for fault detection in induction motors via sound and vibration signals

NASA Astrophysics Data System (ADS)

Delgado-Arredondo, Paulo Antonio; Morinigo-Sotelo, Daniel; Osornio-Rios, Roque Alfredo; Avina-Cervantes, Juan Gabriel; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene de Jesus

2017-01-01

Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.

Characterization of emission microscopy and liquid crystal thermography in IC fault localization

NASA Astrophysics Data System (ADS)

Lau, C. K.; Sim, K. S.

2013-05-01

This paper characterizes two fault localization techniques - Emission Microscopy (EMMI) and Liquid Crystal Thermography (LCT) by using integrated circuit (IC) leakage failures. The majority of today's semiconductor failures do not reveal a clear visual defect on the die surface and therefore require fault localization tools to identify the fault location. Among the various fault localization tools, liquid crystal thermography and frontside emission microscopy are commonly used in most semiconductor failure analysis laboratories. Many people misunderstand that both techniques are the same and both are detecting hot spot in chip failing with short or leakage. As a result, analysts tend to use only LCT since this technique involves very simple test setup compared to EMMI. The omission of EMMI as the alternative technique in fault localization always leads to incomplete analysis when LCT fails to localize any hot spot on a failing chip. Therefore, this research was established to characterize and compare both the techniques in terms of their sensitivity in detecting the fault location in common semiconductor failures. A new method was also proposed as an alternative technique i.e. the backside LCT technique. The research observed that both techniques have successfully detected the defect locations resulted from the leakage failures. LCT wass observed more sensitive than EMMI in the frontside analysis approach. On the other hand, EMMI performed better in the backside analysis approach. LCT was more sensitive in localizing ESD defect location and EMMI was more sensitive in detecting non ESD defect location. Backside LCT was proven to work as effectively as the frontside LCT and was ready to serve as an alternative technique to the backside EMMI. The research confirmed that LCT detects heat generation and EMMI detects photon emission (recombination radiation). The analysis results also suggested that both techniques complementing each other in the IC fault localization. It is necessary for a failure analyst to use both techniques when one of the techniques produces no result.

A mechanical model for complex fault patterns induced by fluid overpressures due to dehydration reaction within evaporitic rocks

NASA Astrophysics Data System (ADS)

de Paola, N.; Collettini, C.; Trippetta, F.; Barchi, M. R.; Minelli, G.

2006-12-01

Complex fault patterns, i.e. faults which exhibit a diverse range of strikes, may develop under a weak/absent regional tectonic field (e.g. polygonal faults). We studied a complex synsedimentary fault pattern, geometrically similar to polygonal fault systems, developed during an early Jurassic faulting episode and exposed in the Umbria-Marche Apennines (Italy). Along the passive margin of the African plate, these faults disrupt the Early Jurassic platform overlying the Triassic Evaporites, and bound the subsiding basins where a pelagic succession was successively deposited. We digitised the fault pattern at the regional scale on the grounds of the available geological maps, characterising each fault in terms of attitude, length and throw (i.e. vertical displacement). Fault statistical analysis shows a largely scattered orientation, a high grade of fragmentation, an average length of about 10 km and a constant length/displacement ratio. The measured stratigraphic throw ranges from 300 m to 700 m leading to very low long-term fault slip rates (less than 0.1 mm/yr). We propose a mechanical model where Jurassic faulting has been strongly influenced by the onset of dehydration of the Triassic Evaporites, made of interbedded gypsum layers and dolostones. Dehydration, i.e. anhydritization of the gypsum rich layers, initiated during burial at 1000 m of depth. During initial phases of dehydration increasing fluid pressures trapped at the gypsum-dolostones interface, promote hydrofracturing and faulting within the dolostone layers and subsequent fluid release. Fluid expulsion produces volume contraction of the dehydrating rocks causing vertical thinning and horizontal isotropic extension. This state of non-plane strain is accommodated within the composite gypsum-dolostones sequence by a mix of ductile (flowage and boudinage) and brittle (hydrofracturing and faulting) deformation processes. The stress field caused by the former processes, consistent with an almost isotropic stress distribution within the horizontal plane, explains well the studied complex fault pattern and seems to be dominant over the far-field regional extensional tectonics.

Seismic reflection images of shallow faulting, northernmost Mississippi embayment, north of the New Madrid seismic zone

USGS Publications Warehouse

McBride, J.H.; Nelson, W.J.

2001-01-01

High-resolution seismic reflection surveys document tectonic faults that displace Pleistocene and older strata just beyond the northeast termination of the New Madrid seismic zone, at the northernmost extent of the Mississippi embayment. These faults, which are part of the Fluorspar Area fault complex in southeastern Illinois, are directly in line with the northeast-trending seismic zone. The reflection data were acquired using an elastic weight-drop source recorded to 500 msec by a 48-geophone array (24-fold) with a 10-ft (??3.0m) station interval. Recognizable reflections were recorded to about 200 msec (100-150 m). The effects of multiple reflections, numerous diffractions, low apparent velocity (i.e., steeply dipping) noise, and the relatively low-frequency content of the recorded signal provided challenges for data processing and interpreting subtle fault offsets. Data processing steps that were critical to the detection of faults included residual statics, post-stack migration, deconvolution, and noise-reduction filtering. Seismic migration was crucial for detecting and mitigating complex fault-related diffraction patterns, which produced an apparent 'folding' of reflectors on unmigrated sections. Detected individual offsets of shallow reflectors range from 5 to 10 m for the top of Paleozoic bedrock and younger strata. The migrated sections generally indicate vertical to steeply dipping normal and reverse faults, which in places outline small horsts and/or grabens. Tilting or folding of stratal reflectors associated with faulting is also locally observed. At one site, the observed faulting is superimposed over a prominent antiformal structure, which may itself be a product of the Quaternary deformation that produced the steep normal and reverse faults. Our results suggest that faulting of the Paleozoic bedrock and younger sediments of the northern Mississippi embayment is more pervasive and less localized than previously thought.

CPSGrader: Auto-Grading and Feedback Generation for Cyber-Physical Systems Education

DTIC Science & Technology

2014-12-21

to refer to ⋃ C∈C+ Ω(C,Γ(p)) (and⋃ C∈C− Ω(C,Γ(p))). The rationale behind this choice of ρ is two-fold: 1. To increase coverage of fault detection ... fault mentioned in Section 2.3. The purpose of the test is to detect that at some time instant t0, the robot bumps into the obstacle, then turns about...sampling. 5.6 Investigating Unknown Faults Using Clustering CPSGrader works with a fixed pre-defined library of faults and associated test benches

Polyphase exhumation in the western Qinling Mountains, China: Rapid Early Cretaceous cooling along a lithospheric-scale tear fault and pulsed Cenozoic uplift

PubMed Central

Heberer, Bianca; Anzenbacher, Thomas; Neubauer, Franz; Genser, Johann; Dong, Yunpeng; Dunkl, István

2014-01-01

The western sector of the Qinling–Dabie orogenic belt plays a key role in both Late Jurassic to Early Cretaceous “Yanshanian” intracontinental tectonics and Cenozoic lateral escape triggered by India–Asia collision. The Taibai granite in the northern Qinling Mountains is located at the westernmost tip of a Yanshanian granite belt. It consists of multiple intrusions, constrained by new Late Jurassic and Early Cretaceous U–Pb zircon ages (156 ± 3 Ma and 124 ± 1 Ma). Applying various geochronometers (40Ar/39Ar on hornblende, biotite and K-feldspar, apatite fission-track, apatite [U–Th–Sm]/He) along a vertical profile of the Taibai Mountain refines the cooling and exhumation history. The new age constraints record the prolonged pre-Cenozoic intracontinental deformation as well as the cooling history mostly related to India–Asia collision. We detected rapid cooling for the Taibai granite from ca. 800 to 100 °C during Early Cretaceous (ca. 123 to 100 Ma) followed by a period of slow cooling from ca. 100 Ma to ca. 25 Ma, and pulsed exhumation of the low-relief Cretaceous peneplain during Cenozoic times. We interpret the Early Cretaceous rapid cooling and exhumation as a result from activity along the southern sinistral lithospheric scale tear fault of the recently postulated intracontinental subduction of the Archean/Palaeoproterozoic North China Block beneath the Alashan Block. A Late Oligocene to Early Miocene cooling phase might be triggered either by the lateral motion during India–Asia collision and/or the Pacific subduction zone. Late Miocene intensified cooling is ascribed to uplift of the Tibetan Plateau. PMID:27065503

Reset Tree-Based Optical Fault Detection

PubMed Central

Lee, Dong-Geon; Choi, Dooho; Seo, Jungtaek; Kim, Howon

2013-01-01

In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit's reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool. PMID:23698267

Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

PubMed

Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

2017-05-24

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

Precise tremor source locations and amplitude variations along the lower-crustal central San Andreas Fault

USGS Publications Warehouse

Shelly, David R.; Hardebeck, Jeanne L.

2010-01-01

We precisely locate 88 tremor families along the central San Andreas Fault using a 3D velocity model and numerous P and S wave arrival times estimated from seismogram stacks of up to 400 events per tremor family. Maximum tremor amplitudes vary along the fault by at least a factor of 7, with by far the strongest sources along a 25 km section of the fault southeast of Parkfield. We also identify many weaker tremor families, which have largely escaped prior detection. Together, these sources extend 150 km along the fault, beneath creeping, transitional, and locked sections of the upper crustal fault. Depths are mostly between 18 and 28 km, in the lower crust. Epicenters are concentrated within 3 km of the surface trace, implying a nearly vertical fault. A prominent gap in detectible activity is located directly beneath the region of maximum slip in the 2004 magnitude 6.0 Parkfield earthquake.

Probabilistic evaluation of on-line checks in fault-tolerant multiprocessor systems

NASA Technical Reports Server (NTRS)

Nair, V. S. S.; Hoskote, Yatin V.; Abraham, Jacob A.

1992-01-01

The analysis of fault-tolerant multiprocessor systems that use concurrent error detection (CED) schemes is much more difficult than the analysis of conventional fault-tolerant architectures. Various analytical techniques have been proposed to evaluate CED schemes deterministically. However, these approaches are based on worst-case assumptions related to the failure of system components. Often, the evaluation results do not reflect the actual fault tolerance capabilities of the system. A probabilistic approach to evaluate the fault detecting and locating capabilities of on-line checks in a system is developed. The various probabilities associated with the checking schemes are identified and used in the framework of the matrix-based model. Based on these probabilistic matrices, estimates for the fault tolerance capabilities of various systems are derived analytically.

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

PubMed Central

2018-01-01

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. PMID:29316731

Evaluating the performance of a fault detection and diagnostic system for vapor compression equipment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Breuker, M.S.; Braun, J.E.

This paper presents a detailed evaluation of the performance of a statistical, rule-based fault detection and diagnostic (FDD) technique presented by Rossi and Braun (1997). Steady-state and transient tests were performed on a simple rooftop air conditioner over a range of conditions and fault levels. The steady-state data without faults were used to train models that predict outputs for normal operation. The transient data with faults were used to evaluate FDD performance. The effect of a number of design variables on FDD sensitivity for different faults was evaluated and two prototype systems were specified for more complete evaluation. Good performancemore » was achieved in detecting and diagnosing five faults using only six temperatures (2 input and 4 output) and linear models. The performance improved by about a factor of two when ten measurements (three input and seven output) and higher order models were used. This approach for evaluating and optimizing the performance of the statistical, rule-based FDD technique could be used as a design and evaluation tool when applying this FDD method to other packaged air-conditioning systems. Furthermore, the approach could also be modified to evaluate the performance of other FDD methods.« less

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

NASA Technical Reports Server (NTRS)

Tejada, Arturo

2009-01-01

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

NASA Astrophysics Data System (ADS)

Wang, Pan-Pan; Yu, Qiang; Hu, Yong-Jun; Miao, Chang-Xin

2017-11-01

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Multiple sensor fault diagnosis for dynamic processes.

PubMed

Li, Cheng-Chih; Jeng, Jyh-Cheng

2010-10-01

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

A comparative study of sensor fault diagnosis methods based on observer for ECAS system

NASA Astrophysics Data System (ADS)

Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

2017-03-01

The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis

PubMed Central

Leng, Yonggang; Fan, Shengbo

2018-01-01

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method. PMID:29693577

Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L. (Technical Monitor)

2003-01-01

In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

Satellite-based measurements of surface deformation reveal fluid flow associated with the geological storage of carbon dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasco, D.W.; Rucci, A.; Ferretti, A.

2009-10-15

Interferometric Synthetic Aperture Radar (InSAR), gathered over the In Salah CO{sub 2} storage project in Algeria, provides an early indication that satellite-based geodetic methods can be effective in monitoring the geological storage of carbon dioxide. An injected volume of 3 million tons of carbon dioxide, from one of the first large-scale carbon sequestration efforts, produces a measurable surface displacement of approximately 5 mm/year. Using geophysical inverse techniques we are able to infer flow within the reservoir layer and within a seismically detected fracture/ fault zone intersecting the reservoir. We find that, if we use the best available elastic Earth model,more » the fluid flow need only occur in the vicinity of the reservoir layer. However, flow associated with the injection of the carbon dioxide does appear to extend several kilometers laterally within the reservoir, following the fracture/fault zone.« less

Design and fabrication of prototype system for early warning of impending bearing failure

NASA Technical Reports Server (NTRS)

Meacher, J.; Chen, H. M.

1974-01-01

A test program was conducted with the objective of developing a method and equipment for on-line monitoring of installed ball bearings to detect deterioration or impending failure of the bearings. The program was directed at the spin-axis bearings of a control moment gyro. The bearings were tested at speeds of 6000 and 8000 rpm, thrust loads from 50 to 1000 pounds, with a wide range of lubrication conditions, with and without a simulated fatigue spall implanted in the inner race ball track. It was concluded that a bearing monitor system based on detection and analysis of modulations of a fault indicating bearing resonance frequency can provide a low threshold of sensitivity.

McGee Mountain Shallow (2m) Temperature Survey, Humboldt County, Nevada 2009

DOE Data Explorer

Richard Zehner

2009-01-01

This shapefile contains location and attribute data for a shallow (2 meter) temperature survey conducted by Geothermal Technical Partners, Inc. during late 2008 and early 2009. Temperatures at 2m depth were measured at 192 separate points as outlined by Coolbaugh et al., 2007. The purpose of the survey was to try and detect a shallow thermal anomaly associated with the McGee Mountain geothermal area as discovered by Phillips Petroleum and Earth Power Resources in the late 1970’s. Drilling identified ~120oC temperatures at ~100m depth. This 2-meter survey delineated what was interpreted as a steam-heated fault zone centered along a range front fault in the vicinity of the drilled holes and fumaroles. Coolbaugh, M.F., Sladek, C., Faulds, J.E., Zehner, R.E., and Oppliger, G.L., 2007, Use of rapid temperature measurements at a 2-meter depth to augment deeper temperature gradient drilling: Proceedings, 32nd Workshop on Geothermal Reservoir Engineering, Stanford University, Stanford, CA, Jan. 22-24, 2007, p. 109-116. Zehner, R., Tullar, K., and Rutledge, E., 2012, Effectiveness of 2-Meter and geoprobe shallow temperature surveys in early stage geothermal exploration: Geothermal Resources Council Transactions, v. 36, in press.

Tsunami Early Warning via a Physics-Based Simulation Pipeline

NASA Astrophysics Data System (ADS)

Wilson, J. M.; Rundle, J. B.; Donnellan, A.; Ward, S. N.; Komjathy, A.

2017-12-01

Through independent efforts, physics-based simulations of earthquakes, tsunamis, and atmospheric signatures of these phenomenon have been developed. With the goal of producing tsunami forecasts and early warning tools for at-risk regions, we join these three spheres to create a simulation pipeline. The Virtual Quake simulator can produce thousands of years of synthetic seismicity on large, complex fault geometries, as well as the expected surface displacement in tsunamigenic regions. These displacements are used as initial conditions for tsunami simulators, such as Tsunami Squares, to produce catalogs of potential tsunami scenarios with probabilities. Finally, these tsunami scenarios can act as input for simulations of associated ionospheric total electron content, signals which can be detected by GNSS satellites for purposes of early warning in the event of a real tsunami. We present the most recent developments in this project.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles

PubMed Central

Jeon, Namju; Lee, Hyeongcheol

2016-01-01

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed. PMID:27973431

Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

PubMed Central

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

PubMed

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

NASA Technical Reports Server (NTRS)

Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette

2010-01-01

Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis.

Geometry and evolution of low-angle normal faults (LANF) within a Cenozoic high-angle rift system, Thailand: Implications for sedimentology and the mechanisms of LANF development

NASA Astrophysics Data System (ADS)

Morley, Chris K.

2009-10-01

At least eight examples of large (5-35 km heave), low-angle normal faults (LANFs, 20°-30° dip) occur in the Cenozoic rift basins of Thailand and laterally pass into high-angle extensional fault systems. Three large-displacement LANFs are found in late Oligocene-Miocene onshore rift basins (Suphan Buri, Phitsanulok, and Chiang Mai basins), they have (1) developed contemporaneous with, or after the onset of, high-angle extension, (2) acted as paths for magma and associated fluids, and (3) impacted sedimentation patterns. Displacement on low-angle faults appears to be episodic, marked by onset of lacustrine conditions followed by axial progradation of deltaic systems that infilled the lakes during periods of low or no displacement. The Chiang Mai LANF is a low-angle (15°-25°), high-displacement (15-35 km heave), ESE dipping LANF immediately east of the late early Miocene Doi Inthanon and Doi Suthep metamorphic core complexes. Early Cenozoic transpressional crustal thickening followed by the northward motion of India coupled with Burma relative to east Burma and Thailand (˜40-30 Ma) caused migmatization and gneiss dome uplift in the late Oligocene of the core complex region, followed by LANF activity. LANF displacement lasted 4-6 Ma during the early Miocene and possibly transported a late Oligocene-early Miocene high-angle rift system 35 km east. Other LANFs in Thailand have lower displacements and no associated metamorphic core complexes. The three LANFs were initiated as low-angle faults, not by isostatic rotation of high-angle faults. The low-angle dips appear to follow preexisting low-angle fabrics (thrusts, shear zones, and other low-angle ductile foliations) predominantly developed during Late Paleozoic and early Paleogene episodes of thrusting and folding.

Health Monitoring Survey of Bell 412EP Transmissions

NASA Technical Reports Server (NTRS)

Tucker, Brian E.; Dempsey, Paula J.

2016-01-01

Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified into "healthy" and "faulted" populations based on actual condition and these populations are compared against their CI thresholds to quantify the probability of false alarm and the probability of missed detection. Receiver Operator Characteristic analysis is used to optimize thresholds. Based on the results of the analysis, shortcomings in the classification method are identified for slow-moving CI trends. Recommendations for improving classification using time-dependent receiver-operator characteristic methods are put forth. Finally, lessons learned regarding OEM-operator communication are presented.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

NASA Astrophysics Data System (ADS)

Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

2016-01-01

The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

A method based on multi-sensor data fusion for fault detection of planetary gearboxes.

PubMed

Lei, Yaguo; Lin, Jing; He, Zhengjia; Kong, Detong

2012-01-01

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

Development and evaluation of a fault-tolerant multiprocessor (FTMP) computer. Volume 1: FTMP principles of operation

NASA Technical Reports Server (NTRS)

Smith, T. B., Jr.; Lala, J. H.

1983-01-01

The basic organization of the fault tolerant multiprocessor, (FTMP) is that of a general purpose homogeneous multiprocessor. Three processors operate on a shared system (memory and I/O) bus. Replication and tight synchronization of all elements and hardware voting is employed to detect and correct any single fault. Reconfiguration is then employed to repair a fault. Multiple faults may be tolerated as a sequence of single faults with repair between fault occurrences.

Failure detection and fault management techniques for flush airdata sensing systems

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.; Leondes, Cornelius T.

1992-01-01

Methods based on chi-squared analysis are presented for detecting system and individual-port failures in the high-angle-of-attack flush airdata sensing system on the NASA F-18 High Alpha Research Vehicle. The HI-FADS hardware is introduced, and the aerodynamic model describes measured pressure in terms of dynamic pressure, angle of attack, angle of sideslip, and static pressure. Chi-squared analysis is described in the presentation of the concept for failure detection and fault management which includes nominal, iteration, and fault-management modes. A matrix of pressure orifices arranged in concentric circles on the nose of the aircraft indicate the parameters which are applied to the regression algorithms. The sensing techniques are applied to the F-18 flight data, and two examples are given of the computed angle-of-attack time histories. The failure-detection and fault-management techniques permit the matrix to be multiply redundant, and the chi-squared analysis is shown to be useful in the detection of failures.

Induction motor inter turn fault detection using infrared thermographic analysis

NASA Astrophysics Data System (ADS)

Singh, Gurmeet; Anil Kumar, T. Ch.; Naikan, V. N. A.

2016-07-01

Induction motors are the most commonly used prime movers in industries. These are subjected to various environmental, thermal and load stresses that ultimately reduces the motor efficiency and later leads to failure. Inter turn fault is the second most commonly observed faults in the motors and is considered the most severe. It can lead to the failure of complete phase and can even cause accidents, if left undetected or untreated. This paper proposes an online and non invasive technique that uses infrared thermography, in order to detect the presence of inter turn fault in induction motor drive. Two methods have been proposed that detect the fault and estimate its severity. One method uses transient thermal monitoring during the start of motor and other applies pseudo coloring technique on infrared image of the motor, after it reaches a thermal steady state. The designed template for pseudo-coloring is in acquiescence with the InterNational Electrical Testing Association (NETA) thermographic standard. An index is proposed to assess the severity of the fault present in the motor.

Gas Path On-line Fault Diagnostics Using a Nonlinear Integrated Model for Gas Turbine Engines

NASA Astrophysics Data System (ADS)

Lu, Feng; Huang, Jin-quan; Ji, Chun-sheng; Zhang, Dong-dong; Jiao, Hua-bin

2014-08-01

Gas turbine engine gas path fault diagnosis is closely related technology that assists operators in managing the engine units. However, the performance gradual degradation is inevitable due to the usage, and it result in the model mismatch and then misdiagnosis by the popular model-based approach. In this paper, an on-line integrated architecture based on nonlinear model is developed for gas turbine engine anomaly detection and fault diagnosis over the course of the engine's life. These two engine models have different performance parameter update rate. One is the nonlinear real-time adaptive performance model with the spherical square-root unscented Kalman filter (SSR-UKF) producing performance estimates, and the other is a nonlinear baseline model for the measurement estimates. The fault detection and diagnosis logic is designed to discriminate sensor fault and component fault. This integration architecture is not only aware of long-term engine health degradation but also effective to detect gas path performance anomaly shifts while the engine continues to degrade. Compared to the existing architecture, the proposed approach has its benefit investigated in the experiment and analysis.

Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

PubMed Central

Garcia-Ramirez, Armando G.; Osornio-Rios, Roque A.; Granados-Lieberman, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

2012-01-01

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

Appropriate IMFs associated with cepstrum and envelope analysis for ball-bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Tsao, Wen-Chang; Pan, Min-Chun

2014-03-01

The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.

Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive.

PubMed

Ameid, Tarek; Menacer, Arezki; Talhaoui, Hicham; Azzoug, Youness

2018-05-03

This paper presents a methodology for the broken rotor bars fault detection is considered when the rotor speed varies continuously and the induction machine is controlled by Field-Oriented Control (FOC). The rotor fault detection is obtained by analyzing a several mechanical and electrical quantities (i.e., rotor speed, stator phase current and output signal of the speed regulator) by the Discrete Wavelet Transform (DWT) in variable speed drives. The severity of the fault is obtained by stored energy calculation for active power signal. Hence, it can be a useful solution as fault indicator. The FOC is implemented in order to preserve a good performance speed control; to compensate the broken rotor bars effect in the mechanical speed and to ensure the operation continuity and to investigate the fault effect in the variable speed. The effectiveness of the technique is evaluated in simulation and in a real-time implementation by using Matlab/Simulink with the real-time interface (RTI) based on dSpace 1104 board. Copyright © 2018. Published by Elsevier Ltd.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

PubMed

Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

2012-01-01

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

NASA Astrophysics Data System (ADS)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

BEAT: A Web-Based Boolean Expression Fault-Based Test Case Generation Tool

ERIC Educational Resources Information Center

Chen, T. Y.; Grant, D. D.; Lau, M. F.; Ng, S. P.; Vasa, V. R.

2006-01-01

BEAT is a Web-based system that generates fault-based test cases from Boolean expressions. It is based on the integration of our several fault-based test case selection strategies. The generated test cases are considered to be fault-based, because they are aiming at the detection of particular faults. For example, when the Boolean expression is in…

Investigations into early rift development and geothermal resources in the Pyramid Lake fault zone, Western Nevada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eisses, A.; Kell, A.; Kent, G.

A. K. Eisses, A. M. Kell, G. Kent, N. W. Driscoll, R. E. Karlin, R. L. Baskin, J. N. Louie, S. Pullammanappallil, 2010, Investigations into early rift development and geothermal resources in the Pyramid Lake fault zone, Western Nevada: Abstract T33C-2278 presented at 2010 Fall Meeting, AGU, San Francisco, Calif., 13-17 Dec.

Advanced Information Processing System - Fault detection and error handling

NASA Technical Reports Server (NTRS)

Lala, J. H.

1985-01-01

The Advanced Information Processing System (AIPS) is designed to provide a fault tolerant and damage tolerant data processing architecture for a broad range of aerospace vehicles, including tactical and transport aircraft, and manned and autonomous spacecraft. A proof-of-concept (POC) system is now in the detailed design and fabrication phase. This paper gives an overview of a preliminary fault detection and error handling philosophy in AIPS.

On-line bolt-loosening detection method of key components of running trains using binocular vision

NASA Astrophysics Data System (ADS)

Xie, Yanxia; Sun, Junhua

2017-11-01

Bolt loosening, as one of hidden faults, affects the running quality of trains and even causes serious safety accidents. However, the developed fault detection approaches based on two-dimensional images cannot detect bolt-loosening due to lack of depth information. Therefore, we propose a novel online bolt-loosening detection method using binocular vision. Firstly, the target detection model based on convolutional neural network (CNN) is used to locate the target regions. And then, stereo matching and three-dimensional reconstruction are performed to detect bolt-loosening faults. The experimental results show that the looseness of multiple bolts can be characterized by the method simultaneously. The measurement repeatability and precision are less than 0.03mm, 0.09mm respectively, and its relative error is controlled within 1.09%.

Analysis of the impact of error detection on computer performance

NASA Technical Reports Server (NTRS)

Shin, K. C.; Lee, Y. H.

1983-01-01

Conventionally, reliability analyses either assume that a fault/error is detected immediately following its occurrence, or neglect damages caused by latent errors. Though unrealistic, this assumption was imposed in order to avoid the difficulty of determining the respective probabilities that a fault induces an error and the error is then detected in a random amount of time after its occurrence. As a remedy for this problem a model is proposed to analyze the impact of error detection on computer performance under moderate assumptions. Error latency, the time interval between occurrence and the moment of detection, is used to measure the effectiveness of a detection mechanism. This model is used to: (1) predict the probability of producing an unreliable result, and (2) estimate the loss of computation due to fault and/or error.

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, J. C.

1987-01-01

Specific topics briefly addressed include: the consistent comparison problem in N-version system; analytic models of comparison testing; fault tolerance through data diversity; and the relationship between failures caused by automatically seeded faults.

Infrared thermography based diagnosis of inter-turn fault and cooling system failure in three phase induction motor

NASA Astrophysics Data System (ADS)

Singh, Gurmeet; Naikan, V. N. A.

2017-12-01

Thermography has been widely used as a technique for anomaly detection in induction motors. International Electrical Testing Association (NETA) proposed guidelines for thermographic inspection of electrical systems and rotating equipment. These guidelines help in anomaly detection and estimating its severity. However, it focus only on location of hotspot rather than diagnosing the fault. This paper addresses two such faults i.e. inter-turn fault and failure of cooling system, where both results in increase of stator temperature. Present paper proposes two thermal profile indicators using thermal analysis of IRT images. These indicators are in compliance with NETA standard. These indicators help in correctly diagnosing inter-turn fault and failure of cooling system. The work has been experimentally validated for healthy and with seeded faults scenarios of induction motors.

Geology and neotectonism in the epicentral area of the 2011 M5.8 Mineral, Virginia, earthquake

USGS Publications Warehouse

Burton, William C.; Spears, David B.; Harrison, Richard W.; Evans, Nicholas H.; Schindler, J. Stephen; Counts, Ronald C.

2015-01-01

arc (Ordovician Chopawamsic Formation) to Laurentia, intrusion of a granodiorite pluton (Ordovician Ellisville pluton), and formation of a post-Chopawamsic successor basin (Ordovician Quantico Formation), all accompanied by early Paleozoic regional deformation and metamorphism. Local transpressional faulting and retrograde metamorphism occurred in the late Paleozoic, followed by diabase dike intrusion and possible local normal faulting in the early Mesozoic. The overall goal of the bedrock mapping is to determine what existing geologic structures might have been reactivated during the 2011 seismic event, and surfi cial deposits along the South Anna River are being mapped in order to determine possible neotectonic uplift. In addition to bedrock and surfi cial studies, we have excavated trenches in an area that contains two late Paleozoic faults and represents the updip projection of the causative fault for the 2011 quake. The trenches reveal faulting that has offset surfi cial deposits dated as Quaternary in age, as well as numerous other brittle structures that suggest a geologically recent history of neotectonic activity.

Failure Diagnosis for the Holdup Tank System via ISFA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Huijuan; Bragg-Sitton, Shannon; Smidts, Carol

This paper discusses the use of the integrated system failure analysis (ISFA) technique for fault diagnosis for the holdup tank system. ISFA is a simulation-based, qualitative and integrated approach used to study fault propagation in systems containing both hardware and software subsystems. The holdup tank system consists of a tank containing a fluid whose level is controlled by an inlet valve and an outlet valve. We introduce the component and functional models of the system, quantify the main parameters and simulate possible failure-propagation paths based on the fault propagation approach, ISFA. The results show that most component failures in themore » holdup tank system can be identified clearly and that ISFA is viable as a technique for fault diagnosis. Since ISFA is a qualitative technique that can be used in the very early stages of system design, this case study provides indications that it can be used early to study design aspects that relate to robustness and fault tolerance.« less

Space shuttle main engine fault detection using neural networks

NASA Technical Reports Server (NTRS)

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data.

The Border Ranges fault system in Glacier Bay National Park, Alaska: Evidence for major early Cenozoic dextral strike-slip motion

USGS Publications Warehouse

Smart, K.J.; Pavlis, T.L.; Sisson, V.B.; Roeske, S.M.; Snee, L.W.

1996-01-01

The Border Ranges fault system of southern Alaska, the fundamental break between the arc basement and the forearc accretionary complex, is the boundary between the Peninsular-Alexander-Wrangellia terrane and the Chugach terrane. The fault system separates crystalline rocks of the Alexander terrane from metamorphic rocks of the Chugach terrane in Glacier Bay National Park. Mylonitic rocks in the zone record abundant evidence for dextral strike-slip motion along north-northwest-striking subvertical surfaces. Geochronologic data together with regional correlations of Chugach terrane rocks involved in the deformation constrain this movement between latest Cretaceous and Early Eocene (???50 Ma). These findings are in agreement with studies to the northwest and southeast along the Border Ranges fault system which show dextral strike-slip motion occurring between 58 and 50 Ma. Correlations between Glacier Bay plutons and rocks of similar ages elsewhere along the Border Ranges fault system suggest that as much as 700 km of dextral motion may have been accommodated by this structure. These observations are consistent with oblique convergence of the Kula plate during early Cenozoic and forearc slivering above an ancient subduction zone following late Mesozoic accretion of the Peninsular-Alexander-Wrangellia terrane to North America.

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

The buried active faults in southeastern China as revealed by the relocated background seismicity and fault plane solutions

NASA Astrophysics Data System (ADS)

Zhu, A.; Wang, P.; Liu, F.

2017-12-01

The southeastern China in the mainland corresponds to the south China block, which is characterized by moderate historical seismicity and low stain rate. Most faults are buried under thick Quaternary deposits, so it is difficult to detect and locate them using the routine geological methods. Only a few have been identified to be active in late Quaternary, which leads to relatively high potentially seismic risk to this region due to the unexpected locations of the earthquakes. We performed both hypoDD and tomoDD for the background seismicity from 2000 to 2016 to investigate the buried faults. Some buried active faults are revealed by the relocated seismicity and the velocity structure, no geologically known faults corresponding to them and no surface active evidence ever observed. The geometries of the faults are obtained by analyzing the hypocentral distribution pattern and focal mechanism. The focal mechanism solutions indicate that all the revealed faults are dominated in strike-slip mechanisms, or with some thrust components. While the previous fault investigation and detection results show that most of the Quaternary faults in southeastern China are dominated by normal movement. It suggests that there may exist two fault systems in deep and shallow tectonic regimes. The revealed faults may construct the deep one that act as the seismogenic faults, and the normal faults at shallow cannot generate the destructive earthquakes. The variation in the Curie-point depths agrees well with the structure plane of the revealed active faults, suggesting that the faults may have changed the deep structure.

Earthquake epicenters and fault intersections in central and southern California

NASA Technical Reports Server (NTRS)

Abdel-Gawad, M. (Principal Investigator); Silverstein, J.

1972-01-01

The author has identifed the following significant results. ERTS-1 imagery provided evidence for the existence of short transverse fault segments lodged between faults of the San Andreas system in the Coast Ranges, California. They indicate that an early episode of transverse shear has affected the Coast Ranges prior to the establishment of the present San Andreas fault. The fault has been offset by transverse faults of the Transverse Ranges. It appears feasible to identify from ERTS-1 imagery geomorphic criteria of recent fault movements. Plots of historic earthquakes in the Coast Ranges and western Transverse Ranges show clusters in areas where structures are complicated by interaction of tow active fault systems. A fault lineament apparently not previously mapped was identified in the Uinta Mountains, Utah. Part of the lineament show evidence of recent faulting which corresponds to a moderate earthquake cluster.

The timing of strike-slip shear along the Ranong and Khlong Marui faults, Thailand

NASA Astrophysics Data System (ADS)

Watkinson, Ian; Elders, Chris; Batt, Geoff; Jourdan, Fred; Hall, Robert; McNaughton, Neal J.

2011-09-01

The timing of shear along many important strike-slip faults in Southeast Asia, such as the Ailao Shan-Red River, Mae Ping and Three Pagodas faults, is poorly understood. We present 40Ar/39Ar, U-Pb SHRIMP and microstructural data from the Ranong and Khlong Marui faults of Thailand to show that they experienced a major period of ductile dextral shear during the middle Eocene (48-40 Ma, centered on 44 Ma) which followed two phases of dextral shear along the Ranong Fault, before the Late Cretaceous (>81 Ma) and between the late Paleocene and early Eocene (59-49 Ma). Many of the sheared rocks were part of a pre-kinematic crystalline basement complex, which partially melted and was intruded by Late Cretaceous (81-71 Ma) and early Eocene (48 Ma) tin-bearing granites. Middle Eocene dextral shear at temperatures of ˜300-500°C formed extensive mylonite belts through these rocks and was synchronous with granitoid vein emplacement. Dextral shear along the Ranong and Khlong Marui faults occurred at the same time as sinistral shear along the Mae Ping and Three Pagodas faults of northern Thailand, a result of India-Burma coupling in advance of India-Asia collision. In the late Eocene (<37 Ma) the Ranong and Khlong Marui faults were reactivated as curved sinistral branches of the Mae Ping and Three Pagodas faults, which were accommodating lateral extrusion during India-Asia collision and Himalayan orogenesis.

Late Miocene-Early Pliocene reactivation of the Main Boundary Thrust: Evidence from the seismites in southeastern Kumaun Himalaya, India

NASA Astrophysics Data System (ADS)

Mishra, Anurag; Srivastava, Deepak C.; Shah, Jyoti

2013-05-01

Tectonic history of the Himalaya is punctuated by successive development of the faults that run along the boundaries between different lithotectonic terrains. The Main Boundary Fault, defining the southern limit of the Lesser Himalayan terrain, is tectonically most active. A review of published literature reveals that the nature and age of reactivation events on the Main Boundary Fault is one of the poorly understood aspects of the Himalayan orogen. By systematic outcrop mapping of the seismites, this study identifies a Late Miocene-Early Pliocene reactivation on the Main Boundary Thrust in southeast Kumaun Himalaya. Relatively friable and cohesionless Neogene sedimentary sequences host abundant soft-sediment deformation structures in the vicinity of the Main Boundary Thrust. Among a large variety of structures, deformed cross-beds, liquefaction pockets, slump folds, convolute laminations, sand dykes, mushroom structures, fluid escape structures, flame and load structures and synsedimentary faults are common. The morphological attributes, the structural association and the distribution pattern of the soft-sediment deformation structures with respect to the Main Boundary Fault strongly suggest their development by seismically triggered liquefaction and fluidization. Available magnetostratigraphic age data imply that the seismites were developed during a Late Miocene-Early Pliocene slip on the Main Boundary Thrust. The hypocenter of the main seismic event may lie on the Main Boundary Thrust or to the north of the study area on an unknown fault or the Basal Detachment Thrust.

Simultaneous fault detection and control design for switched systems with two quantized signals.

PubMed

Li, Jian; Park, Ju H; Ye, Dan

2017-01-01

The problem of simultaneous fault detection and control design for switched systems with two quantized signals is presented in this paper. Dynamic quantizers are employed, respectively, before the output is passed to fault detector, and before the control input is transmitted to the switched system. Taking the quantized errors into account, the robust performance for this kind of system is given. Furthermore, sufficient conditions for the existence of fault detector/controller are presented in the framework of linear matrix inequalities, and fault detector/controller gains and the supremum of quantizer range are derived by a convex optimized method. Finally, two illustrative examples demonstrate the effectiveness of the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy logic based on-line fault detection and classification in transmission line.

PubMed

Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam

2016-01-01

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

Transient Faults in Computer Systems

NASA Technical Reports Server (NTRS)

Masson, Gerald M.

1993-01-01

A powerful technique particularly appropriate for the detection of errors caused by transient faults in computer systems was developed. The technique can be implemented in either software or hardware; the research conducted thus far primarily considered software implementations. The error detection technique developed has the distinct advantage of having provably complete coverage of all errors caused by transient faults that affect the output produced by the execution of a program. In other words, the technique does not have to be tuned to a particular error model to enhance error coverage. Also, the correctness of the technique can be formally verified. The technique uses time and software redundancy. The foundation for an effective, low-overhead, software-based certification trail approach to real-time error detection resulting from transient fault phenomena was developed.

Surface morphology of active normal faults in hard rock: Implications for the mechanics of the Asal Rift, Djibouti

NASA Astrophysics Data System (ADS)

Pinzuti, Paul; Mignan, Arnaud; King, Geoffrey C. P.

2010-10-01

Tectonic-stretching models have been previously proposed to explain the process of continental break-up through the example of the Asal Rift, Djibouti, one of the few places where the early stages of seafloor spreading can be observed. In these models, deformation is distributed starting at the base of a shallow seismogenic zone, in which sub-vertical normal faults are responsible for subsidence whereas cracks accommodate extension. Alternative models suggest that extension results from localised magma intrusion, with normal faults accommodating extension and subsidence only above the maximum reach of the magma column. In these magmatic rifting models, or so-called magmatic intrusion models, normal faults have dips of 45-55° and root into dikes. Vertical profiles of normal fault scarps from levelling campaign in the Asal Rift, where normal faults seem sub-vertical at surface level, have been analysed to discuss the creation and evolution of normal faults in massive fractured rocks (basalt lava flows), using mechanical and kinematics concepts. We show that the studied normal fault planes actually have an average dip ranging between 45° and 65° and are characterised by an irregular stepped form. We suggest that these normal fault scarps correspond to sub-vertical en echelon structures, and that, at greater depth, these scarps combine and give birth to dipping normal faults. The results of our analysis are compatible with the magmatic intrusion models instead of tectonic-stretching models. The geometry of faulting between the Fieale volcano and Lake Asal in the Asal Rift can be simply related to the depth of diking, which in turn can be related to magma supply. This new view supports the magmatic intrusion model of early stages of continental breaking.

Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis

DTIC Science & Technology

2014-10-02

Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis M. Samadani1, C. A. Kitio Kwuimy2, and C. Nataraj3...diagnostics of nonlinear systems. A detailed nonlinear math- ematical model of a servo electro-hydraulic system has been used to demonstrate the procedure...Two faults have been considered associated with the servo valve including the in- creased friction between spool and sleeve and the degradation of the

Experiments in fault tolerant software reliability

NASA Technical Reports Server (NTRS)

Mcallister, David F.; Vouk, Mladen A.

1989-01-01

Twenty functionally equivalent programs were built and tested in a multiversion software experiment. Following unit testing, all programs were subjected to an extensive system test. In the process sixty-one distinct faults were identified among the versions. Less than 12 percent of the faults exhibited varying degrees of positive correlation. The common-cause (or similar) faults spanned as many as 14 components. However, a majority of these faults were trivial, and easily detected by proper unit and/or system testing. Only two of the seven similar faults were difficult faults, and both were caused by specification ambiguities. One of these faults exhibited variable identical-and-wrong response span, i.e. response span which varied with the testing conditions and input data. Techniques that could have been used to avoid the faults are discussed. For example, it was determined that back-to-back testing of 2-tuples could have been used to eliminate about 90 percent of the faults. In addition, four of the seven similar faults could have been detected by using back-to-back testing of 5-tuples. It is believed that most, if not all, similar faults could have been avoided had the specifications been written using more formal notation, the unit testing phase was subject to more stringent standards and controls, and better tools for measuring the quality and adequacy of the test data (e.g. coverage) were used.

A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram

NASA Astrophysics Data System (ADS)

Tian, Xiange; Xi Gu, James; Rehab, Ibrahim; Abdalla, Gaballa M.; Gu, Fengshou; Ball, A. D.

2018-02-01

Envelope analysis is a widely used method for rolling element bearing fault detection. To obtain high detection accuracy, it is critical to determine an optimal frequency narrowband for the envelope demodulation. However, many of the schemes which are used for the narrowband selection, such as the Kurtogram, can produce poor detection results because they are sensitive to random noise and aperiodic impulses which normally occur in practical applications. To achieve the purposes of denoising and frequency band optimisation, this paper proposes a novel modulation signal bispectrum (MSB) based robust detector for bearing fault detection. Because of its inherent noise suppression capability, the MSB allows effective suppression of both stationary random noise and discrete aperiodic noise. The high magnitude features that result from the use of the MSB also enhance the modulation effects of a bearing fault and can be used to provide optimal frequency bands for fault detection. The Kurtogram is generally accepted as a powerful means of selecting the most appropriate frequency band for envelope analysis, and as such it has been used as the benchmark comparator for performance evaluation in this paper. Both simulated and experimental data analysis results show that the proposed method produces more accurate and robust detection results than Kurtogram based approaches for common bearing faults under a range of representative scenarios.

SCADA alarms processing for wind turbine component failure detection

NASA Astrophysics Data System (ADS)

Gonzalez, E.; Reder, M.; Melero, J. J.

2016-09-01

Wind turbine failure and downtime can often compromise the profitability of a wind farm due to their high impact on the operation and maintenance (O&M) costs. Early detection of failures can facilitate the changeover from corrective maintenance towards a predictive approach. This paper presents a cost-effective methodology to combine various alarm analysis techniques, using data from the Supervisory Control and Data Acquisition (SCADA) system, in order to detect component failures. The approach categorises the alarms according to a reviewed taxonomy, turning overwhelming data into valuable information to assess component status. Then, different alarms analysis techniques are applied for two purposes: the evaluation of the SCADA alarm system capability to detect failures, and the investigation of the relation between components faults being followed by failure occurrences in others. Various case studies are presented and discussed. The study highlights the relationship between faulty behaviour in different components and between failures and adverse environmental conditions.

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

PubMed Central

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-01-01

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

PubMed

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-02-02

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.

A single dynamic observer-based module for design of simultaneous fault detection, isolation and tracking control scheme

NASA Astrophysics Data System (ADS)

Davoodi, M.; Meskin, N.; Khorasani, K.

2018-03-01

The problem of simultaneous fault detection, isolation and tracking (SFDIT) control design for linear systems subject to both bounded energy and bounded peak disturbances is considered in this work. A dynamic observer is proposed and implemented by using the H∞/H-/L1 formulation of the SFDIT problem. A single dynamic observer module is designed that generates the residuals as well as the control signals. The objective of the SFDIT module is to ensure that simultaneously the effects of disturbances and control signals on the residual signals are minimised (in order to accomplish the fault detection goal) subject to the constraint that the transfer matrix from the faults to the residuals is equal to a pre-assigned diagonal transfer matrix (in order to accomplish the fault isolation goal), while the effects of disturbances, reference inputs and faults on the specified control outputs are minimised (in order to accomplish the fault-tolerant and tracking control goals). A set of linear matrix inequality (LMI) feasibility conditions are derived to ensure solvability of the problem. In order to illustrate and demonstrate the effectiveness of our proposed design methodology, the developed and proposed schemes are applied to an autonomous unmanned underwater vehicle (AUV).

Fault detection for piecewise affine systems with application to ship propulsion systems.

PubMed

Yang, Ying; Linlin, Li; Ding, Steven X; Qiu, Jianbin; Peng, Kaixiang

2017-09-09

In this paper, the design approach of non-synchronized diagnostic observer-based fault detection (FD) systems is investigated for piecewise affine processes via continuous piecewise Lyapunov functions. Considering that the dynamics of piecewise affine systems in different regions can be considerably different, the weighting matrices are used to weight the residual of each region, so as to optimize the fault detectability. A numerical example and a case study on a ship propulsion system are presented in the end to demonstrate the effectiveness of the proposed results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Critical fault patterns determination in fault-tolerant computer systems

NASA Technical Reports Server (NTRS)

Mccluskey, E. J.; Losq, J.

1978-01-01

The method proposed tries to enumerate all the critical fault-patterns (successive occurrences of failures) without analyzing every single possible fault. The conditions for the system to be operating in a given mode can be expressed in terms of the static states. Thus, one can find all the system states that correspond to a given critical mode of operation. The next step consists in analyzing the fault-detection mechanisms, the diagnosis algorithm and the process of switch control. From them, one can find all the possible system configurations that can result from a failure occurrence. Thus, one can list all the characteristics, with respect to detection, diagnosis, and switch control, that failures must have to constitute critical fault-patterns. Such an enumeration of the critical fault-patterns can be directly used to evaluate the overall system tolerance to failures. Present research is focused on how to efficiently make use of these system-level characteristics to enumerate all the failures that verify these characteristics.

Fault Diagnosis for Centre Wear Fault of Roll Grinder Based on a Resonance Demodulation Scheme

NASA Astrophysics Data System (ADS)

Wang, Liming; Shao, Yimin; Yin, Lei; Yuan, Yilin; Liu, Jing

2017-05-01

Roll grinder is one of the important parts in the rolling machinery, and the grinding precision of roll surface has direct influence on the surface quality of steel strip. However, during the grinding process, the centre bears the gravity of the roll and alternating stress. Therefore, wear or spalling faults are easily observed on the centre, which will lead to an anomalous vibration of the roll grinder. In this study, a resonance demodulation scheme is proposed to detect the centre wear fault of roll grinder. Firstly, fast kurtogram method is employed to help select the sub-band filter parameters for optimal resonance demodulation. Further, the envelope spectrum are derived based on the filtered signal. Finally, two health indicators are designed to conduct the fault diagnosis for centre wear fault. The proposed scheme is assessed by analysing experimental data from a roll grinder of twenty-high rolling mill. The results show that the proposed scheme can effectively detect the centre wear fault of the roll grinder.

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

NASA Technical Reports Server (NTRS)

Sun, Joy Z.; Josh, Suresh M.

2009-01-01

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.

Built-in-test by signature inspection (bitsi)

DOEpatents

Bergeson, Gary C.; Morneau, Richard A.

1991-01-01

A system and method for fault detection for electronic circuits. A stimulus generator sends a signal to the input of the circuit under test. Signature inspection logic compares the resultant signal from test nodes on the circuit to an expected signal. If the signals do not match, the signature inspection logic sends a signal to the control logic for indication of fault detection in the circuit. A data input multiplexer between the test nodes of the circuit under test and the signature inspection logic can provide for identification of the specific node at fault by the signature inspection logic. Control logic responsive to the signature inspection logic conveys information about fault detection for use in determining the condition of the circuit. When used in conjunction with a system test controller, the built-in test by signature inspection system and method can be used to poll a plurality of circuits automatically and continuous for faults and record the results of such polling in the system test controller.

Fault detection and accommodation testing on an F100 engine in an F-15 airplane

NASA Technical Reports Server (NTRS)

Myers, L. P.; Baer-Riedhart, J. L.; Maxwell, M. D.

1985-01-01

The fault detection and accommodation (FDA) methodology for digital engine-control systems may range from simple comparisons of redundant parameters to the more complex and sophisticated observer models of the entire engine system. Evaluations of the various FDA schemes are done using analytical methods, simulation, and limited-altitude-facility testing. Flight testing of the FDA logic has been minimal because of the difficulty of inducing realistic faults in flight. A flight program was conducted to evaluate the fault detection and accommodation capability of a digital electronic engine control in an F-15 aircraft. The objective of the flight program was to induce selected faults and evaluate the resulting actions of the digital engine controller. Comparisons were made between the flight results and predictions. Several anomalies were found in flight and during the ground test. Simulation results showed that the inducement of dual pressure failures was not feasible since the FDA logic was not designed to accommodate these types of failures.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

PubMed Central

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-01-01

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. PMID:27649171

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Detection of Frictional Heating on Faults Using Raman Spectra of Carbonaceous Material

NASA Astrophysics Data System (ADS)

Ito, K.; Ujiie, K.; Kagi, H.

2017-12-01

Raman spectra of carbonaceous material (RSCM) have been used as geothermometer in sedimentary and metamorphic rocks. However, it remains poorly understood whether RSCM are useful for detecting past frictional heating on faults. To detect increased heating during seismic slip, we examine the thrust fault in the Jurassic accretionary complex, central Japan. The thrust fault zone includes 10 cm-thick cataclasite and a few mm-thick dark layer. The cataclasite is characterized by fragments of black and gray chert in the black carbonaceous mudstone matrix. The dark layer is marked by intensely cracked gray chert fragments in the dark matrix of carbonaceous mudstone composition, which bounds the fractured gray chert above from the cataclasite below. The RSCM are analyzed for carbonaceous material in the cataclasite, dark layer, and host rock <10 mm from cataclasite and dark layer boundaries. The result indicates that there is no increased carbonization in the cataclasite. In contrast, the dark layer and part of host rocks <2 mm from the dark layer boundaries show prominent increase in carbonization. The absent of increased carbonization in the cataclasite could be attributed to insufficient frictional heating associated with distributed shear and/or faulting at low slip rates. The dark layer exhibits the appearance of fault and injection veins, and the dark layer boundaries are irregularly embayed or intensely cracked; these features have been characteristically observed in pseudotachylytes. Therefore, the increased carbonization in the dark layer is likely resulted from increased heating during earthquake faulting. The intensely cracked fragments in the dark layer and cracked wall rocks may reflect thermal fracturing in chert, which is caused by heat conduction from the molten zone. We suggest that RSCM are useful for the detection of increased heating on faults, particularly when the temperature is high enough for frictional melting and thermal fracturing.

Sensor fault-tolerant control for gear-shifting engaging process of automated manual transmission

NASA Astrophysics Data System (ADS)

Li, Liang; He, Kai; Wang, Xiangyu; Liu, Yahui

2018-01-01

Angular displacement sensor on the actuator of automated manual transmission (AMT) is sensitive to fault, and the sensor fault will disturb its normal control, which affects the entire gear-shifting process of AMT and results in awful riding comfort. In order to solve this problem, this paper proposes a method of fault-tolerant control for AMT gear-shifting engaging process. By using the measured current of actuator motor and angular displacement of actuator, the gear-shifting engaging load torque table is built and updated before the occurrence of the sensor fault. Meanwhile, residual between estimated and measured angular displacements is used to detect the sensor fault. Once the residual exceeds a determined fault threshold, the sensor fault is detected. Then, switch control is triggered, and the current observer and load torque table estimates an actual gear-shifting position to replace the measured one to continue controlling the gear-shifting process. Numerical and experiment tests are carried out to evaluate the reliability and feasibility of proposed methods, and the results show that the performance of estimation and control is satisfactory.

Fault Management Metrics

NASA Technical Reports Server (NTRS)

Johnson, Stephen B.; Ghoshal, Sudipto; Haste, Deepak; Moore, Craig

2017-01-01

This paper describes the theory and considerations in the application of metrics to measure the effectiveness of fault management. Fault management refers here to the operational aspect of system health management, and as such is considered as a meta-control loop that operates to preserve or maximize the system's ability to achieve its goals in the face of current or prospective failure. As a suite of control loops, the metrics to estimate and measure the effectiveness of fault management are similar to those of classical control loops in being divided into two major classes: state estimation, and state control. State estimation metrics can be classified into lower-level subdivisions for detection coverage, detection effectiveness, fault isolation and fault identification (diagnostics), and failure prognosis. State control metrics can be classified into response determination effectiveness and response effectiveness. These metrics are applied to each and every fault management control loop in the system, for each failure to which they apply, and probabilistically summed to determine the effectiveness of these fault management control loops to preserve the relevant system goals that they are intended to protect.

Structural analysis of the Tabaco anticline, Cerrejón open-cast coal mine, Colombia, South America

NASA Astrophysics Data System (ADS)

Cardozo, Néstor; Montes, Camilo; Marín, Dora; Gutierrez, Iván; Palencia, Alejandro

2016-06-01

The Tabaco anticline is a 15 km long, south plunging, east-vergent anticline in northern Colombia, close to the transpressional collisional margin between the Caribbean and South American plates. In the Cerrejón open-cast coal mine, systematic mapping of coal seams in the middle to upper Paleocene Cerrejón Formation has yielded an exceptional dataset consisting of 10 horizontal slices (sea level to 90 m elevation, regularly spaced at 10 m intervals) through the anticline. Coal seams and fault traces in these slices are used to construct a 3D model of the anticline. This 3D model shows tighter folds within lower coal seams, NW-vergent thrusts and related folds on the gentler western limb, and strike-slip faults on the steeper eastern limb. Fault slip-tendency analysis is used to infer that these two faulting styles resulted from two different stress fields: an earlier one consistent with thrusting and uplift of the Perijá range, and a later one consistent with strike-slip faulting (Oca, Ranchería and Samán faults). Our preferred interpretation is that the anticline developed its eastern vergence during the early stages (late Paleocene-early Eocene) of tilting of the Santa Marta massif. Later NW-vergent thrusting on the western limb (early to middle Eocene) was related to western propagation of the Perijá thrust system. These results contribute to the understanding of the structural evolution of the area. They are also a good example of the complex interplay between detachment folding, thrusting, and strike-slip faulting during the growth of a km-size fold in a transpressive setting.

Structural setting and kinematics of Nubian fault system, SE Western Desert, Egypt: An example of multi-reactivated intraplate strike-slip faults

NASA Astrophysics Data System (ADS)

Sakran, Shawky; Said, Said Mohamed

2018-02-01

Detailed surface geological mapping and subsurface seismic interpretation have been integrated to unravel the structural style and kinematic history of the Nubian Fault System (NFS). The NFS consists of several E-W Principal Deformation Zones (PDZs) (e.g. Kalabsha fault). Each PDZ is defined by spectacular E-W, WNW and ENE dextral strike-slip faults, NNE sinistral strike-slip faults, NE to ENE folds, and NNW normal faults. Each fault zone has typical self-similar strike-slip architecture comprising multi-scale fault segments. Several multi-scale uplifts and basins were developed at the step-over zones between parallel strike-slip fault segments as a result of local extension or contraction. The NNE faults consist of right-stepping sinistral strike-slip fault segments (e.g. Sin El Kiddab fault). The NNE sinistral faults extend for long distances ranging from 30 to 100 kms and cut one or two E-W PDZs. Two nearly perpendicular strike-slip tectonic regimes are recognized in the NFS; an inactive E-W Late Cretaceous - Early Cenozoic dextral transpression and an active NNE sinistral shear.

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis.

PubMed

Talhaoui, Hicham; Menacer, Arezki; Kessal, Abdelhalim; Kechida, Ridha

2014-09-01

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Sensor Allocation for Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

2004-01-01

Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

FAULT PROPAGATION AND EFFECTS ANALYSIS FOR DESIGNING AN ONLINE MONITORING SYSTEM FOR THE SECONDARY LOOP OF A NUCLEAR POWER PLANT PART OF A HYBRID ENERGY SYSTEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Huijuan; Diao, Xiaoxu; Li, Boyuan

This paper studies the propagation and effects of faults of critical components that pertain to the secondary loop of a nuclear power plant found in Nuclear Hybrid Energy Systems (NHES). This information is used to design an on-line monitoring (OLM) system which is capable of detecting and forecasting faults that are likely to occur during NHES operation. In this research, the causes, features, and effects of possible faults are investigated by simulating the propagation of faults in the secondary loop. The simulation is accomplished by using the Integrated System Failure Analysis (ISFA). ISFA is used for analyzing hardware and softwaremore » faults during the conceptual design phase. In this paper, the models of system components required by ISFA are initially constructed. Then, the fault propagation analysis is implemented, which is conducted under the bounds set by acceptance criteria derived from the design of an OLM system. The result of the fault simulation is utilized to build a database for fault detection and diagnosis, provide preventive measures, and propose an optimization plan for the OLM system.« less

Real-time diagnostics for a reusable rocket engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Merrill, W.; Duyar, A.

1992-01-01

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Lessons Learned on Implementing Fault Detection, Isolation, and Recovery (FDIR) in a Ground Launch Environment

NASA Technical Reports Server (NTRS)

Ferell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Goerz, Jesse; Brown, Barbara

2010-01-01

This paper's main purpose is to detail issues and lessons learned regarding designing, integrating, and implementing Fault Detection Isolation and Recovery (FDIR) for Constellation Exploration Program (CxP) Ground Operations at Kennedy Space Center (KSC).

Orion GN&C Fault Management System Verification: Scope And Methodology

NASA Technical Reports Server (NTRS)

Brown, Denise; Weiler, David; Flanary, Ronald

2016-01-01

In order to ensure long-term ability to meet mission goals and to provide for the safety of the public, ground personnel, and any crew members, nearly all spacecraft include a fault management (FM) system. For a manned vehicle such as Orion, the safety of the crew is of paramount importance. The goal of the Orion Guidance, Navigation and Control (GN&C) fault management system is to detect, isolate, and respond to faults before they can result in harm to the human crew or loss of the spacecraft. Verification of fault management/fault protection capability is challenging due to the large number of possible faults in a complex spacecraft, the inherent unpredictability of faults, the complexity of interactions among the various spacecraft components, and the inability to easily quantify human reactions to failure scenarios. The Orion GN&C Fault Detection, Isolation, and Recovery (FDIR) team has developed a methodology for bounding the scope of FM system verification while ensuring sufficient coverage of the failure space and providing high confidence that the fault management system meets all safety requirements. The methodology utilizes a swarm search algorithm to identify failure cases that can result in catastrophic loss of the crew or the vehicle and rare event sequential Monte Carlo to verify safety and FDIR performance requirements.

Fault finder

DOEpatents

Bunch, Richard H.

1986-01-01

A fault finder for locating faults along a high voltage electrical transmission line. Real time monitoring of background noise and improved filtering of input signals is used to identify the occurrence of a fault. A fault is detected at both a master and remote unit spaced along the line. A master clock synchronizes operation of a similar clock at the remote unit. Both units include modulator and demodulator circuits for transmission of clock signals and data. All data is received at the master unit for processing to determine an accurate fault distance calculation.

On-board fault management for autonomous spacecraft

NASA Technical Reports Server (NTRS)

Fesq, Lorraine M.; Stephan, Amy; Doyle, Susan C.; Martin, Eric; Sellers, Suzanne

1991-01-01

The dynamic nature of the Cargo Transfer Vehicle's (CTV) mission and the high level of autonomy required mandate a complete fault management system capable of operating under uncertain conditions. Such a fault management system must take into account the current mission phase and the environment (including the target vehicle), as well as the CTV's state of health. This level of capability is beyond the scope of current on-board fault management systems. This presentation will discuss work in progress at TRW to apply artificial intelligence to the problem of on-board fault management. The goal of this work is to develop fault management systems. This presentation will discuss work in progress at TRW to apply artificial intelligence to the problem of on-board fault management. The goal of this work is to develop fault management systems that can meet the needs of spacecraft that have long-range autonomy requirements. We have implemented a model-based approach to fault detection and isolation that does not require explicit characterization of failures prior to launch. It is thus able to detect failures that were not considered in the failure and effects analysis. We have applied this technique to several different subsystems and tested our approach against both simulations and an electrical power system hardware testbed. We present findings from simulation and hardware tests which demonstrate the ability of our model-based system to detect and isolate failures, and describe our work in porting the Ada version of this system to a flight-qualified processor. We also discuss current research aimed at expanding our system to monitor the entire spacecraft.

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

PubMed Central

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate. PMID:22163789

Simple random sampling-based probe station selection for fault detection in wireless sensor networks.

PubMed

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate.

Late oligocene and miocene faulting and sedimentation, and evolution of the southern Rio Grande rift, New Mexico, USA

NASA Astrophysics Data System (ADS)

Mack, Greg H.; Seager, William R.; Kieling, John

1994-08-01

The distribution of nonmarine lithofacies, paleocurrents, and provenance data are used to define the evolution of late Oligocene and Miocene basins and complementary uplifts in the southern Rio Grande rift in the vicinity of Hatch, New Mexico, USA. The late Oligocene-middle Miocene Hayner Ranch Formation, which consists of a maximum of 1000 m of alluvial-fan, alluvial-flat, and lacustrine-carbonate lithofacies, was deposited in a narrow (12 km), northwest-trending, northeast-tilted half graben, whose footwall was the Caballo Mountains block. Stratigraphic separation on the border faults of the Caballo Mountains block was approximately 1615 m. An additional 854 m of stratigraphic separation along the Caballo Mountains border faults occurred during deposition of the middle-late Miocene Rincon Valley Formation, which is composed of up to 610 m of alluvial-fan, alluvial-flat, braided-fluvial, and gypsiferous playa lithofacies. Two new, north-trending fault blocks (Sierra de las Uvas and Dona Ana Mountains) and complementary west-northwest-tilted half graben also developed during Rincon Valley time, with approximately 549 m of stratigraphic separation along the border fault of the Sierra de las Uvas block. In latest Miocene and early Pliocene time, following deposition of the Rincon Valley Formation, movement continued along the border faults of the Caballo Mountains, Dona Ana Mountains, and Sierra de las Uvas blocks, and large parts of the Hayner Ranch and Rincon Valley basins were segmented into smaller fault blocks and basins by movement along new, largely north-trending faults. Analysis of the Hayner Ranch and Rincon Valley Formations, along with previous studies of the early Oligocene Bell Top Formation and late Pliocene-early Pleistocene Camp Rice Formation, indicate that the traditional two-stage model for development of the southern Rio Grande rift should be abandoned in favor of at least four episodes of block faulting beginning 35 Ma ago. With the exception of two northwest-trending border faults of the Caballo Mountains block that may be reactivated along Eocene compressional structures, the majority of border faults and complementary basins throughout the history of the southern Rio Grande rift were north-trending, which challenges the conventional idea of a clockwise change in stress through time.

An online outlier identification and removal scheme for improving fault detection performance.

PubMed

Ferdowsi, Hasan; Jagannathan, Sarangapani; Zawodniok, Maciej

2014-05-01

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.

Flight experience with a fail-operational digital fly-by-wire control system

NASA Technical Reports Server (NTRS)

Brown, S. R.; Szalai, K. J.

1977-01-01

The NASA Dryden Flight Research Center is flight testing a triply redundant digital fly-by-wire (DFBW) control system installed in an F-8 aircraft. The full-time, full-authority system performs three-axis flight control computations, including stability and command augmentation, autopilot functions, failure detection and isolation, and self-test functions. Advanced control law experiments include an active flap mode for ride smoothing and maneuver drag reduction. This paper discusses research being conducted on computer synchronization, fault detection, fault isolation, and recovery from transient faults. The F-8 DFBW system has demonstrated immunity from nuisance fault declarations while quickly identifying truly faulty components.

Regional investigations of tectonic and igneous geology, Iran, Pakistan, and Turkey

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. An extension of the trace of the Chaman-Nushki fault was detected and delineated for 42 km, as was the Ornach-Nal fault for 170 km. Two structural intersections responsible for restricted movements in particular segments of the Chaman-Nushki fault were detected and interpreted. The newest and youngest fault named the Quetta-Mustung-Surab system was delineated for 580 km. The igneous complex of the Lasbela area was interpreted and differentiation was made between ultramafic complex, mafic complex, and basaltic lava flows. One oblong feature was also found which was interpreted as a porphyritic basalt plug.

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1996-01-01

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

NASA Astrophysics Data System (ADS)

Le, Huy Xuan; Matunaga, Saburo

2014-12-01

This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with real-time updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.

Along strike behavior of the Tizi n' Firest fault during the Lower Jurassic rifting (Central High Atlas Carbonate basin, Morocco)

NASA Astrophysics Data System (ADS)

Sarih, S.; Quiquerez, A.; Allemand, P.; Garcia, J. P.; El Hariri, K.

2018-03-01

The purpose of this study is to document the along-strike early syn-rift history of the Lower Jurassic Carbonate basin of the Central High Atlas (Morocco) by combining sedimentological observations and high-resolution biostratigraphy. Six sections, each from the Sinemurian to the Upper Pliensbachian, were investigated along a 75 km-long transect at the hanging wall of a major fault of the Lower Jurassic Basin (i.e. the Tizi n' Firest fault). Depositional geometries of the early syn-rift deposits were reconstructed from the correlation between eight main timelines dated by biochronological markers for a time span covering about 6 Ma. Depocentre migration was examined and accommodation rates were calculated at the sub-zone timescale to discuss the along-strike-fault behavior of the Lower Jurassic basin formation. The early stages of extension are marked by contrasted along-strike variations in depositional geometry thickness, depocentre migration and accommodation rates, leading to the growth of three independent sub-basins (i.e. western, central, and eastern), ranging in size from 30 to 50 km, and displaying three contrasted tectono-sedimentary histories. Our results suggest that, during the early rifting phase, tectonic activity was not a continuous and progressive process evolving towards a rift climax stage, but rather a series of acceleration periods that alternated with periods of much reduced activity. The length of active fault segments is estimated at about 15-20 km, with a lifespan of a few ammonite sub-zones (> 2-3 Ma).

Structural superposition in fault systems bounding Santa Clara Valley, California

USGS Publications Warehouse

Graymer, Russell W.; Stanley, Richard G.; Ponce, David A.; Jachens, Robert C.; Simpson, Robert W.; Wentworth, Carl M.

2015-01-01

Santa Clara Valley is bounded on the southwest and northeast by active strike-slip and reverse-oblique faults of the San Andreas fault system. On both sides of the valley, these faults are superposed on older normal and/or right-lateral normal oblique faults. The older faults comprised early components of the San Andreas fault system as it formed in the wake of the northward passage of the Mendocino Triple Junction. On the east side of the valley, the great majority of fault displacement was accommodated by the older faults, which were almost entirely abandoned when the presently active faults became active after ca. 2.5 Ma. On the west side of the valley, the older faults were abandoned earlier, before ca. 8 Ma and probably accumulated only a small amount, if any, of the total right-lateral offset accommodated by the fault zone as a whole. Apparent contradictions in observations of fault offset and the relation of the gravity field to the distribution of dense rocks at the surface are explained by recognition of superposed structures in the Santa Clara Valley region.

Online Fault Detection of Permanent Magnet Demagnetization for IPMSMs by Nonsingular Fast Terminal-Sliding-Mode Observer

PubMed Central

Zhao, Kai-Hui; Chen, Te-Fang; Zhang, Chang-Fan; He, Jing; Huang, Gang

2014-01-01

To prevent irreversible demagnetization of a permanent magnet (PM) for interior permanent magnet synchronous motors (IPMSMs) by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO) is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LAB platform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance. PMID:25490582

Online fault detection of permanent magnet demagnetization for IPMSMs by nonsingular fast terminal-sliding-mode observer.

PubMed

Zhao, Kai-Hui; Chen, Te-Fang; Zhang, Chang-Fan; He, Jing; Huang, Gang

2014-12-05

To prevent irreversible demagnetization of a permanent magnet (PM) for interior permanent magnet synchronous motors (IPMSMs) by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO) is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LAB platform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance.

System and method of detecting cavitation in pumps

DOEpatents

Lu, Bin; Sharma, Santosh Kumar; Yan, Ting; Dimino, Steven A.

2017-10-03

A system and method for detecting cavitation in pumps for fixed and variable supply frequency applications is disclosed. The system includes a controller having a processor programmed to repeatedly receive real-time operating current data from a motor driving a pump, generate a current frequency spectrum from the current data, and analyze current data within a pair of signature frequency bands of the current frequency spectrum. The processor is further programmed to repeatedly determine fault signatures as a function of the current data within the pair of signature frequency bands, repeatedly determine fault indices based on the fault signatures and a dynamic reference signature, compare the fault indices to a reference index, and identify a cavitation condition in a pump based on a comparison between the reference index and a current fault index.

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Ferrell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Brown, Barbara

2010-01-01

The purpose of this paper is to present the model development process used to create a Functional Fault Model (FFM) of a liquid hydrogen (L H2) system that will be used for realtime fault isolation in a Fault Detection, Isolation and Recover (FDIR) system. The paper explains th e steps in the model development process and the data products required at each step, including examples of how the steps were performed fo r the LH2 system. It also shows the relationship between the FDIR req uirements and steps in the model development process. The paper concl udes with a description of a demonstration of the LH2 model developed using the process and future steps for integrating the model in a live operational environment.

Fuzzy model-based observers for fault detection in CSTR.

PubMed

Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

2015-11-01

Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

Saturating time-delay transformer for overcurrent protection. [Patent application

DOEpatents

Praeg, W.F.

1975-12-18

Electrical loads connected to dc supplies are protected from damage by overcurrent in the case of a load fault by connecting in series with the load a saturating transformer that detects a load fault and limits the fault current to a safe level for a period long enough to correct the fault or else disconnect the power supply.

Saturating time-delay transformer for overcurrent protection

DOEpatents

Praeg, Walter F.

1977-01-01

Electrical loads connected to d-c supplies are protected from damage by overcurrent in the case of a load fault by connecting in series with the load a saturating transformer that detects a load fault and limits the fault current to a safe level for a period long enough to correct the fault or else disconnect the power supply.

Back Propagation Artificial Neural Network and Its Application in Fault Detection of Condenser Failure in Thermo Plant

NASA Astrophysics Data System (ADS)

Ismail, Firas B.; Thiruchelvam, Vinesh

2013-06-01

Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system"

Development of Monitoring and Diagnostic Methods for Robots Used In Remediation of Waste Sites - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, M.

2000-04-01

This project is the first evaluation of model-based diagnostics to hydraulic robot systems. A greater understanding of fault detection for hydraulic robots has been gained, and a new theoretical fault detection model developed and evaluated.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

Detecting and isolating abrupt changes in linear switching systems

NASA Astrophysics Data System (ADS)

Nazari, Sohail; Zhao, Qing; Huang, Biao

2015-04-01

In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a 'lifted' linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models' parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.

Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering

NASA Astrophysics Data System (ADS)

Rai, Akhand; Upadhyay, S. H.

2017-09-01

Bearing is the most critical component in rotating machinery since it is more susceptible to failure. The monitoring of degradation in bearings becomes of great concern for averting the sudden machinery breakdown. In this study, a novel method for bearing performance degradation assessment (PDA) based on an amalgamation of empirical mode decomposition (EMD) and k-medoids clustering is encouraged. The fault features are extracted from the bearing signals using the EMD process. The extracted features are then subjected to k-medoids based clustering for obtaining the normal state and failure state cluster centres. A confidence value (CV) curve based on dissimilarity of the test data object to the normal state is obtained and employed as the degradation indicator for assessing the health of bearings. The proposed outlook is applied on the vibration signals collected in run-to-failure tests of bearings to assess its effectiveness in bearing PDA. To validate the superiority of the suggested approach, it is compared with commonly used time-domain features RMS and kurtosis, well-known fault diagnosis method envelope analysis (EA) and existing PDA classifiers i.e. self-organizing maps (SOM) and Fuzzy c-means (FCM). The results demonstrate that the recommended method outperforms the time-domain features, SOM and FCM based PDA in detecting the early stage degradation more precisely. Moreover, EA can be used as an accompanying method to confirm the early stage defect detected by the proposed bearing PDA approach. The study shows the potential application of k-medoids clustering as an effective tool for PDA of bearings.

Distributed bearing fault diagnosis based on vibration analysis

NASA Astrophysics Data System (ADS)

Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

2016-01-01

Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Real-time fault diagnosis for propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet

1991-01-01

Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.

Reconnaissance study of late quaternary faulting along cerro GoDen fault zone, western Puerto Rico

USGS Publications Warehouse

Mann, P.; Prentice, C.S.; Hippolyte, J.-C.; Grindlay, N.R.; Abrams, L.J.; Lao-Davila, D.

2005-01-01

The Cerro GoDen fault zone is associated with a curvilinear, continuous, and prominent topographic lineament in western Puerto Rico. The fault varies in strike from northwest to west. In its westernmost section, the fault is ???500 m south of an abrupt, curvilinear mountain front separating the 270- to 361-m-high La CaDena De San Francisco range from the Rio A??asco alluvial valley. The Quaternary fault of the A??asco Valley is in alignment with the bedrock fault mapped by D. McIntyre (1971) in the Central La Plata quadrangle sheet east of A??asco Valley. Previous workers have postulated that the Cerro GoDen fault zone continues southeast from the A??asco Valley and merges with the Great Southern Puerto Rico fault zone of south-central Puerto Rico. West of the A??asco Valley, the fault continues offshore into the Mona Passage (Caribbean Sea) where it is characterized by offsets of seafloor sediments estimated to be of late Quaternary age. Using both 1:18,500 scale air photographs taken in 1936 and 1:40,000 scale photographs taken by the U.S. Department of Agriculture in 1986, we iDentified geomorphic features suggestive of Quaternary fault movement in the A??asco Valley, including aligned and Deflected drainages, apparently offset terrace risers, and mountain-facing scarps. Many of these features suggest right-lateral displacement. Mapping of Paleogene bedrock units in the uplifted La CaDena range adjacent to the Cerro GoDen fault zone reveals the main tectonic events that have culminated in late Quaternary normal-oblique displacement across the Cerro GoDen fault. Cretaceous to Eocene rocks of the La CaDena range exhibit large folds with wavelengths of several kms. The orientation of folds and analysis of fault striations within the folds indicate that the folds formed by northeast-southwest shorTening in present-day geographic coordinates. The age of Deformation is well constrained as late Eocene-early Oligocene by an angular unconformity separating folDed, Deep-marine middle Eocene rocks from transgressive, shallow-marine rocks of middle-upper Oligocene age. Rocks of middle Oligocene-early Pliocene age above unconformity are gently folDed about the roughly east-west-trending Puerto Rico-Virgin Islands arch, which is well expressed in the geomorphology of western Puerto Rico. Arching appears ongoing because onshore and offshore late Quaternary oblique-slip faults closely parallel the complexly Deformed crest of the arch and appear to be related to exTensional strains focused in the crest of the arch. We estimate ???4 km of vertical throw on the Cerro GoDen fault based on the position of the carbonate cap north of the fault in the La CaDena De San Francisco and its position south of the fault inferred from seismic reflection data in Mayaguez Bay. Based on these observations, our interpretation of the kinematics and history of the Cerro GoDen fault zone incluDes two major phases of motion: (1) Eocene northeast-southwest shorTening possibly accompanied by left-lateral shearing as Determined by previous workers on the Great Southern Puerto Rico fault zone; and (2) post-early Pliocene regional arching of Puerto Rico accompanied by normal offset and right-lateral shear along faults flanking the crest of the arch. The second phase of Deformation accompanied east-west opening of the Mona rift and is inferred to continue to the present day. ?? 2005 Geological Society of America.

Optimal Sensor Location Design for Reliable Fault Detection in Presence of False Alarms

PubMed Central

Yang, Fan; Xiao, Deyun; Shah, Sirish L.

2009-01-01

To improve fault detection reliability, sensor location should be designed according to an optimization criterion with constraints imposed by issues of detectability and identifiability. Reliability requires the minimization of undetectability and false alarm probability due to random factors on sensor readings, which is not only related with sensor readings but also affected by fault propagation. This paper introduces the reliability criteria expression based on the missed/false alarm probability of each sensor and system topology or connectivity derived from the directed graph. The algorithm for the optimization problem is presented as a heuristic procedure. Finally, a boiler system is illustrated using the proposed method. PMID:22291524

Fault detection and diagnosis in an industrial fed-batch cell culture process.

PubMed

Gunther, Jon C; Conner, Jeremy S; Seborg, Dale E

2007-01-01

A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of fault detection and diagnosis. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process faults.

Fault detection in reciprocating compressor valves under varying load conditions

NASA Astrophysics Data System (ADS)

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

2016-03-01

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage

NASA Technical Reports Server (NTRS)

Lewicki, David G.; Dempsey, Paula J.; Heath, Gregory F.; Shanthakumaran, Perumal

2009-01-01

A study was performed to evaluate fault detection effectiveness as applied to gear tooth pitting fatigue damage. Vibration and oil-debris monitoring (ODM) data were gathered from 24 sets of spur pinion and face gears run during a previous endurance evaluation study. Three common condition indicators (RMS, FM4, and NA4) were deduced from the time-averaged vibration data and used with the ODM to evaluate their performance for gear fault detection. The NA4 parameter showed to be a very good condition indicator for the detection of gear tooth surface pitting failures. The FM4 and RMS parameters performed average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant amount of debris from all the gear tooth pitting fatigue failures. Excluding outliers, the average cumulative mass at the end of a test was 40 mg.

DIFFERENTIAL FAULT SENSING CIRCUIT

DOEpatents

Roberts, J.H.

1961-09-01

A differential fault sensing circuit is designed for detecting arcing in high-voltage vacuum tubes arranged in parallel. A circuit is provided which senses differences in voltages appearing between corresponding elements likely to fault. Sensitivity of the circuit is adjusted to some level above which arcing will cause detectable differences in voltage. For particular corresponding elements, a group of pulse transformers are connected in parallel with diodes connected across the secondaries thereof so that only voltage excursions are transmitted to a thyratron which is biased to the sensitivity level mentioned.

Simultaneous Sensor and Process Fault Diagnostics for Propellant Feed System

NASA Technical Reports Server (NTRS)

Cao, J.; Kwan, C.; Figueroa, F.; Xu, R.

2006-01-01

The main objective of this research is to extract fault features from sensor faults and process faults by using advanced fault detection and isolation (FDI) algorithms. A tank system that has some common characteristics to a NASA testbed at Stennis Space Center was used to verify our proposed algorithms. First, a generic tank system was modeled. Second, a mathematical model suitable for FDI has been derived for the tank system. Third, a new and general FDI procedure has been designed to distinguish process faults and sensor faults. Extensive simulations clearly demonstrated the advantages of the new design.

Taking apart the Big Pine fault: Redefining a major structural feature in southern California

USGS Publications Warehouse

Onderdonk, N.W.; Minor, S.A.; Kellogg, K.S.

2005-01-01

New mapping along the Big Pine fault trend in southern California indicates that this structural alignment is actually three separate faults, which exhibit different geometries, slip histories, and senses of offset since Miocene time. The easternmost fault, along the north side of Lockwood Valley, exhibits left-lateral reverse Quaternary displacement but was a north dipping normal fault in late Oligocene to early Miocene time. The eastern Big Pine fault that bounds the southern edge of the Cuyama Badlands is a south dipping reverse fault that is continuous with the San Guillermo fault. The western segment of the Big Pine fault trend is a north dipping thrust fault continuous with the Pine Mountain fault and delineates the northern boundary of the rotated western Transverse Ranges terrane. This redefinition of the Big Pine fault differs greatly from the previous interpretation and significantly alters regional tectonic models and seismic risk estimates. The outcome of this study also demonstrates that basic geologic mapping is still needed to support the development of geologic models. Copyright 2005 by the American Geophysical Union.

Evidence for a Battle Mountain-Eureka crustal fault zone, north-central Nevada, and its relation to Neoproterozoic-Early Paleozoic continental breakup

USGS Publications Warehouse

Grauch, V.J.S.; Rodriguez, B.D.; Bankey, V.; Wooden, J.L.

2003-01-01

Combined evidence from gravity, radiogenic isotope, and magnetotelluric (MT) data indicates a crustal fault zone that coincides with the northwest-trending Battle Mountain-Eureka (BME) mineral trend in north-central Nevada, USA. The BME crustal fault zone likely originated during Neoproterozoic-Early Paleozoic rifting of the continent and had a large influence on subsequent tectonic events, such as emplacement of allochthons and episodic deformation, magmatism, and mineralization throughout the Phanerozoic. MT models show the fault zone is about 10 km wide, 130-km long, and extends from 1 to 5 km below the surface to deep crustal levels. Isotope data and gravity models imply the fault zone separates crust of fundamentally different character. Geophysical evidence for such a long-lived structure, likely inherited from continental breakup, defies conventional wisdom that structures this old have been destroyed by Cenozoic extensional processes. Moreover, the coincidence with the alignment of mineral deposits supports the assertion by many economic geologists that these alignments are indicators of buried regional structures.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

PubMed

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-09-16

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

PubMed Central

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-01-01

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. PMID:28926953

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2005-01-01

In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.

Automatic detection of electric power troubles (AI application)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint

1987-01-01

The design goals for the Automatic Detection of Electric Power Troubles (ADEPT) were to enhance Fault Diagnosis Techniques in a very efficient way. ADEPT system was designed in two modes of operation: (1) Real time fault isolation, and (2) a local simulator which simulates the models theoretically.

Statistical fault diagnosis of wind turbine drivetrain applied to a 5MW floating wind turbine

NASA Astrophysics Data System (ADS)

Ghane, Mahdi; Nejad, Amir R.; Blanke, Mogens; Gao, Zhen; Moan, Torgeir

2016-09-01

Deployment of large scale wind turbine parks, in particular offshore, requires well organized operation and maintenance strategies to make it as competitive as the classical electric power stations. It is important to ensure systems are safe, profitable, and cost-effective. In this regards, the ability to detect, isolate, estimate, and prognose faults plays an important role. One of the critical wind turbine components is the gearbox. Failures in the gearbox are costly both due to the cost of the gearbox itself and also due to high repair downtime. In order to detect faults as fast as possible to prevent them to develop into failure, statistical change detection is used in this paper. The Cumulative Sum Method (CUSUM) is employed to detect possible defects in the downwind main bearing. A high fidelity gearbox model on a 5-MW spar-type wind turbine is used to generate data for fault-free and faulty conditions of the bearing at the rated wind speed and the associated wave condition. Acceleration measurements are utilized to find residuals used to indirectly detect damages in the bearing. Residuals are found to be nonGaussian, following a t-distribution with multivariable characteristic parameters. The results in this paper show how the diagnostic scheme can detect change with desired false alarm and detection probabilities.

Fault detection and identification in missile system guidance and control: a filtering approach

NASA Astrophysics Data System (ADS)

Padgett, Mary Lou; Evers, Johnny; Karplus, Walter J.

1996-03-01

Real-world applications of computational intelligence can enhance the fault detection and identification capabilities of a missile guidance and control system. A simulation of a bank-to- turn missile demonstrates that actuator failure may cause the missile to roll and miss the target. Failure of one fin actuator can be detected using a filter and depicting the filter output as fuzzy numbers. The properties and limitations of artificial neural networks fed by these fuzzy numbers are explored. A suite of networks is constructed to (1) detect a fault and (2) determine which fin (if any) failed. Both the zero order moment term and the fin rate term show changes during actuator failure. Simulations address the following questions: (1) How bad does the actuator failure have to be for detection to occur, (2) How bad does the actuator failure have to be for fault detection and isolation to occur, (3) are both zero order moment and fine rate terms needed. A suite of target trajectories are simulated, and properties and limitations of the approach reported. In some cases, detection of the failed actuator occurs within 0.1 second, and isolation of the failure occurs 0.1 after that. Suggestions for further research are offered.

Distribution, facies, ages, and proposed tectonic associations of regionally metamorphosed rocks in east- and south-central Alaska

USGS Publications Warehouse

Dusel-Bacon, Cynthia; Csejtey, Bela; Foster, Helen L.; Doyle, Elizabeth O.; Nokleberg, Warren J.; Plafker, George

1993-01-01

Most of the exposed bedrock in east- and south-central Alaska has been regionally metamorphosed and deformed during Mesozoic and early Cenozoic time. All the regionally metamorphosed rocks are assigned to metamorphic-facies units on the basis of their temperature and pressure conditions and metamorphic age. North of the McKinley and Denali faults, the crystalline rocks of the Yukon- Tanana upland and central Alaska Range compose a sequence of dynamothermally metamorphosed Paleozoic and older(?) metasedimentary rocks and metamorphosed products of a Devonian and Mississippian continental-margin magmatic arc. This sequence was extensively intruded by postmetamorphic mid-Cretaceous and younger granitoids. Many metamorphic-unit boundaries in the Yukon-Tanana upland are low-angle faults that juxtapose units of differing metamorphic grade, which indicates that metamorphism predated final emplacement of the fault-bounded units. In some places, the relation of metamorphic grade across a fault is best explained by contractional faulting; in other places, it is suggestive of extensional faulting.Near the United States-Canadian border in the central Yukon- Tanana upland, metamorphism, plutonism, and thrusting occurred during a latest Triassic and Early Jurassic event that presumably resulted from the accretion of a terrane that had affinities to the Stikinia terrane onto the continental margin of North America. Elsewhere in the Yukon-Tanana upland, metamorphic rocks give predominantly late Early Cretaceous isotopic ages. These ages are interpreted to date either the timing of a subsequent Early Cretaceous episode of crustal thickening and metamorphism or, assuming that these other areas were also originally heated during the latest Triassic to Early Jurassic and remained buried, the timing of their uplift and cooling. This uplift and cooling may have resulted from extension.South of the McKinley and Denali faults and north of the Border Ranges fault system, medium-grade metamorphism across much of the southern Peninsular and Wrangellia terranes was early to synkinematic with the intrusion of tonalitic and granodioritic plutons of primarily Early and Middle Jurassic age in the Peninsular terrane and Late Jurassic age in the Wrangellia terrane. Areas metamorphosed during the Jurassic episode that crop out near the Border Ranges fault system were subsequently retrograded and deformed in Cretaceous and early Tertiary time during accretion of younger units to the south. North of the Jurassic metamorphic and plutonic complex, low-grade metamorphism affected the rest of the Wrangellia terrane sometime during Jurassic and (or) Cretaceous time.North of the Wrangellia terrane and immediately south of the McKinley and Denali faults, flyschoid rocks, which were deposited within a basin that separated the Wrangellia terrane from the western margin of North America, form a northeastward-tapering wedge. Within the western half of the wedge, flysch and structurally interleaved tectonic fragments were highly deformed and weakly metamorphosed; much of the metamorphism and deformation probably occurred sometime during mid- to Late Cretaceous time. In the eastern half of the wedge, flyschoid rocks form an intermediate-pressure Barrovian sequence (Maclaren metamorphic belt). Metamorphism of the Maclaren metamorphic belt was synkinematic with the Late Cretaceous to earliest Tertiary intrusion of foliated plutons of intermediate composition. Isotopic data suggest metamorphism extended into the early Tertiary and was accompanied by rapid uplift and cooling. Low- to medium-grade metamorphism throughout the wedge was probably associated with the accretion of the outboard Wrangellia terrane, as has been proposed for the Maclaren metamorphic belt.South of the Border Ranges fault system lie variably metamorphosed sequences of oceanic rocks that comprise the successively accreted Chugach, Yakutat, Ghost Rocks, and Prince William terranes. The Chugach terrane consists of three successively accreted sequences of differing metamorphic histories. Metamorphism in all the sequences was associated with north-directed underthrusting beneath either the combined Peninsular-Wrangellia terrane or the older and inner parts of the Chugach terrane. These sequences, from innermost to outermost are: (1) intermediate- to highpressure, transitional greenschist- to blueschist-facies metabasalt and metasedimentary rocks that were metamorphosed during the Early and Middle Jurassic; (2) prehnite-pumpellyite-facies melange that was metamorphosed sometime during the Jurassic and Cretaceous; and (3) low-pressure prehnite-pumpellyite- or greenschist- facies flysch and metavolcanic rocks that were initially metamorphosed during latest Cretaceous to early Tertiary time and, in the eastern Chugach Mountains, were subsequently overprinted by low-pressure amphibolite-facies metamorphism that accompanied widespread intrusion during Eocene time. A similar low-pressure-facies series also developed within melange and flysch of the Yakutat terrane; these rocks are also intruded by Eocene plutons and are correlated with similar rocks of the Chugach terrane.Seaward of the Chugach terrane are the strongly deformed but weakly metamorphosed (prehnite-pumpellyite-facies) deep-sea metasedimentary rocks and oceanic metavolcanic rocks of the Ghost Rocks and Prince William terranes. Metamorphism and deformation occurred during underthrusting of these terranes beneath the Chugach terrane in early Tertiary time and predated, perhaps by very little, intrusion by early Tertiary granitoids.

Automated visual inspection of brake shoe wear

NASA Astrophysics Data System (ADS)

Lu, Shengfang; Liu, Zhen; Nan, Guo; Zhang, Guangjun

2015-10-01

With the rapid development of high-speed railway, the automated fault inspection is necessary to ensure train's operation safety. Visual technology is paid more attention in trouble detection and maintenance. For a linear CCD camera, Image alignment is the first step in fault detection. To increase the speed of image processing, an improved scale invariant feature transform (SIFT) method is presented. The image is divided into multiple levels of different resolution. Then, we do not stop to extract the feature from the lowest resolution to the highest level until we get sufficient SIFT key points. At that level, the image is registered and aligned quickly. In the stage of inspection, we devote our efforts to finding the trouble of brake shoe, which is one of the key components in brake system on electrical multiple units train (EMU). Its pre-warning on wear limitation is very important in fault detection. In this paper, we propose an automatic inspection approach to detect the fault of brake shoe. Firstly, we use multi-resolution pyramid template matching technology to fast locate the brake shoe. Then, we employ Hough transform to detect the circles of bolts in brake region. Due to the rigid characteristic of structure, we can identify whether the brake shoe has a fault. The experiments demonstrate that the way we propose has a good performance, and can meet the need of practical applications.

Earthquakes: Risk, Detection, Warning, and Research

DTIC Science & Technology

2010-01-14

which affect taller , multi-story buildings. Ground motion that affects shorter buildings of a few stories, called short-period seismic waves, is...places in a single fault, or jump between connected faults. Earthquakes that occur along the Sierra Madre fault in southern California, for example

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Model-Based Diagnostics for Propellant Loading Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Foygel, Michael; Smelyanskiy, Vadim N.

2011-01-01

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles

PubMed Central

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-01-01

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists. PMID:27548183

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles.

PubMed

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-08-19

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

Real-Time Fault Classification for Plasma Processes

PubMed Central

Yang, Ryan; Chen, Rongshun

2011-01-01

Plasma process tools, which usually cost several millions of US dollars, are often used in the semiconductor fabrication etching process. If the plasma process is halted due to some process fault, the productivity will be reduced and the cost will increase. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection is required in a plasma reactor. The classification of fault events can help the users to quickly identify fault processes, and thus can save downtime of the plasma tool. In this work, optical emission spectroscopy (OES) is employed as the metrology sensor for in-situ process monitoring. Splitting into twelve different match rates by spectrum bands, the matching rate indicator in our previous work (Yang, R.; Chen, R.S. Sensors 2010, 10, 5703–5723) is used to detect the fault process. Based on the match data, a real-time classification of plasma faults is achieved by a novel method, developed in this study. Experiments were conducted to validate the novel fault classification. From the experimental results, we may conclude that the proposed method is feasible inasmuch that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success. PMID:22164001

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, J. C.

1986-01-01

Multiversion or N-version programming was proposed as a method of providing fault tolerance in software. The approach requires the separate, independent preparation of multiple versions of a piece of software for some application. Specific topics addressed are: failure probabilities in N-version systems, consistent comparison in N-version systems, descriptions of the faults found in the Knight and Leveson experiment, analytic models of comparison testing, characteristics of the input regions that trigger faults, fault tolerance through data diversity, and the relationship between failures caused by automatically seeded faults.

Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection

NASA Astrophysics Data System (ADS)

Xue, Song; Howard, Ian

2018-02-01

This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planetary gearbox faults detection. The traditional approach for condition monitoring of the planetary gear uses a stationary transducer mounted on the ring gear casing to measure all the vibration data when the planet gears pass by with the rotation of the carrier arm. However, the time variant vibration transfer paths between the stationary transducer and the rotating planet gear modulate the resultant vibration spectra and make it complex. Torsional vibration signals are theoretically free from this modulation effect and therefore, it is expected to be much easier and more effective to diagnose planetary gear faults using the fault diagnostic information extracted from the torsional vibration. In this paper, a 20 degree of freedom planetary gear lumped-parameter model was developed to obtain the gear dynamic response. In the model, the gear mesh stiffness variations are the main internal vibration generation mechanism and the finite element models were developed for calculation of the sun-planet and ring-planet gear mesh stiffnesses. Gear faults on different components were created in the finite element models to calculate the resultant gear mesh stiffnesses, which were incorporated into the planetary gear model later on to obtain the faulted vibration signal. Some advanced signal processing techniques were utilized to analyses the fault diagnostic results from the torsional vibration. It was found that the planetary gear torsional vibration not only successfully detected the gear fault, but also had the potential to indicate the location of the gear fault. As a result, the planetary gear torsional vibration can be considered an effective alternative approach for planetary gear condition monitoring.

Voltage Based Detection Method for High Impedance Fault in a Distribution System

NASA Astrophysics Data System (ADS)

Thomas, Mini Shaji; Bhaskar, Namrata; Prakash, Anupama

2016-09-01

High-impedance faults (HIFs) on distribution feeders cannot be detected by conventional protection schemes, as HIFs are characterized by their low fault current level and waveform distortion due to the nonlinearity of the ground return path. This paper proposes a method to identify the HIFs in distribution system and isolate the faulty section, to reduce downtime. This method is based on voltage measurements along the distribution feeder and utilizes the sequence components of the voltages. Three models of high impedance faults have been considered and source side and load side breaking of the conductor have been studied in this work to capture a wide range of scenarios. The effect of neutral grounding of the source side transformer is also accounted in this study. The results show that the algorithm detects the HIFs accurately and rapidly. Thus, the faulty section can be isolated and service can be restored to the rest of the consumers.

An evaluation of a real-time fault diagnosis expert system for aircraft applications

NASA Technical Reports Server (NTRS)

Schutte, Paul C.; Abbott, Kathy H.; Palmer, Michael T.; Ricks, Wendell R.

1987-01-01

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Detecting Taiwan's Shanchiao Active Fault Using AMT and Gravity Methods

NASA Astrophysics Data System (ADS)

Liu, H.-C.; Yang, C.-H.

2009-04-01

Taiwan's Shanchiao normal fault runs in a northeast-southwest direction and is located on the western edge of the Taipei Basin in northern Taiwan. The overburden of the fault is late Quaternary sediment with a thickness of approximately a few tenth of a meter to several hundred meters. No detailed studies of the western side of the Shanchiao fault are available. As Taiwan is located on the Neotectonic Belt in the western Pacific, detecting active faults near the Taipei metropolitan area will provide necessary information for further disaster prevention. It is the responsibility of geologists and geophysicists in Taiwan to perform this task. Examination of the resistivity and density contrasts of subsurface layers permits a mapping of the Shanchiao fault and the deformed Tertiary strata of the Taipei Basin. The audio-frequency magnetotelluric (AMT) method and gravity method were chosen for this study. Significant resistivity and gravity anomalies were observed in the suspected fault zone. The interpretation reveals a good correlation between the features of the Shanchiao fault and resistivity and density distribution at depth. In this observation, AMT and gravity methods provides a viable means for mapping the Shanchiao fault position and studying its features associated with the subsidence of the western side of the Taipei Basin. This study indicates the AMT and gravity methods' considerable potential for accurately mapping an active fault.

Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing

NASA Astrophysics Data System (ADS)

Deng, Feiyue; Yang, Shaopu; Tang, Guiji; Hao, Rujiang; Zhang, Mingliang

2017-04-01

Wheel bearings are essential mechanical components of trains, and fault detection of the wheel bearing is of great significant to avoid economic loss and casualty effectively. However, considering the operating conditions, detection and extraction of the fault features hidden in the heavy noise of the vibration signal have become a challenging task. Therefore, a novel method called adaptive multi-scale AVG-Hat morphology filter (MF) is proposed to solve it. The morphology AVG-Hat operator not only can suppress the interference of the strong background noise greatly, but also enhance the ability of extracting fault features. The improved envelope spectrum sparsity (IESS), as a new evaluation index, is proposed to select the optimal filtering signal processed by the multi-scale AVG-Hat MF. It can present a comprehensive evaluation about the intensity of fault impulse to the background noise. The weighted coefficients of the different scale structural elements (SEs) in the multi-scale MF are adaptively determined by the particle swarm optimization (PSO) algorithm. The effectiveness of the method is validated by analyzing the real wheel bearing fault vibration signal (e.g. outer race fault, inner race fault and rolling element fault). The results show that the proposed method could improve the performance in the extraction of fault features effectively compared with the multi-scale combined morphological filter (CMF) and multi-scale morphology gradient filter (MGF) methods.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

Dynamic Fault Detection Chassis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mize, Jeffery J

2007-01-01

Abstract The high frequency switching megawatt-class High Voltage Converter Modulator (HVCM) developed by Los Alamos National Laboratory for the Oak Ridge National Laboratory's Spallation Neutron Source (SNS) is now in operation. One of the major problems with the modulator systems is shoot-thru conditions that can occur in a IGBTs H-bridge topology resulting in large fault currents and device failure in a few microseconds. The Dynamic Fault Detection Chassis (DFDC) is a fault monitoring system; it monitors transformer flux saturation using a window comparator and dV/dt events on the cathode voltage caused by any abnormality such as capacitor breakdown, transformer primarymore » turns shorts, or dielectric breakdown between the transformer primary and secondary. If faults are detected, the DFDC will inhibit the IGBT gate drives and shut the system down, significantly reducing the possibility of a shoot-thru condition or other equipment damaging events. In this paper, we will present system integration considerations, performance characteristics of the DFDC, and discuss its ability to significantly reduce costly down time for the entire facility.« less

Experimental Fault Diagnosis in Systems Containing Finite Elements of Plate of Kirchoff by Using State Observers Methodology

NASA Astrophysics Data System (ADS)

Alegre, D. M.; Koroishi, E. H.; Melo, G. P.

2015-07-01

This paper presents a methodology for detection and localization of faults by using state observers. State Observers can rebuild the states not measured or values from points of difficult access in the system. So faults can be detected in these points without the knowledge of its measures, and can be track by the reconstructions of their states. In this paper this methodology will be applied in a system which represents a simplified model of a vehicle. In this model the chassis of the car was represented by a flat plate, which was divided in finite elements of plate (plate of Kirchoff), in addition, was considered the car suspension (springs and dampers). A test rig was built and the developed methodology was used to detect and locate faults on this system. In analyses done, the idea is to use a system with a specific fault, and then use the state observers to locate it, checking on a quantitative variation of the parameter of the system which caused this crash. For the computational simulations the software MATLAB was used.

76 FR 58424 - Transmission Relay Loadability Reliability Standard

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-21

... Protection Systems 2. Protective relays are devices that detect and initiate the removal of faults [[Page... protective relay detects a fault on an element of the system under its protection, it sends a signal to an... distribution providers to set load-responsive phase protection relays according to specific criteria to ensure...

Fault detection monitor circuit provides ''self-heal capability'' in electronic modules - A concept

NASA Technical Reports Server (NTRS)

Kennedy, J. J.

1970-01-01

Self-checking technique detects defective solid state modules used in electronic test and checkout instrumentation. A ten bit register provides failure monitor and indication for 1023 comparator circuits, and the automatic fault-isolation capability permits the electronic subsystems to be repaired by replacing the defective module.

The structures, stratigraphy and evolution of the Gulf of Corinth rift, Greece

NASA Astrophysics Data System (ADS)

Taylor, Brian; Weiss, Jonathan R.; Goodliffe, Andrew M.; Sachpazi, Maria; Laigle, Mireille; Hirn, Alfred

2011-06-01

A multichannel seismic and bathymetry survey of the central and eastern Gulf of Corinth (GoC), Greece, reveals the offshore fault geometry, seismic stratigraphy and basin evolution of one of Earths most active continental rift systems. Active, right-stepping, en-echelon, north-dipping border faults trend ESE along the southern Gulf margin, significantly overlapping along strike. The basement offsets of three (Akrata-Derveni, Sithas and Xylocastro) are linked. The faults are biplanar to listric: typically intermediate angle (˜35° in the centre and 45-48° in the east) near the surface but decreasing in dip and/or intersecting a low- or shallow-angle (15-20° in the centre and 19-30° in the east) curvi-planar reflector in the basement. Major S-dipping border faults were active along the northern margin of the central Gulf early in the rift history, and remain active in the western Gulf and in the subsidiary Gulf of Lechaio, but unlike the southern border faults, are without major footwall uplift. Much of the eastern rift has a classic half-graben architecture whereas the central rift has a more symmetric w- or u-shape. The narrower and shallower western Gulf that transects the >40-km-thick crust of the Hellenides is associated with a wider distribution of overlapping high-angle normal faults that were formerly active on the Peloponnesus Peninsula. The easternmost sector includes the subsidiary Gulfs of Lechaio and Alkyonides, with major faults and basement structures trending NE, E-W and NW. The basement faults that control the rift architecture formed early in the rift history, with little evidence (other than the Vrachonisida fault along the northern margin) in the marine data for plan view evolution by subsequent fault linkage. Several have maximum offsets near one end. Crestal collapse graben formed where the hanging wall has pulled off the steeper onto the shallower downdip segment of the Derveni Fault. The dominant strikes of the Corinth rift faults gradually rotate from 090-120° in the basement and early rift to 090-100° in the latest rift, reflecting a ˜10° rotation of the opening direction to the 005° presently measured by GPS. The sediments include a (locally >1.5-km-) thick, early-rift section, and a late-rift section (also locally >1.5-km-thick) that we subdivide into three sequences and correlate with seven 100-ka glacio-eustatic cycles. The Gulf depocentre has deepened through time (currently >700 mbsl) as subsidence has outpaced sedimentation. We measure the minimum total horizontal extension across the central and eastern Gulf as varying along strike between 4 and 10 km, and estimate full values of 6-11 km. The rift evolution is strongly influenced by the inherited basement fabric. The regional NNW structural fabric of the Hellenic nappes changes orientation to ESE in the Parnassos terrane, facilitating the focused north-south extension observed offshore there. The basement-penetrating faults lose seismic reflectivity above the 4-14-km-deep seismogenic zone. Multiple generations and dips of normal faults, some cross-cutting, accommodate extension beneath the GoC, including low-angle (15-20°) interfaces in the basement nappes. The thermally cool forearc setting and cross-orogen structures unaccompanied by magmatism make this rift a poor analogue and unlikely precursor for metamorphic core complex formation.

Detection of High-impedance Arcing Faults in Radial Distribution DC Systems

NASA Technical Reports Server (NTRS)

Gonzalez, Marcelo C.; Button, Robert M.

2003-01-01

High voltage, low current arcing faults in DC power systems have been researched at the NASA Glenn Research Center in order to develop a method for detecting these 'hidden faults', in-situ, before damage to cables and components from localized heating can occur. A simple arc generator was built and high-speed and low-speed monitoring of the voltage and current waveforms, respectively, has shown that these high impedance faults produce a significant increase in high frequency content in the DC bus voltage and low frequency content in the DC system current. Based on these observations, an algorithm was developed using a high-speed data acquisition system that was able to accurately detect high impedance arcing events induced in a single-line system based on the frequency content of the DC bus voltage or the system current. Next, a multi-line, radial distribution system was researched to see if the arc location could be determined through the voltage information when multiple 'detectors' are present in the system. It was shown that a small, passive LC filter was sufficient to reliably isolate the fault to a single line in a multi-line distribution system. Of course, no modification is necessary if only the current information is used to locate the arc. However, data shows that it might be necessary to monitor both the system current and bus voltage to improve the chances of detecting and locating high impedance arcing faults

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

NASA Astrophysics Data System (ADS)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

Insulation detection of electric vehicle batteries

NASA Astrophysics Data System (ADS)

Dai, Qiqi; Zhu, Zhongwen; Huang, Denggao; Du, Mingxing; Wei, Kexin

2018-06-01

In this paper, an electric vehicle insulation detection method with single side switching fixed resistance is designed, and the hardware and software design of the system are given. The experiment proves that the insulation detection system can detect the insulation resistance in a wide range of resistance values, and accurately report the fault level. This system can effectively monitor the insulation fault between the car body and the high voltage line and avoid the passengers from being injured.

Application of fault factor method to fault detection and diagnosis for space shuttle main engine

NASA Astrophysics Data System (ADS)

Cha, Jihyoung; Ha, Chulsu; Ko, Sangho; Koo, Jaye

2016-09-01

This paper deals with an application of the multiple linear regression algorithm to fault detection and diagnosis for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, fault factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each fault and the levels of severity can be obtained from the estimated fault factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.

Neural networks and fault probability evaluation for diagnosis issues.

PubMed

Kourd, Yahia; Lefebvre, Dimitri; Guersi, Noureddine

2014-01-01

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.

PubMed

Liu, Junxiu; Harkin, Jim; Maguire, Liam P; McDaid, Liam J; Wade, John J

2018-04-01

Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

Structural evolution of the Mount Wall region in the Hamersley province, Western Australia and its control on hydrothermal alteration and formation of high-grade iron deposits

NASA Astrophysics Data System (ADS)

Dalstra, Hilke J.

2014-10-01

The discovery of two relatively small but high-grade iron ore deposits near Mt Wall, an intensely faulted part of the southwestern Hamersley province provides unique insights into the structural control on ore formation in this region. The deposits have many geological features typical of the high grade microplaty hematite group which also contains the much larger Mt Tom Price, Paraburdoo and Mt Whaleback deposits. The deposits are structurally controlled along early normal faults and contain abundant microplaty hematite and martite, and are largely confined to the Dales Gorge member of the Brockman Iron Formation. In addition to the microplaty hematite-martite ore, there are martite-goethite ores and rare magnetite-goethite or magnetite-hematite ores. Below the modern weathering surface, hydrothermally altered zones in wallrock BIF from the Lower Dales Gorge member contain magnetite, hematite and carbonate/talc bearing mineral assemblages. A staged ore genesis model involving early extension and fluid circulation along normal faults, hypogene silica leaching and carbonate alteration, followed by deep meteoric oxidation with microplaty hematite formation and finally weathering can explain most features of the Mt Wall deposits. The role of deformation was to provide pathways for mineralising fluids and initiate the seed points for the mineralised systems. High grade iron in the Wellthandalthaluna deposit is situated between the NW to NNW trending Boolgeeda Creek fault and a synthetic joining splay, the Northern fault. Both are high angle normal faults and formed during early extension in this part of the province. Faults are characterised by localised small scale deformation and brecciation, deep carbonate alteration and oxidation. Recent weathering has penetrated deeply into the fault zones, converting the carbonate-rich assemblages into goethite. Mineralisation in the Arochar deposit is situated in the overlap or relay zone between two segments of the Mt Wall fault zone, a moderately to steeply southerly dipping normal fault system which at Arochar is intruded by dolerite dykes. At both locations, the ore controlling faults are offset by later NW trending dextral and normal faults. Fault relay zones or fault splay zones were likely zones of increased permeability and fluid flow during fault development or reactivation and may also have been important in initiating mineralisation in larger deposits such as Mt Tom Price and Mt Whaleback. However structural controls on the largest iron ore deposits are often obscured due to the intensity and scale of ore development, whereas they are better preserved in the smaller deposits. Recognition that carbonate bearing protores at Mt Wall survived for nearly two billion years until intense recent weathering converted them to martite-goethite or magnetite-goethite ores may imply that more of the giant hematite-goethite deposits of the Hamersley province had hydrothermal precursors and were not formed by supergene processes alone.

Seismicity and tectonic relationships of the Nemaha Uplift in Oklahoma. Part V. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luza, K.V.; Lawson, J.E. Jr.

1983-02-01

The Nemaha Ridge is composed of a number of crustal blocks typically 3 to 5 miles (5 to 8 km) wide and 5 to 20 miles (8 to 32 km) long. Structure-contour maps prepared of the top of the Viola Formation (Ordovician), the base of the Pennsylvanian, and the top of the Oswego Formation (Middle Pennsylvanian) reveal a complex fault pattern associated with the Nemaha Uplift. This fault pattern is dominated by several discontinuous uplifts, such as the Oklahoma City, Lovell, Garber, and Crescent Uplifts. A detailed study of the Oklahoma City Uplift suggests that a number of the Nemaha-relatedmore » faults were developed in pre-Mississippian time. Many of these faults exhibit both increasing and decreasing displacements from early to late Paleozoic time. However, the displacement for most of the Oklahoma City faults took place between the end of Oswego time and the end of Hunton time. A lineament map was prepared for north-central Oklahoma. A detailed gravity map was prepared for the Kingfisher and Medford maxima. A total-intensity aeromagnetic map for the Enid and Oklahoma City 1/sup 0/ by 2/sup 0/ Quadrangles was prepared. A regional seismograph network was established to supplement existing seismological capability. A local earthquake-location program, named HYPERCUBE, was developed. From 1897 through 1976, Oklahoma has had approximately 128 known earthquakes. After the network became operational in late 1977, 255 additional earthquakes were detected in Oklahoma (through 1981). A study of earthquake distribution and intensity values in Oklahoma led to the development of a seismic-source map for Oklahoma and parts of the adjacent states. Six seismic-source zones were identified. For each zone except one, a magnitude-frequency relationship was determined.« less

The effects of transistor source-to-gate bridging faults in complex CMOS gates

NASA Astrophysics Data System (ADS)

Visweswaran, G. S.; Ali, Akhtar-Uz-Zaman M.; Lala, Parag K.; Hartmann, Carlos R. P.

1991-06-01

A study of the effect of gate-to-source bridging faults in the pull-up section of a complex CMOS gate is presented. The manifestation of these faults depends on the resistance value of the connection causing the bridging. It is shown that such faults manifest themselves either as stuck-at or stuck-open faults and can be detected by tests for stuck-at and stuck-open faults generated for the equivalent logic current. It is observed that for transistor channel lengths larger than 1 microns there exists a range of values of the bridging resistance for which the fault behaves as a pseudo-stuck-open fault.

Transmission line relay mis-operation detection based on time-synchronized field data

DOE PAGES

Esmaeilian, Ahad; Popovic, Tomo; Kezunovic, Mladen

2015-05-04

In this paper, a real-time tool to detect transmission line relay mis-operation is implemented. The tool uses time-synchronized measurements obtained from both ends of the line during disturbances. The proposed fault analysis tool comes into the picture only after the protective device has operated and tripped the line. The proposed methodology is able not only to detect, classify, and locate transmission line faults, but also to accurately confirm whether the line was tripped due to a mis-operation of protective relays. The analysis report includes either detailed description of the fault type and location or detection of relay mis-operation. As such,more » it can be a source of very useful information to support the system restoration. The focus of the paper is on the implementation requirements that allow practical application of the methodology, which is illustrated using the field data obtained the real power system. Testing and validation is done using the field data recorded by digital fault recorders and protective relays. The test data included several hundreds of event records corresponding to both relay mis-operations and actual faults. The discussion of results addresses various challenges encountered during the implementation and validation of the presented methodology.« less

Health management and controls for Earth-to-orbit propulsion systems

NASA Astrophysics Data System (ADS)

Bickford, R. L.

1995-03-01

Avionics and health management technologies increase the safety and reliability while decreasing the overall cost for Earth-to-orbit (ETO) propulsion systems. New ETO propulsion systems will depend on highly reliable fault tolerant flight avionics, advanced sensing systems and artificial intelligence aided software to ensure critical control, safety and maintenance requirements are met in a cost effective manner. Propulsion avionics consist of the engine controller, actuators, sensors, software and ground support elements. In addition to control and safety functions, these elements perform system monitoring for health management. Health management is enhanced by advanced sensing systems and algorithms which provide automated fault detection and enable adaptive control and/or maintenance approaches. Aerojet is developing advanced fault tolerant rocket engine controllers which provide very high levels of reliability. Smart sensors and software systems which significantly enhance fault coverage and enable automated operations are also under development. Smart sensing systems, such as flight capable plume spectrometers, have reached maturity in ground-based applications and are suitable for bridging to flight. Software to detect failed sensors has reached similar maturity. This paper will discuss fault detection and isolation for advanced rocket engine controllers as well as examples of advanced sensing systems and software which significantly improve component failure detection for engine system safety and health management.

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

Analysis of soft-sediment deformation structures in Neogene fluvio-lacustrine deposits of Çaybağı Formation, Eastern Turkey

NASA Astrophysics Data System (ADS)

Koç Taşgin, Calibe; Türkmen, İbrahim

2009-06-01

During the Neogene, both strike-slip and extensional regimes coexisted in eastern Turkey and, a number of fault-bounded basins associated with the East Anatolian Fault System developed. The Çaybağı Formation (Late Miocene-Early Pliocene) deposited in one of these basins consists of fluvio-lacustrine deposits. Numerous soft-sediment deformation structures are encountered in this formation, particularly in conglomerates, medium- to coarse-grained tuffaceous sandstones and claystones: folded structures (slumps, convolute laminations, and simple recumbent folds), water-escape structures (intruded sands, internal cusps, interpenetrative cusps and sand volcanoes), and load structures (load casts, pseudonodules, flame structures, and pillow structures). These structures are produced by liquefaction and/or fluidization of the unconsolidated sediments during a seismic shock. Consequently, the existence of seismically-induced deformation structures in the Çaybağı Formation and the association with a Neogene intraformational unconformity, growth faults, and reverse faults in the Çaybağı basin attest to the tectonic activity in this area during the Late Miocene and Early Pliocene. The East Anatolian Fault System, in particular the Uluova fault zone, is the most probable seismogenic source. Earthquakes with a magnitude of over 5 in the Richter scale can be postulated.

Method to Determine Appropriate Source Models of Large Earthquakes Including Tsunami Earthquakes for Tsunami Early Warning in Central America

NASA Astrophysics Data System (ADS)

Tanioka, Yuichiro; Miranda, Greyving Jose Arguello; Gusman, Aditya Riadi; Fujii, Yushiro

2017-08-01

Large earthquakes, such as the Mw 7.7 1992 Nicaragua earthquake, have occurred off the Pacific coasts of El Salvador and Nicaragua in Central America and have generated distractive tsunamis along these coasts. It is necessary to determine appropriate fault models before large tsunamis hit the coast. In this study, first, fault parameters were estimated from the W-phase inversion, and then an appropriate fault model was determined from the fault parameters and scaling relationships with a depth dependent rigidity. The method was tested for four large earthquakes, the 1992 Nicaragua tsunami earthquake (Mw7.7), the 2001 El Salvador earthquake (Mw7.7), the 2004 El Astillero earthquake (Mw7.0), and the 2012 El Salvador-Nicaragua earthquake (Mw7.3), which occurred off El Salvador and Nicaragua in Central America. The tsunami numerical simulations were carried out from the determined fault models. We found that the observed tsunami heights, run-up heights, and inundation areas were reasonably well explained by the computed ones. Therefore, our method for tsunami early warning purpose should work to estimate a fault model which reproduces tsunami heights near the coast of El Salvador and Nicaragua due to large earthquakes in the subduction zone.

Model-based design and experimental verification of a monitoring concept for an active-active electromechanical aileron actuation system

NASA Astrophysics Data System (ADS)

Arriola, David; Thielecke, Frank

2017-09-01

Electromechanical actuators have become a key technology for the onset of power-by-wire flight control systems in the next generation of commercial aircraft. The design of robust control and monitoring functions for these devices capable to mitigate the effects of safety-critical faults is essential in order to achieve the required level of fault tolerance. A primary flight control system comprising two electromechanical actuators nominally operating in active-active mode is considered. A set of five signal-based monitoring functions are designed using a detailed model of the system under consideration which includes non-linear parasitic effects, measurement and data acquisition effects, and actuator faults. Robust detection thresholds are determined based on the analysis of parametric and input uncertainties. The designed monitoring functions are verified experimentally and by simulation through the injection of faults in the validated model and in a test-rig suited to the actuation system under consideration, respectively. They guarantee a robust and efficient fault detection and isolation with a low risk of false alarms, additionally enabling the correct reconfiguration of the system for an enhanced operational availability. In 98% of the performed experiments and simulations, the correct faults were detected and confirmed within the time objectives set.

Episodic Rifting Events Within the Tjörnes Fracture Zone, an Onshore-Offshore Ridge-Transform in N-Iceland

NASA Astrophysics Data System (ADS)

Brandsdottir, B.; Magnusdottir, S.; Karson, J. A.; Detrick, R. S.; Driscoll, N. W.

2015-12-01

The multi-branched plate boundary across Iceland is made up of divergent and oblique rifts, and transform zones, characterized by entwined extensional and transform tectonics. The Tjörnes Fracture Zone (TFZ), located on the coast and offshore Northern Iceland, is a complex transform linking the northern rift zone (NVZ) on land with the Kolbeinsey Ridge offshore. Extension across TFZ is partitioned across three N-S trending rift basins; Eyjafjarðaráll, Skjálfandadjúp (SB) and Öxarfjörður and three WNW-NW oriented seismic lineaments; the Grímsey Oblique Rift, Húsavík-Flatey Faults (HFFs) and Dalvík Lineament. We compile the tectonic framework of the TFZ ridge-transform from aerial photos, satellite images, multibeam bathymetry and high-resolution seismic reflection data (Chirp). The rift basins are made up of normal faults with vertical displacements of up to 50-60 m, and post-glacial sediments of variable thickness. The SB comprises N5°W obliquely trending, eastward dipping normal faults as well as N10°E striking, westward dipping faults oriented roughly perpendicular to the N104°E spreading direction, indicative of early stages of rifting. Correlation of Chirp reflection data and tephrachronology from a sediment core within SB reveal major rifting episodes between 10-12.1 kyrs BP activating the whole basin, followed by smaller-scale fault movements throughout Holocene. Onshore faults have the same orientations as those mapped offshore and provide a basis for the interpretation of the kinematics of the faults throughout the region. These include transform parallel right-lateral, strike-slip faults separating domains dominated by spreading parallel left-lateral bookshelf faults. Shearing is most prominent along the HFFs, a system of right-lateral strike-slip faults with vertical displacement up to 15 m. Vertical fault movements reflect increased tectonic activity during early postglacial time coinciding with isostatic rebound enhancing volcanism within Iceland.

Mountain building, strike-slip faulting, and landscape evolution in the Marlborough Fault System, NZ: Insights from new low-temperature thermochronology and modeling

NASA Astrophysics Data System (ADS)

Duvall, A. R.; Collett, C.; Flowers, R. M.; Tucker, G. E.; Upton, P.

2016-12-01

The 150 km wide Marlborough Fault System (MFS) and adjacent dextral-reverse Alpine Fault accommodate oblique convergence of the Australian and Pacific plates in a broad transform boundary that extends for much of the South Island New Zealand. Understanding the deformation history of the Marlborough region offers the opportunity to study topographic evolution in a strike-slip setting and a fuller picture of the evolving New Zealand plate boundary as the MFS lies at the transition from oceanic Pacific plate subduction to oblique continental collision. Here we present low-temperature thermochronology from the MFS to place new limits on the timing and style of mountain building. We sampled a range of elevations spanning 2 km within and adjacent to the Kaikoura Mountains, which stand high as topographic anomalies above active strike-slip faults. Young apatite (U-Th)/He ages ( 2-5 Ma) on both sides of range-bounding faults are consistent with regional distributed deformation since the Pliocene initiation of strike-slip faulting. However, large differences in both zircon helium and apatite fission track ages, from Paleogene/Neogene ages within hanging walls to unreset >100 Ma ages in footwalls, indicate an early phase of fault-related vertical exhumation. Thermal modeling using the QTQt program reveals two phases of exhumation within the Kaikoura Ranges: rapid cooling at 15-12 Ma localized to hanging wall rocks and regional rapid cooling reflected in all samples starting at 4-5 Ma. These results and landscape evolution models suggest that, despite the presence of active mountain front faults, much of the topographic relief in this region may predate the onset of strike-slip faulting and that portions of the Marlborough Faults are re-activated thrusts that coincide with the early development of the transpressive plate boundary. Regional exhumation after 5 Ma likely reflects increased proximity to the migrating Pacific plate subduction zone and the buoyant Chatham Rise.

A combined approach for weak fault signature extraction of rolling element bearing using Hilbert envelop and zero frequency resonator

NASA Astrophysics Data System (ADS)

Kumar, Keshav; Shukla, Sumitra; Singh, Sachin Kumar

2018-04-01

Periodic impulses arise due to localised defects in rolling element bearing. At the early stage of defects, the weak impulses are immersed in strong machinery vibration. This paper proposes a combined approach based upon Hilbert envelop and zero frequency resonator for the detection of the weak periodic impulses. In the first step, the strength of impulses is increased by taking normalised Hilbert envelop of the signal. It also helps in better localization of these impulses on time axis. In the second step, Hilbert envelope of the signal is passed through the zero frequency resonator for the exact localization of the periodic impulses. Spectrum of the resonator output gives peak at the fault frequency. Simulated noisy signal with periodic impulses is used to explain the working of the algorithm. The proposed technique is verified with experimental data also. A comparison of the proposed method with Hilbert-Haung transform (HHT) based method is presented to establish the effectiveness of the proposed method.

Fault geometries in basement-induced wrench faulting under different initial stress states

NASA Astrophysics Data System (ADS)

Naylor, M. A.; Mandl, G.; Supesteijn, C. H. K.

Scaled sandbox experiments were used to generate models for relative ages, dip, strike and three-dimensional shape of faults in basement-controlled wrench faulting. The basic fault sequence runs from early en échelon Riedel shears and splay faults through 'lower-angle' shears to P shears. The Riedel shears are concave upwards and define a tulip structure in cross-section. In three dimensions, each Riedel shear has a helicoidal form. The sequence of faults and three-dimensional geometry are rationalized in terms of the prevailing stress field and Coulomb-Mohr theory of shear failure. The stress state in the sedimentary overburden before wrenching begins has a substantial influence on the fault geometries and on the final complexity of the fault zone. With the maximum compressive stress (∂ 1) initially parallel to the basement fault (transtension), Riedel shears are only slightly en échelon, sub-parallel to the basement fault, steeply dipping with a reduced helicoidal aspect. Conversely, with ∂ 1 initially perpendicular to the basement fault (transpression), Riedel shears are strongly oblique to the basement fault strike, have lower dips and an exaggerated helicoidal form; the final fault zone is both wide and complex. We find good agreement between the models and both mechanical theory and natural examples of wrench faulting.

Modeling the Fault Tolerant Capability of a Flight Control System: An Exercise in SCR Specification

NASA Technical Reports Server (NTRS)

Alexander, Chris; Cortellessa, Vittorio; DelGobbo, Diego; Mili, Ali; Napolitano, Marcello

2000-01-01

In life-critical and mission-critical applications, it is important to make provisions for a wide range of contingencies, by providing means for fault tolerance. In this paper, we discuss the specification of a flight control system that is fault tolerant with respect to sensor faults. Redundancy is provided by analytical relations that hold between sensor readings; depending on the conditions, this redundancy can be used to detect, identify and accommodate sensor faults.

Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zappala, D.; Tavner, P.; Crabtree, C.

2013-01-01

Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data representmore » one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.« less

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Demonstration of a quantum error detection code using a square lattice of four superconducting qubits

PubMed Central

Córcoles, A.D.; Magesan, Easwar; Srinivasan, Srikanth J.; Cross, Andrew W.; Steffen, M.; Gambetta, Jay M.; Chow, Jerry M.

2015-01-01

The ability to detect and deal with errors when manipulating quantum systems is a fundamental requirement for fault-tolerant quantum computing. Unlike classical bits that are subject to only digital bit-flip errors, quantum bits are susceptible to a much larger spectrum of errors, for which any complete quantum error-correcting code must account. Whilst classical bit-flip detection can be realized via a linear array of qubits, a general fault-tolerant quantum error-correcting code requires extending into a higher-dimensional lattice. Here we present a quantum error detection protocol on a two-by-two planar lattice of superconducting qubits. The protocol detects an arbitrary quantum error on an encoded two-qubit entangled state via quantum non-demolition parity measurements on another pair of error syndrome qubits. This result represents a building block towards larger lattices amenable to fault-tolerant quantum error correction architectures such as the surface code. PMID:25923200

Demonstration of a quantum error detection code using a square lattice of four superconducting qubits.

PubMed

Córcoles, A D; Magesan, Easwar; Srinivasan, Srikanth J; Cross, Andrew W; Steffen, M; Gambetta, Jay M; Chow, Jerry M

2015-04-29

The ability to detect and deal with errors when manipulating quantum systems is a fundamental requirement for fault-tolerant quantum computing. Unlike classical bits that are subject to only digital bit-flip errors, quantum bits are susceptible to a much larger spectrum of errors, for which any complete quantum error-correcting code must account. Whilst classical bit-flip detection can be realized via a linear array of qubits, a general fault-tolerant quantum error-correcting code requires extending into a higher-dimensional lattice. Here we present a quantum error detection protocol on a two-by-two planar lattice of superconducting qubits. The protocol detects an arbitrary quantum error on an encoded two-qubit entangled state via quantum non-demolition parity measurements on another pair of error syndrome qubits. This result represents a building block towards larger lattices amenable to fault-tolerant quantum error correction architectures such as the surface code.

Gear-box fault detection using time-frequency based methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Odgaard, Peter Fogh; Stoustrup, Jakob

2015-01-01

Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors. An alternative would be to use the existing measurements which are normally available for the wind turbine control system. The usage of these sensors instead would cut down the cost of the wind turbine by not using additional sensors. One of these available measurements is the generator speed, in which changes in the gear-box resonance frequency can be detected.more » Two different time-frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen-Loeve basis. Both of them detects the gear-box fault with an acceptable detection delay.« less

Fault detection for hydraulic pump based on chaotic parallel RBF network

NASA Astrophysics Data System (ADS)

Lu, Chen; Ma, Ning; Wang, Zhipeng

2011-12-01

In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

Reactivation of pre-existing mechanical anisotropies during polyphase tectonic evolution: slip tendency analysis as a tool to constrain mechanical properties of rocks

NASA Astrophysics Data System (ADS)

Traforti, Anna; Bistacchi, Andrea; Massironi, Matteo; Zampieri, Dario; Di Toro, Giulio

2017-04-01

Intracontinental deformation within the upper crust is accommodated by nucleation of new faults (generally satisfying the Anderson's theory of faulting) or brittle reactivation of pre-existing anisotropies when certain conditions are met. How prone to reactivation an existing mechanical anisotropy or discontinuity is, depends on its mechanical strength compared to that of the intact rock and on its orientation with respect to the regional stress field. In this study, we consider how different rock types (i.e. anisotropic vs. isotropic) are deformed during a well-constrained brittle polyphase tectonic evolution to derive the mechanical strength of pre-existing anisotropies and discontinuities (i.e. metamorphic foliations and inherited faults/fractures). The analysis has been carried out in the Eastern Sierras Pampeanas of Central Argentina. These are a series of basement ranges of the Andean foreland, which show compelling evidence of a long-lasting brittle deformation history from the Early Carboniferous to Present time, with three main deformational events (Early Triassic to Early Jurassic NE-SW extension, Early Cretaceous NW-SE extension and Miocene to Present ENE-WNW compression). The study area includes both isotropic granitic bodies and anisotropic phyllosilicate-bearing rocks (gneisses and phyllites). In this environment, each deformation phase causes significant reactivation of the inherited structures and rheological anisotropies, or alternatively formation of neo-formed Andersonian faults, thus providing a multidirectional probing of mechanical properties of these rocks. A meso- and micro-structural analysis of brittle reactivation of metamorphic foliation or inherited faults/fractures revealed that different rock types present remarkable differences in the style of deformation (i.e., phyllite foliation is reactivated during the last compressional phase and cut by newly-formed Andersonian faults/fractures during the first two extensional regimes; instead, gneiss foliation is pervasively reactivated during all the tectonic phases). Considering these observations, we applied a Slip Tendency analysis to estimate the upper and lower bounds to the friction coefficient for slip along the foliations (μs) and along pre-existing faults/fractures (μf). If an hypothetical condition with simultaneous failure on the inherited mechanical discontinuity (foliation or pre-existing fault/fracture) and new Andersonian faults is assumed, the ratio between μsor μf and μ0(the average friction coefficient for intact isotropic rocks) can be calculated as μs (or μf) = NTs ṡ μ0(where NTs represents the normalized slip tendency of the analyzed discontinuity). When just reactivation of foliation/faults/fractures is observed (i.e. no newly-formed Andersonian faults are recognised), an upper bound to μsand μfcan be estimated as μs (or μf) < NTs ṡ μ0. By contrast, the lower bound to μsand μfcan be obtained as μs (or μf) > NTs ṡ μ0, when the mechanical anisotropies are not reactivated and new Andersonian faults nucleate. Applying the above analysis to multiple deformation phases and rock types, we were able to approximatively estimate μs < 0.4 (gneisses) and 0.1 < μs < 0.2 (phyllites) and μf ≈ 0.4 (phyllites) and 0.3 (gneisses).

Finding faults: analogical comparison supports spatial concept learning in geoscience.

PubMed

Jee, Benjamin D; Uttal, David H; Gentner, Dedre; Manduca, Cathy; Shipley, Thomas F; Sageman, Bradley

2013-05-01

A central issue in education is how to support the spatial thinking involved in learning science, technology, engineering, and mathematics (STEM). We investigated whether and how the cognitive process of analogical comparison supports learning of a basic spatial concept in geoscience, fault. Because of the high variability in the appearance of faults, it may be difficult for students to learn the category-relevant spatial structure. There is abundant evidence that comparing analogous examples can help students gain insight into important category-defining features (Gentner in Cogn Sci 34(5):752-775, 2010). Further, comparing high-similarity pairs can be especially effective at revealing key differences (Sagi et al. 2012). Across three experiments, we tested whether comparison of visually similar contrasting examples would help students learn the fault concept. Our main findings were that participants performed better at identifying faults when they (1) compared contrasting (fault/no fault) cases versus viewing each case separately (Experiment 1), (2) compared similar as opposed to dissimilar contrasting cases early in learning (Experiment 2), and (3) viewed a contrasting pair of schematic block diagrams as opposed to a single block diagram of a fault as part of an instructional text (Experiment 3). These results suggest that comparison of visually similar contrasting cases helped distinguish category-relevant from category-irrelevant features for participants. When such comparisons occurred early in learning, participants were more likely to form an accurate conceptual representation. Thus, analogical comparison of images may provide one powerful way to enhance spatial learning in geoscience and other STEM disciplines.

A fault-tolerant control architecture for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Drozeski, Graham R.

Research has presented several approaches to achieve varying degrees of fault-tolerance in unmanned aircraft. Approaches in reconfigurable flight control are generally divided into two categories: those which incorporate multiple non-adaptive controllers and switch between them based on the output of a fault detection and identification element, and those that employ a single adaptive controller capable of compensating for a variety of fault modes. Regardless of the approach for reconfigurable flight control, certain fault modes dictate system restructuring in order to prevent a catastrophic failure. System restructuring enables active control of actuation not employed by the nominal system to recover controllability of the aircraft. After system restructuring, continued operation requires the generation of flight paths that adhere to an altered flight envelope. The control architecture developed in this research employs a multi-tiered hierarchy to allow unmanned aircraft to generate and track safe flight paths despite the occurrence of potentially catastrophic faults. The hierarchical architecture increases the level of autonomy of the system by integrating five functionalities with the baseline system: fault detection and identification, active system restructuring, reconfigurable flight control; reconfigurable path planning, and mission adaptation. Fault detection and identification algorithms continually monitor aircraft performance and issue fault declarations. When the severity of a fault exceeds the capability of the baseline flight controller, active system restructuring expands the controllability of the aircraft using unconventional control strategies not exploited by the baseline controller. Each of the reconfigurable flight controllers and the baseline controller employ a proven adaptive neural network control strategy. A reconfigurable path planner employs an adaptive model of the vehicle to re-shape the desired flight path. Generation of the revised flight path is posed as a linear program constrained by the response of the degraded system. Finally, a mission adaptation component estimates limitations on the closed-loop performance of the aircraft and adjusts the aircraft mission accordingly. A combination of simulation and flight test results using two unmanned helicopters validates the utility of the hierarchical architecture.

An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

2016-02-01

Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses hidden in vibration signals and performs well for bearing fault diagnosis.

Correlation of Titleist Performance Institute (TPI) level 1 movement screens and golf swing faults.

PubMed

Gulgin, Heather R; Schulte, Brian C; Crawley, Amy A

2014-02-01

Although some research in the past has examined how physical limitations in strength or flexibility affect a golfer's performance, the performance outcome most measured was driving distance. Currently, there are no data that have examined the relationship between selected strength and flexibility variables and golf swing faults. The purpose of this study was to examine the relationship between Titleist Performance Institute (TPI) level 1 movement screen variables and 14 common golf swing faults. Thirty-six male and female golfers (mean age, 25.4 ± 9.9 years; height, 175.9 ± 16.2 cm; mass, 76.2 ± 14.6 kg; handicap, 14.2 ± 10.4) participated. Twelve physical tests of strength, flexibility, and balance were assessed using the TPI level 1 golf fitness screening tool. Golfers then hit 4 golf shots (with a 5-iron) while being videoed, and those were then analyzed for 14 different golf swing faults (using V1Pro software). Three significant associations between a physical limitation and a particular golf swing fault were found: toe touch and early hip extension (p = 0.015), bridge on right side with both early hip extension (p = 0.050), and loss of posture (p = 0.028). In addition, an odds ratio showed that when a golfer could not overhead deep squat or single leg balance on left side, they were 2-3 times more likely to exhibit a early hip extension, loss of posture, or slide during the golf swing, as compared with those who could perform a correct overhead deep squat. Based on our findings, it is important for the golf fitness professional to particularly address a golfer's core strength, balance, and hamstring flexibility to help avoid common golf swing faults, which affect a golfer's ball striking ability and ultimately their performance.

Tectonic history of the Illinois basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolata, D.R.; Nelson, J.W.

1990-05-01

The Illinois basin began as a failed rift that developed during breakup of a supercontinent approximately 550 Ma. A rift basin in the southernmost part of the present Illinois basin subsided rapidly and filled with about 3,000 m of probable Early and Middle Cambrian sediments. By the Late Cambrian, the rift-bounding faults became inactive and a broad relatively slowly subsiding embayment, extending well beyond the rift and open to the Iapetus Ocean, persisted through most of the Paleozoic Era. Widespread deformation swept through the proto-Illinois basin beginning in the latest Mississippian, continuing to the end of the Paleozoic Era. Upliftmore » of basement fault blocks resulted in the formation of many major folds and faults. The timing of deformation and location of these structures in the forelands of the Ouachita and Alleghanian orogenic belts suggest that much of the deformation resulted from continental collision between North America and Gondwana. The associated compressional stress reactivated the ancient rift-bounding faults, upthrusting the northern edge of a crustal block approximately 1,000 m within the rift. Concurrently, dikes (radiometrically dated as Early Permian), sills, and explosion breccias formed in or adjacent to the reactivated rift. Subsequent extensional stress, probably associated with breakup of Pangea, caused the crustal block within the rift to sink back to near its original position. High-angle, northeast- to east-west-trending normal faults, with as much as 1,000 m of displacement, formed in the southern part of the basin. These faults displace some of the northwest trending Early Permian dikes. Structural closure of the southern end of the Illinois basin was caused by uplift of the Pascola arch sometime between the Late Pennsylvanian and Late Cretaceous.« less

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems.

PubMed

Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun; Wang, Gi-Nam

2016-01-01

Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

PubMed Central

Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun

2016-01-01

Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively. PMID:27974882

Analytic Confusion Matrix Bounds for Fault Detection and Isolation Using a Sum-of-Squared- Residuals Approach

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2009-01-01

Given a system which can fail in 1 or n different ways, a fault detection and isolation (FDI) algorithm uses sensor data in order to determine which fault is the most likely to have occurred. The effectiveness of an FDI algorithm can be quantified by a confusion matrix, which i ndicates the probability that each fault is isolated given that each fault has occurred. Confusion matrices are often generated with simulation data, particularly for complex systems. In this paper we perform FDI using sums of squares of sensor residuals (SSRs). We assume that the sensor residuals are Gaussian, which gives the SSRs a chi-squared distribution. We then generate analytic lower and upper bounds on the confusion matrix elements. This allows for the generation of optimal sensor sets without numerical simulations. The confusion matrix bound s are verified with simulated aircraft engine data.

Fault detection for singular switched linear systems with multiple time-varying delay in finite frequency domain

NASA Astrophysics Data System (ADS)

Zhai, Ding; Lu, Anyang; Li, Jinghao; Zhang, Qingling

2016-10-01

This paper deals with the problem of the fault detection (FD) for continuous-time singular switched linear systems with multiple time-varying delay. In this paper, the actuator fault is considered. Besides, the systems faults and unknown disturbances are assumed in known frequency domains. Some finite frequency performance indices are initially introduced to design the switched FD filters which ensure that the filtering augmented systems under switching signal with average dwell time are exponentially admissible and guarantee the fault input sensitivity and disturbance robustness. By developing generalised Kalman-Yakubovic-Popov lemma and using Parseval's theorem and Fourier transform, finite frequency delay-dependent sufficient conditions for the existence of such a filter which can guarantee the finite-frequency H- and H∞ performance are derived and formulated in terms of linear matrix inequalities. Four examples are provided to illustrate the effectiveness of the proposed finite frequency method.

Contrasting tectonothermal domains and faulting in the Potomac terrane, Virginia-Maryland - Discrimination by 40Ar/39Ar and fission-track thermochronology

USGS Publications Warehouse

Kunk, Michael J.; Wintsch, R.P.; Naeser, C.W.; Naeser, N.D.; Southworth, C.S.; Drake, Avery A.; Becker, J.L.

2005-01-01

New 40Ar/39Ar data reveal ages and thermal discontinuities that identify mapped and unmapped fault boundaries in the Potomac terrane in northern Virginia, thus confirming previous interpretations that it is a composite terrane. The rocks of the Potomac terrane were examined along the Potomac River, where it has been previously subdivided into three units: the Mather Gorge, Sykesville, and Laurel Formations. In the Mather Gorge Formation, at least two metamorphic thermal domains were identified, the Blockhouse Point and Bear Island domains, separated by a fault active in the late Devonian. Early Ordovician (ca. 475 Ma) cooling ages of amphibole in the Bear Island domain reflect cooling from Taconic metamorphism, whereas the Blockhouse Point domain was first metamorphosed in the Devonian. The 40Ar/39Ar data from muscovites in a third (eastern) domain within the Mather Gorge Formation, the Stubblefield Falls domain, record thrusting of the Sykesville Formation over the Mather Gorge Formation on the Plummers Island fault in the Devonian. The existence of two distinctly different thermal domains separated by a tectonic boundary within the Mather Gorge argues against its status as a formation. Hornblende cooling ages in the Sykesville Formation are Early Devonian (ca. 400 Ma), reflecting cooling from Taconic and Acadian metamorphism. The ages of retrograde and overprinting muscovite in phyllonites from domain-bounding faults are late Devonian (Acadian) and late Pennsylvanian (Alleghanian), marking the time of assembly of these domains and subsequent movement on the Plummers Island fault. Our data indicate that net vertical motion between the Bear Island domain of the Mather Gorge complex and the Sykesville Formation across the Plummers Island fault is east-side-up. Zircon fission-track cooling ages demonstrate thermal equillbrium across the Potomac terrane in the early Permian, and apatite fission-track cooling ages record tilting of the Potomac terrane in the Cretaceous or later with the west side up at least 1 km. ?? 2005 Geological Society of America.

Power System Transient Diagnostics Based on Novel Traveling Wave Detection

NASA Astrophysics Data System (ADS)

Hamidi, Reza Jalilzadeh

Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by improving the state-of-the-art algorithms. The proliferation of high-voltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configuration, such as multi-terminal hybrid transmission systems, limits the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observation of the squares of wavelet transformation coefficients. Using the ATs, an over-determined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method are evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase auto-reclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the features of the electrical arcs (transient faults) are severely influenced by a number of random parameters, including the convection of the air and plasma, wind speed, air pressure, and humidity. Therefore, the dead-time (the de-energization duration of the faulty phase) is unpredictable. Accordingly, conservatively long dead-times are usually considered by protection engineers. However, if the exact arc distinction time is determined, the power system stability and quality will enhance. Therefore, a new method for detection of arc extinction times leading to a new ASPAR method utilizing power line carrier (PLC) signals is presented. The efficiency of the proposed ASPAR method is verified through simulations and compared with the existing ASPAR methods. High-sampling measurements are prone to be skewed by the environmental noises and analog-to-digital (A/D) converters quantization errors. Therefore noise-contaminated measurements are the major source of uncertainties and errors in the outcomes of traveling wave-based diagnostic applications. The existing AT-detection methods do not provide enough sensitivity and selectivity at the same time. Therefore, a new AT-detection method based on short-time matrix pencil (STMPM) is developed to accurately detect ATs of the traveling waves with low signal-to-noise (SNR) ratios. As STMPM is based on matrix algebra, it is a challenging to implement this new technique in microprocessor-based fault locators. Hence, a fully recursive and computationally efficient method based on adaptive discrete Kalman filter (ADKF) is introduced for AT-detection, which is proper for microprocessors and able to accomplish accurate AT-detection for online applications such as ultra-high-speed protection. Both proposed AT-detection methods are evaluated based on extensive simulation studies, and the superior outcomes are compared to the existing methods.