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1

Arc Fault Detection and Discrimination Methods  

Microsoft Academic Search

Arc waveform characteristics can be evaluated with various methods to recognize the presence of hazardous arc fault conditions. Discussion covers the arc phenomena and how it is generated in a low voltage electrical distribution circuit, as well as the isolation of the presence of hazardous conditions versus conditions that could falsely mimic the presence of an arc fault. Many waveform

Carlos E. Restrepo

2007-01-01

2

Fault Detection and Diagnosis Method for VAV Terminal Units  

E-print Network

This paper proposes two fault detection and diagnosis methods for VAV units without a sensor of supply air volume, and the results of applying these methods to a real building are presented. One method detects faults by applying a statistical method...

Miyata, M.; Yoshida, H.; Asada, M.; Wang, F.; Hashiguchi, S.

2004-01-01

3

Supervision, fault-detection and fault-diagnosis methods — An introduction  

Microsoft Academic Search

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper gives an introduction to the field of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure that is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from

R. Isermann

1997-01-01

4

Fault-Tolerant Structure and Modulation Strategies With Fault Detection Method for Matrix Converters  

Microsoft Academic Search

This paper proposes a fault-tolerant matrix converter with reconfigurable structure and modified switch control schemes, along with a fault diagnosis technique for open-circuited switch failures. The proposed fault recognition method can detect and locate a failed bidirectional switch with voltage error signals dedicated to each switch, based on a direct comparison of the input and the output voltages. Following the

Sangshin Kwak

2010-01-01

5

Improved Hidden-Markov-Model Method Of Detecting Faults  

NASA Technical Reports Server (NTRS)

Method of automated, continuous monitoring to detect faults in complicated dynamic system based on hidden-Markov-model (HMM) approach. Simpler than another, recently proposed HMM method, but retains advantages of that method, including low susceptibility to false alarms, no need for mathematical model of dynamics of system under normal or faulty conditions, and ability to detect subtle changes in characteristics of monitored signals. Examples of systems monitored by use of this method include motors, turbines, and pumps critical in their applications; chemical-processing plants; powerplants; and biomedical systems.

Smyth, Padhraic J.

1994-01-01

6

ACTUATOR FAULT DETECTION, ISOLATION METHOD AND STATE ESTIMATOR DESIGN FOR HOT ROLLING MILL MONITORING  

E-print Network

ACTUATOR FAULT DETECTION, ISOLATION METHOD AND STATE ESTIMATOR DESIGN FOR HOT ROLLING MILL difficult, however model-based approach among fault diagnosis methods or Fault Detection and Isolation (FDI of this paper is to develop an actuator fault diagnosis technique for a Single Input Multiple Output (SIMO

Paris-Sud XI, Université de

7

High resolution seismics methods in application to fault zone detection  

NASA Astrophysics Data System (ADS)

Surveys were carried out along border line between Outer Carpathians, Inner Carpathians and Pieniny Klippen Belt. Main point of interest was imaging transition zone structured by para-conglomerates, sandstone and clays lenses, crossing in near neighbourhood of Stare Bystre, village in the southern part of Poland. Actually geological works states existence of two hypothetical faults, first at the direction NE-SW and second NNW-SSE. Main aim of geological and geophysical investigation was to prove that mentioned fault has a system of smaller discontinuities connected with previous main fault activity. Para-conglomerate exposures, which is localized close to discussed fault is cut by visible system of cracks. That fact provide geological evidences that this system could be the effect of previous fault activity so in other words, it has a continuation up to main discontinuities. What is more part of the same formation para-conglomerates is covered by Neogen river sediments, so non-direct detection methods of cracks azimuth must be applied. Geophysical investigation was located near mentioned exposure and conducted in 3-D variant. Measurements were extremely focused on determining any changes of elevation buried para-conglomerates and velocity variation inside studied sediments. Seismic methods such as refraction and refraction tomography were used to imaging bedrock. Surveys were carried out in non typical acquisition, azimuthal schema. During field works 24- channels seismograph and 4 Hz, 10 Hz and 100 Hz geophones were used. Hypothetical discontinuities were estimated after analysing seismic records and expressed by velocity variation in bedding rocks and additionally evaluated changes in its elevation. Furthermore, in this study attempt of use refraction wave attributes related to loosing rock - para-conglomerates continuity were exposed. The presentation of geophysical data had a volumetric character what was easier to interpret and better related to assumptions about geological structure of mentioned zone. Correlation between geophysical and geological results seems to be very effective in reconstruction the forming processes of fault zones. Better understanding phenomena, which rules of young fault activities, reduce incorporated hazards and simultaneously bring information about presence geodynamics processes.

Matula, Rafal; Czaja, Klaudia; Mahmod, Adam Ahmed

2014-05-01

8

A Statistical, Rule-Based Fault Detection and Diagnostic Method for Vapor Compression Air Conditioners  

Microsoft Academic Search

This paper presents a method for automated detection and diagnosis of faults in vapor compression air conditioners that only requires temperature measurements, and one humidity measurement. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for both fault detection and diagnosis. For fault detection, statistical properties of the

Todd M. Rossi; James E. Braun

1997-01-01

9

Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods  

Microsoft Academic Search

In this paper we present the comparison results of induction motor fault detection using stator current, vibration, and acoustic methods. A broken rotor bar fault and a combination of bearing faults (inner race, outer race, and rolling element faults) were induced into variable speed three-phase induction motors. Both healthy and faulty signatures were acquired under different speed and load conditions.

WEIDONG LI; CHRIS K. MECHEFSKE

2006-01-01

10

A lightweight fault detection method for sensor networks based on anomaly execution statistics  

NASA Astrophysics Data System (ADS)

Fault detection of deployed senor networks is difficult due to server constrained resources and limited access to the networks. A lightweight fault detection method is proposed to address the problem of unknown software fault detection in post-deployment phase. By analyzing characteristics of node programs, a statistical model and corresponding test statistic are devised to detect unknown fault at runtime. Evaluation on typical sensor network applications shows that the storage and runtime overhead are acceptable. A case study further demonstrates the effectiveness of the proposed method.

Ma, Junyan; Zhao, Xiangmo; Hui, Fei; Shi, Xin; Yang, Lan

2014-10-01

11

A Fault Detection filter design method for a class of linear time-varying systems  

Microsoft Academic Search

In this paper we propose a Fault Detection (FD) filter design method for linear parameter varying (LPV) systems. The FD filter is an optimal Hinfin Luenberger observer synthesized by minimizing frequency conditions which ensure guaranteed levels of disturbance rejection and fault detection. Via the Bounded Real Lemma (BRL) and the Separation Principle the design method is formulated as a convex

Alessandro Casavola; Domenico Famularo; G. Franze; Ron J. Patton

2008-01-01

12

Solar system fault detection  

DOEpatents

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.

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

1984-05-14

13

Solar system fault detection  

DOEpatents

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.

Farrington, Robert B. (Wheatridge, CO); Pruett, Jr., James C. (Lakewood, CO)

1986-01-01

14

A Comparison of Fault Detection Methods For a Transcritical Refrigeration System  

E-print Network

for detecting and diagnosing faults have been widely tested for subcritical systems, but have not been applied to transcritical systems. These methods can involve either dynamic analysis of the vapor compression cycle or a variety of algorithms based on steady...

Janecke, Alex Karl

2012-10-19

15

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

NASA Technical Reports Server (NTRS)

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.

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

2007-01-01

16

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

DOEpatents

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 in 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.

Almasi, Gheorghe (Ardsley, NY) [Ardsley, NY; Blumrich, Matthias Augustin (Ridgefield, CT) [Ridgefield, CT; Chen, Dong (Croton-On-Hudson, NY) [Croton-On-Hudson, NY; Coteus, Paul (Yorktown, NY) [Yorktown, NY; Gara, Alan (Mount Kisco, NY) [Mount Kisco, NY; Giampapa, Mark E. (Irvington, NY) [Irvington, NY; Heidelberger, Philip (Cortlandt Manor, NY) [Cortlandt Manor, NY; Hoenicke, Dirk I. (Ossining, NY) [Ossining, NY; Singh, Sarabjeet (Mississauga, CA) [Mississauga, CA; Steinmacher-Burow, Burkhard D. (Wernau, DE) [Wernau, DE; Takken, Todd (Brewster, NY) [Brewster, NY; Vranas, Pavlos (Bedford Hills, NY) [Bedford Hills, NY

2008-06-03

17

Method of detecting the direction of arcing faults on power distribution feeders  

E-print Network

will be next to impossible. Since the algorithm requires only the change on voltage and current signals, a simpler method of obtaining the wavefront has to be adapted. Passing through a highpass filter and obtaining the high frequency signals, 15... fundamental voltage and current transformer signals during an arcing high impedance fault, the higher order frequency components of voltage and current are being used for high impedance fault detection. One method based on increases in third and fifth...

Fernando, W. Anand Krisantha

2012-06-07

18

Advanced Wigner Method for Fault Detection and Diagnosis System  

Microsoft Academic Search

An advanced Wigner method for time-frequency analysis based on the Wigner distribution and short-time Fourier transformation\\u000a (STFT) methods is used to examine the acoustic emission signals detected during the operation of pipelines in the power plants.\\u000a The acoustic emission signals, which depend on the behavior of materials deforming under stress, will be changed when pipelines\\u000a crack or leak. Based on

Do Van Tuan; Sang Jin Cho; Ui Pil Chong

19

Model-Based Methods for Textile Fault Detection J. G. Campbell,1  

E-print Network

Model-Based Methods for Textile Fault Detection J. G. Campbell,1 C. Fraley,2,3 D. Stanford,2 F for woven textiles in discriminating subtle flaw patterns from the pronounced background of repetitive. Int J Imaging Syst Technol, 10, 339­346, 1999 I. FLAW DETECTION IN TEXTILE FABRIC Obstacles to machine

Raftery, Adrian

20

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

NASA Technical Reports Server (NTRS)

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.

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

1993-01-01

21

A review of process fault detection and diagnosis: Part I: Quantitative model-based methods  

Microsoft Academic Search

Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently. AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in

Venkat Venkatasubramanian; Raghunathan Rengaswamy; Kewen Yin; Surya N. Kavuri

2003-01-01

22

Fault detection and fault tolerance in robotics  

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

23

Fault detection for T-s fuzzy time-delay systems: delta operator and input-output methods.  

PubMed

This paper focuses on the problem of fault detection for Takagi-Sugeno fuzzy systems with time-varying delays via delta operator approach. By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem. The time-varying delay is approximated by the two-term approximation method. Fuzzy augmented fault detection system is constructed in ? -domain, and a threshold function is given. By applying the scaled small gain theorem and choosing a Lyapunov-Krasovskii functional in ? -domain, a sufficient condition of asymptotic stability with a prescribed H? disturbance attenuation level is derived for the proposed fault detection system. Then, a solvability condition for the designed fault detection filter is established, with which the desired filter can be obtained by solving a convex optimization problem. Finally, an example is given to demonstrate the feasibility and effectiveness of the proposed method. PMID:24919207

Li, Hongyi; Gao, Yabin; Wu, Ligang; Lam, H K

2015-02-01

24

Fault detection and isolation  

NASA Technical Reports Server (NTRS)

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.

Bernath, Greg

1994-01-01

25

PEM fuel cell fault detection and identification using differential method: simulation and experimental validation  

NASA Astrophysics Data System (ADS)

PEM fuel cell performance and lifetime strongly depend on the polymer membrane and MEA hydration. As the internal moisture is very sensitive to the operating conditions (temperature, stoichiometry, load current, water management…), keeping the optimal working point is complex and requires real-time monitoring. This article focuses on PEM fuel cell stack health diagnosis and more precisely on stack fault detection monitoring. This paper intends to define new, simple and effective methods to get relevant information on usual faults or malfunctions occurring in the fuel cell stack. For this purpose, the authors present a fault detection method using simple and non-intrusive on-line technique based on the space signature of the cell voltages. The authors have the objective to minimize the number of embedded sensors and instrumentation in order to get a precise, reliable and economic solution in a mass market application. A very low number of sensors are indeed needed for this monitoring and the associated algorithm can be implemented on-line. This technique is validated on a 20-cell PEMFC stack. It demonstrates that the developed method is particularly efficient in flooding case. As a matter of fact, it uses directly the stack as a sensor which enables to get a quick feedback on its state of health.

Frappé, E.; de Bernardinis, A.; Bethoux, O.; Candusso, D.; Harel, F.; Marchand, C.; Coquery, G.

2011-05-01

26

Fault detection for modern Diesel engines using signal- and process model-based methods  

Microsoft Academic Search

Modern Diesel engines with direct fuel injection and turbo charging have shown a significant progress in fuel consumption, emissions and driveability. Together with exhaust gas recirculation and variable geometry turbochargers they became complicated and complex processes. Therefore, fault detection and diagnosis is not easily done and need to be improved. This contribution shows a systematic development of fault detection and

Frank Kimmich; Anselm Schwarte; Rolf Isermann

2005-01-01

27

Flight elements: Fault detection and fault management  

NASA Technical Reports Server (NTRS)

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.

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

1990-01-01

28

Outlier Detection Rules for Fault Detection in Solar Photovoltaic Arrays  

E-print Network

. Then the fault detection method becomes straightforward: the string current out of the normal rangeOutlier Detection Rules for Fault Detection in Solar Photovoltaic Arrays Ye Zhao, Brad Lehman Abstract-- Solar photovoltaic (PV) arrays are unique power sources that may have uncleared fault current

Lehman, Brad

29

Discrete Data Qualification System and Method Comprising Noise Series Fault Detection  

NASA Technical Reports Server (NTRS)

A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

Fulton, Christopher; Wong, Edmond; Melcher, Kevin; Bickford, Randall

2013-01-01

30

Fault detection and diagnosis capabilities of test sequence selection  

E-print Network

Review Fault detection and diagnosis capabilities of test sequence selection methods based complete fault coverage. These seven methods are formally analysed for their fault diagnosis capabilities of the test sequences they select, and their fault detection and diagnosis capabilities. Keywords: fault

Thulsiraman, Krishnaiyan

31

Applications of Fault Detection in Vibrating Structures  

NASA Technical Reports Server (NTRS)

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.

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

2012-01-01

32

Plant monitoring and fault detection  

Microsoft Academic Search

Data reconciliation and principal component analysis are two recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). Principal component analysis can then be applied to raw process data for monitoring purpose.

Th Amand; G Heyen; B Kalitventzeff

2001-01-01

33

Fault detection methods for vapor-compression air conditioners using electrical measurements  

E-print Network

(cont.) This method was experimentally tested and validated on a commercially available air handler and duct system. In the second class of faults studied, liquid refrigerant, rather than vapor, enters the cylinder of a ...

Laughman, Christopher Reed.

2008-01-01

34

Row fault detection system  

SciTech Connect

An apparatus, program product and method checks for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2008-10-14

35

Row fault detection system  

DOEpatents

An apparatus, program product and method check for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2012-02-07

36

CMOS Bridging Fault Detection  

Microsoft Academic Search

The authors compare the performance of two test generation techniques, stuck fault testing and current testing, when applied to CMOS bridging faults. Accurate simulation of such faults mandated the development of several new design automation tools, including an analog-digital fault simulator. The results of this simulation are analyzed. It is shown that stuck fault test generation, while inherently incapable of

Thomas M. Storey; Wojciech Maly

1990-01-01

37

Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools  

E-print Network

Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools Brian E detection of equipment and process faults to maintain high process yields and rapid fault classification treat fault detection and classification as a two-step process. We present a novel method

Boning, Duane S.

38

Arc burst pattern analysis fault detection system  

NASA Technical Reports Server (NTRS)

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.

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

1997-01-01

39

Polynomially Complete Fault Detection Problems  

Microsoft Academic Search

We look at several variations of the single fault detection problem for combinational logic circuits and show that deciding whether single faults are detectable by input-output (I\\/O) experiments is polynomially complete, i.e., there is a polynomial time algorithm to decide if these single faults are detectable if and only if there is a polynomial time algorithm for problems such as

Oscar H. Ibarra; Sartaj Sahni

1975-01-01

40

Arc fault detection system  

DOEpatents

An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard.

Jha, Kamal N. (Bethel Park, PA)

1999-01-01

41

Arc fault detection system  

DOEpatents

An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

Jha, K.N.

1999-05-18

42

Tunable architecture for aircraft fault detection  

NASA Technical Reports Server (NTRS)

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.

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

2012-01-01

43

Roving Emulation as a Fault Detection Mechanism  

Microsoft Academic Search

Abstract-In this paperwepresent anewbuilt-in test method- ologyfordetecting andlocating faults indigital systems. The technique iscalled roving emulation andconsists ofanoff-line snapshottypeemulation orsimulation ofoperating components inasystem. Itsprimary application isintesting systems inthe field wherereal-time fault detection isnotrequired. Theprimary performance measureofthistestschemaistakentobethe expected value oftheerrorlatency, i.e., thetimerequired to detect afault onceitfirst occurs. Theprimary results ofthis paperdealwithderiving equations fortheerrorlatency. We present bothaprobabilistic andservice-waiting modeltoanalyze theexpected error latency

Melvin A. Breuer; Asad A. Ismaeel

1986-01-01

44

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

NASA Technical Reports Server (NTRS)

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.

Joshi, Suresh M.

2012-01-01

45

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

NASA Astrophysics Data System (ADS)

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.

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

1999-11-01

46

Row fault detection system  

DOEpatents

An apparatus and program product check for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2010-02-23

47

Opportunistic Transient-Fault Detection  

Microsoft Academic Search

CMOS scaling increases susceptibility of microprocessors to transient faults. Most current proposals for transient-fault detection use full redundancy to achieve perfect coverage while incurring significant performance degradation. However, most commodity systems do not need or provide perfect coverage. A recent paper explores this leniency to reduce the soft-error rate of the issue queue during L2 misses while incurring minimal performance

Mohamed A. Gomaa; T. N. Vijaykumar

2005-01-01

48

Opportunistic transient-fault detection  

Microsoft Academic Search

CMOS scaling increases susceptibility of microprocessors to transient faults. Most current proposals for transient-fault detection use full redundancy to achieve perfect coverage while incurring significant performance degradation. However, most commodity systems do not need or provide perfect coverage. A recent paper explores this leniency to reduce the soft-error rate of the issue queue during L2 misses while incurring minimal performance

Mohamed A. Gomaa; T. N. Vijaykumar

2005-01-01

49

Opportunistic Transient-Fault Detection  

Microsoft Academic Search

CMOS scaling increases susceptibility of microprocessors to transient faults. Most current proposals for transient-fault detection use full redundancy to achieve perfect coverage while incurring significant performance degradation. How- ever, most commodity systems do not need or provide perfect coverage. A recent paper explores this leniency to reduce the soft-error rate of the issue queue during L2 misses while incur- ring

Mohamed A. Gomaa; T. N. Vijaykumar

2006-01-01

50

Leakage Fault Detection Method for Axial-Piston Variable Displacement Pumps  

Microsoft Academic Search

To address the lack of health monitoring for gas turbine engine accessory components, Sentient developed a promising fault estimation algorithm for axial-piston variable displacement pumps. The key to this success was the development of a physics-based nonlinear model of the system that includes three common types of fluid leakage faults. The modeling effort and simulation results provided the thorough knowledge

Jerome J. Palazzolo; Larry D. Scheunemann; J. R. Hartin

2008-01-01

51

All row, planar fault detection system  

DOEpatents

An apparatus, program product and method for detecting nodal faults may simultaneously cause designated nodes of a cell to communicate with all nodes adjacent to each of the designated nodes. Furthermore, all nodes along the axes of the designated nodes are made to communicate with their adjacent nodes, and the communications are analyzed to determine if a node or connection is faulty.

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

2013-07-23

52

Survey of robust residual generation and evaluation methods in observer-based fault detection systems  

Microsoft Academic Search

The paper outlines recent advances of the theory of observer-based fault diagnosis in dynamic systems towards the design of robust techniques of residual generation and residual evaluation. Emphasis will be placed upon the latest contributions using frequency domain techniques including H? theory, nonlinear unknown input observer theory, adaptive observer theory, artificial intelligence including fuzzy logic, knowledge-based techniques and the natural

P. M. Frank; X. Ding

1997-01-01

53

Expert System Detects Power-Distribution Faults  

NASA Technical Reports Server (NTRS)

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.

Walters, Jerry L.; Quinn, Todd M.

1994-01-01

54

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)

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.

Wilson, Edward (Inventor)

2008-01-01

55

Bisectional fault detection system  

DOEpatents

An apparatus, program product and method logically divide a group of nodes and causes node pairs comprising a node from each section to communicate. Results from the communications may be analyzed to determine performance characteristics, such as bandwidth and proper connectivity.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2012-02-14

56

Bisectional fault detection system  

SciTech Connect

An apparatus, program product and method logically divides a group of nodes and causes node pairs comprising a node from each section to communicate. Results from the communications may be analyzed to determine performance characteristics, such as bandwidth and proper connectivity.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2008-11-11

57

The Fault Detection Problem Andreas Haeberlen  

E-print Network

The Fault Detection Problem Andreas Haeberlen Petr Kuznetsov Abstract One of the most important challenges in distributed computing is ensuring that services are correct and available despite faults. Recently it has been argued that fault detection can be factored out from computation, and that a generic

Pennsylvania, University of

58

The Fault Detection Problem Andreas Haeberlen1  

E-print Network

The Fault Detection Problem Andreas Haeberlen1 and Petr Kuznetsov2 1 Max Planck Institute challenges in distributed com- puting is ensuring that services are correct and available despite faults. Recently it has been argued that fault detection can be factored out from computation, and that a generic

Pennsylvania, University of

59

Sensor Fault Detection and Isolation System  

E-print Network

The purpose of this research is to develop a Fault Detection and Isolation (FDI) system which is capable to diagnosis multiple sensor faults in nonlinear cases. In order to lead this study closer to real world applications in oil industries...

Yang, Cheng-Ken

2014-08-01

60

Planetary Gearbox Fault Detection Using Vibration Separation Techniques  

NASA Technical Reports Server (NTRS)

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.

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

2011-01-01

61

Evaluation of a Decoupling-Based Fault Detection and Diagnostic Technique - Part I: Field Emulation Evaluation  

E-print Network

Existing methods addressing automated fault detection and diagnosis (FDD) for vapor compression air conditioning system have good performance for faults that occur individually, but they have difficulty in handling multiple-simultaneous faults...

Li, H.; Braun, J.

2006-01-01

62

Online non-contact fault detection of LED chips  

Microsoft Academic Search

An online non-contact fault detection Method of LED chips is presented based on the photovoltaic effect in diodes. By observing the photo-generated current in the bonding lead frame of a LED chip, the LED chip and its electric connection with the lead frame during packaging are checked. The fault detection principle is described in detail in this paper. By using

Lian Li; Jing wen; Ping Li; Yumei Wen; Fei Yin

2008-01-01

63

Observer-based fault detection for nuclear reactors  

E-print Network

This is a study of fault detection for nuclear reactor systems. Basic concepts are derived from fundamental theories on system observers. Different types of fault- actuator fault, sensor fault, and system dynamics fault ...

Li, Qing, 1972-

2001-01-01

64

APPLICATION OF A SUBSPACE-BASED FAULT DETECTION METHOD TO INDUSTRIAL STRUCTURES  

Microsoft Academic Search

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

L. Mevel; L. Hermans; H. VAN DER AUWERAER

1999-01-01

65

Signal Injection as a Fault Detection Technique  

PubMed Central

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies. PMID:22163801

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

66

Method and apparatus for generating motor current spectra to enhance motor system fault detection  

DOEpatents

A method and circuitry are disclosed for sampling periodic amplitude modulations in a nonstationary periodic carrier wave to determine frequencies in the amplitude modulations. The method and circuit are described in terms of an improved motor current signature analysis. The method insures that the sampled data set contains an exact whole number of carrier wave cycles by defining the rate at which samples of motor current data are collected. The circuitry insures that a sampled data set containing stationary carrier waves is recreated from the analog motor current signal containing nonstationary carrier waves by conditioning the actual sampling rate to adjust with the frequency variations in the carrier wave. After the sampled data is transformed to the frequency domain via the Discrete Fourier Transform, the frequency distribution in the discrete spectra of those components due to the carrier wave and its harmonics will be minimized so that signals of interest are more easily analyzed. 29 figs.

Linehan, D.J.; Bunch, S.L.; Lyster, C.T.

1995-10-24

67

Method and apparatus for generating motor current spectra to enhance motor system fault detection  

DOEpatents

A method and circuitry for sampling periodic amplitude modulations in a nonstationary periodic carrier wave to determine frequencies in the amplitude modulations. The method and circuit are described in terms of an improved motor current signature analysis. The method insures that the sampled data set contains an exact whole number of carrier wave cycles by defining the rate at which samples of motor current data are collected. The circuitry insures that a sampled data set containing stationary carrier waves is recreated from the analog motor current signal containing nonstationary carrier waves by conditioning the actual sampling rate to adjust with the frequency variations in the carrier wave. After the sampled data is transformed to the frequency domain via the Discrete Fourier Transform, the frequency distribution in the discrete spectra of those components due to the carrier wave and its harmonics will be minimized so that signals of interest are more easily analyzed.

Linehan, Daniel J. (Knoxville, TN); Bunch, Stanley L. (Oak Ridge, TN); Lyster, Carl T. (Knoxville, TN)

1995-01-01

68

Surveillance system and method having an adaptive sequential probability fault detection test  

NASA Technical Reports Server (NTRS)

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

Bickford, Randall L. (Inventor); Herzog, James P. (Inventor)

2006-01-01

69

Surveillance System and Method having an Adaptive Sequential Probability Fault Detection Test  

NASA Technical Reports Server (NTRS)

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

Bickford, Randall L. (Inventor); Herzog, James P. (Inventor)

2008-01-01

70

Surveillance system and method having an adaptive sequential probability fault detection test  

NASA Technical Reports Server (NTRS)

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

Herzog, James P. (Inventor); Bickford, Randall L. (Inventor)

2005-01-01

71

Model reconstruction using POD method for gray-box fault detection  

Microsoft Academic Search

Abstruct-This paper describes using the Proper Orthogonal Decomposition (POD) method to create low-order dynamical models for the Model Filter component of Beacon-based Exception Analysis for Multi-missions (BEAM). The POD modeling procedure is described, and its usefulness in creating simple low-order dynamical models of a complex system. The POD procedure will be shown on an example problem of Burgers' Equation. It

Han G. Park; Michail Zak

2003-01-01

72

Fault detection in finite frequency domain for Takagi-Sugeno fuzzy systems with sensor faults.  

PubMed

This paper is concerned with the fault detection (FD) problem in finite frequency domain for continuous-time Takagi-Sugeno fuzzy systems with sensor faults. Some finite-frequency performance indices are initially introduced to measure the fault/reference input sensitivity and disturbance robustness. Based on these performance indices, an effective FD scheme is then presented such that the generated residual is designed to be sensitive to both fault and reference input for faulty cases, while robust against the reference input for fault-free case. As the additional reference input sensitivity for faulty cases is considered, it is shown that the proposed method improves the existing FD techniques and achieves a better FD performance. The theory is supported by simulation results related to the detection of sensor faults in a tunnel-diode circuit. PMID:24184791

Li, Xiao-Jian; Yang, Guang-Hong

2014-08-01

73

A Game Theoretic Fault Detection Filter  

NASA Technical Reports Server (NTRS)

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.

Chung, Walter H.; Speyer, Jason L.

1995-01-01

74

All-to-all sequenced fault detection system  

DOEpatents

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.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2010-11-02

75

Fault detection of univariate non-Gaussian data with Bayesian network  

E-print Network

Fault detection of univariate non-Gaussian data with Bayesian network Sylvain Verron, Teodor.verron@univ-angers.fr Abstract--The purpose of this article is to present a new method for fault detection with Bayesian network. The interest of this method is to propose a new structure of Bayesian network allowing to detect a fault

Paris-Sud XI, Université de

76

Approximate active fault detection and control  

NASA Astrophysics Data System (ADS)

This paper deals with approximate active fault detection and control for nonlinear discrete-time stochastic systems over an infinite time horizon. Multiple model framework is used to represent fault-free and finitely many faulty models. An imperfect state information problem is reformulated using a hyper-state and dynamic programming is applied to solve the problem numerically. The proposed active fault detector and controller is illustrated in a numerical example of an air handling unit.

Škach, Jan; Pun?ochá?, Ivo; Šimandl, Miroslav

2014-12-01

77

Detection of faults and software reliability analysis  

NASA Technical Reports Server (NTRS)

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.

Knight, J. C.

1986-01-01

78

Negative Selection Algorithm for Aircraft Fault Detection  

NASA Technical Reports Server (NTRS)

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.

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

2004-01-01

79

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

PubMed

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. PMID:12858981

Bailey, Margaret B; Kreider, Jan F

2003-07-01

80

Trends in the application of model-based fault detection and diagnosis of technical processes  

Microsoft Academic Search

After a short overview of the historical development of model-based fault detection, some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated, based on the work within the IFAC SAFEPROCESS Technical Committee. Some basic fault-detection and diagnosis methods are briefly considered. Then, an evaluation of publications during the last 5 years shows some trends

R. Isermann; P. Ballé

1997-01-01

81

Predicting Fault Detection Effectiveness J. A. Morgan & G. J. Knafl W. E. Wong  

E-print Network

Predicting Fault Detection Effectiveness J. A. Morgan & G. J. Knafl W. E. Wong School of Computer Regression methods are used to model fault detection effectiveness in terms of several product and testing process measures. The relative importance of these product/process measures for predicting fault detection

Morgan, Joseph

82

LEARNING BAYESIAN NETWORKS FOR FAULT DETECTION: APPLICATION TO THE 747 LONGITUDINAL MOTION  

Microsoft Academic Search

The correct detection of a fault can save worthy resources or even prevent the destruction of key equipment, but, mainly, the correct detection of a single fault can save lives, as, for example, in the case of spaceships, aircraft and nuclear plants. In this work a new fault detection method, based on the learning of a Bayesian network, is applied

Jackson Paul Matsuura; Takashi Yoneyama; Roberto Kawakami; Harrop Galvão

83

Fault Detection and Automated Fault Diagnosis for Embedded Integrated Electrical Passives  

E-print Network

Fault Detection and Automated Fault Diagnosis for Embedded Integrated Electrical Passives Heebyung and automated fault diagnosis us- ing pole zero analysis of embedded integrated pas- sive. For pole zero-matching algorithm to detect faults and perform automated diagnosis of catastrophic and parametric faults using

Swaminathan, Madhavan

84

SIMULTANEOUS FAULT DETECTION AND CLASSIFICATION FOR SEMICONDUCTOR MANUFACTURING TOOLS  

E-print Network

SIMULTANEOUS FAULT DETECTION AND CLASSIFICATION FOR SEMICONDUCTOR MANUFACTURING TOOLS Brian E, accurate, and sensitive detection of equipment and process faults to maintain high process yields and rapid fault classification (diagnosis) of the cause to minimize tool downtime in semiconductor manufacturing

Boning, Duane S.

85

A Simultaneous Imaging Method of Multiple Scattering Modes for Detecting a Fault-Zone Heterogeneous Structure of the San Andreas Fault, Parkfield, California  

NASA Astrophysics Data System (ADS)

One approach to understanding the generation process of earthquakes is to image fault-zone heterogeneity through the use of single-point scatterers. We present an imaging methodology for imaging multiple scattering modes (P-P, P-S, S-P, and S-S) to assess the relative amplitude of heterogeneity in the bulk and shear modulus in the fault zone. This method is designed for a network of three-component seismic stations and a source array produced from an aftershock sequence. Scattering modes and scatterer locations are determined by the following procedure. For each station, the wave-type and slowness (i.e., propagation) vector for the source-to-scatterer part of the path are estimated by performing a semblance analysis. For the scatterer-to-station segment of the path, the wave-type is constrained by comparing the observed polarization vector, inferred from particle motion, with the predicted propagation vectors from candidate scatterer locations, assuming a half-space velocity model (Vp and V_s are 6.40 km/s and 3.45 km/s, respectively in this study). Candidate scatterer locations and allowable scattering modes are then evaluated by comparison of the observed slowness and polarization vectors with a probability density function based on the 95 per cent confidence levels for these two parameters, in addition to the travel time residual between the observed and predicted travel times. We apply the method to borehole seismograms from 10 relocated aftershocks of the October 20, 1992, M=4.7 Parkfield earthquake recorded by eight stations of the High Resolution Seismic Network. To examine the spatial resolution of the image sections and the ability of our data set to distinguishing among scattering modes, we perform a simple numerical experiment with synthetic seismograms in the frequency range of 8-16 Hz where the signal level was highest, adding 20 per cent of Gaussian random noise to the average signal level at each station. We place three scatterers of each scattering mode at various locations along the San Andreas Fault. We find that the scatterers are generally well recovered so that we expect to resolve each of the scattering modes. From the Parkfield data, we obtain image sections for P-P, P-S, S-P, and S-S scattering modes in the frequency band used in the synthetic test. We find a well constrained region for S-S scattering mode that is located about 10 km south-southeast of the epicenter of the 1966 M=6 Parkfield earthquake at 5 km depth. The size is estimated to be 300 m. We observe no other scattering modes with this data set in this region. The strength of the S-S scattering, combined with the absence of P-P, P-S, and S-P scattering modes implies that structural heterogeneity in the region is dominated by variations in the shear modulus. As such, we hypothesize that the S-S scatterer is associated with fluid-filled cracks or fractures (O'Connell and Budiansky, 1974).

Taira, T.; Silver, P. G.; Niu, F.; Nadeau, R. M.

2005-12-01

86

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

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

87

Evaluation of a Decoupling-Based Fault Detection and Diagnostic Technique - Part II: Field Evaluation and Application  

E-print Network

Existing methods addressing automated fault detection and diagnosis (FDD) for vapor compression air conditioning system have good performance for faults that occur individually, but they have difficulty in handling multiple-simultaneous faults...

Li, H.; Braun, J.

2006-01-01

88

Tracy-Widom distribution based fault detection approach: application to aircraft sensor/actuator fault detection.  

PubMed

The fault detection approach based on the Tracy-Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined. PMID:21855060

Hajiyev, Ch

2012-01-01

89

Space shuttle main engine fault detection using neural networks  

NASA Technical Reports Server (NTRS)

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.

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

90

Detect and classify faults using neural nets  

SciTech Connect

The analysis of transmission line faults is essential to the proper performance of the power system. It is required if protective relays are to take the appropriate action and in monitoring the performance of relays, circuit breakers, and other protective and control elements. The detection and classification of transmission line faults is a fundamental component of such fault analysis. Another application of fault analysis is in software packages for automated analysis of digital fault recorder (DFR) files. Recently, such a package, called DFR Assistant, was developed for substation applications. This program can be installed locally in a substation, in which case it is connected directly to the DFR via a high speed parallel link, or it can be installed at a central station, in which case it can be configured to automatically analyze events coming from all DFRs.

Kezunovic, M.; Rikalo, I.

1996-10-01

91

Immunity-Based Aircraft Fault Detection System  

NASA Technical Reports Server (NTRS)

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.

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

2004-01-01

92

Detecting Faults By Use Of Hidden Markov Models  

NASA Technical Reports Server (NTRS)

Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

Smyth, Padhraic J.

1995-01-01

93

Reset Tree-Based Optical Fault Detection  

PubMed Central

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

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

2013-01-01

94

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

NASA Technical Reports Server (NTRS)

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.

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

2010-01-01

95

Cell boundary fault detection system  

DOEpatents

An apparatus and program product determine a nodal fault along the boundary, or face, of a computing cell. Nodes on adjacent cell boundaries communicate with each other, and the communications are analyzed to determine if a node or connection is faulty.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2011-04-19

96

Fault Detection and Isolation in Manufacturing Systems with an Identified Discrete Event Model  

E-print Network

Fault Detection and Isolation in Manufacturing Systems with an Identified Discrete Event Model) In this paper a generic method for fault detection and isolation (FDI) in manufacturing systems considered and controller built on the basis of observed fault free system behavior. An identification algorithm known from

Paris-Sud XI, Université de

97

MULTIPLE FAULT DETECTION AND ISOLATION Weber P. , Gentil S. , Ripoll P.  

E-print Network

MULTIPLE FAULT DETECTION AND ISOLATION Weber P. , Gentil S. , Ripoll P. TTTT , Foulloy L. TTTT CNRS 40 (ripoll@esia.univ-savoie.fr, foulloy@esia.univ-savoie.fr) Abstract: Model-based fault detection methods allow the generation of residuals as fault indicators. Isolation is generally based

Paris-Sud XI, Université de

98

Fault detection system for internal combustion engine control apparatus  

Microsoft Academic Search

A fault-detection system for an internal combustion engine control apparatus is described comprising: an actuator included in an internal combustion engine, main control means for controlling the actuator, first fault detection means for detecting a fault of the main control means; subcontrol means for controlling the actuator in place of the main control means only when a fault of the

T. Abe; M. Takao; M. Tomoaki

1989-01-01

99

The Effects of Fault Counting Methods on Fault Model Quality  

NASA Technical Reports Server (NTRS)

In this paper, we describe three other fault-counting techniques and compare the models resulting from the application of two of those methods to the models obtained from the application of our proposed definition.

Nikora, Allen P.; Munson, John C.

2004-01-01

100

Fault Detection, Isolation and Control Reconfiguration of Three-Phase PMSM Drives  

E-print Network

Fault Detection, Isolation and Control Reconfiguration of Three-Phase PMSM Drives Fabien Meinguet Abstract--This paper deals with on-line software fault de- tection and isolation method for a drive faults are single-phase open-circuit and current sensor outage. The method is based on the monitoring

Paris-Sud XI, Université de

101

A Hybrid Fault Event Detection Algorithm Using Fault Recorder Data  

Microsoft Academic Search

When fault occurs in power grid, a lot of alarms and records are generated in substations. These are secondary circuit signals, fault recorder and PMU data. Fault recorder data contains the details of fault evolution, which can be used for diagnosis fault occurrence time, circuit breaker operation events. It is crucial for power grid diagnosis and intelligent alarm functions. However,

Kang Taifeng; Wu Wenchuan; Sun Hongbin; Zhang Boming; Qian Xiao

2010-01-01

102

Fault detection and diagnosis in multiprocessor systems  

SciTech Connect

A hierarchical approach to multiprocessor system fault tolerance is presented there. This scheme consists of employing concurrent error detection at the processor level while utilizing multiprocessor system testing and diagnosis at the system level. In this manner, errors that are not caused by serious fault conditions are detected and recovered at the processor level, while the more serious faults are detected and diagnosed at the system level. In the concurrent-error-detection area, a technique known as the data-block capture and analysis monitoring process is presented. This process consists of first capturing a sequence of signals forming a block of data from a system and then analyzing the data block for the presence of fault symptoms. At the system level, the problems of detection and diagnosis of faulty processors are considered under a new uniformly probabilistic model. This work focuses on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability.

Blough, D.M.

1988-01-01

103

Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA  

PubMed Central

A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

Lee, Wonhee; Park, Chan Gook

2014-01-01

104

Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.  

PubMed

A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

Lee, Wonhee; Park, Chan Gook

2014-01-01

105

Fault-detection in syndrome testing of digital circuits  

Microsoft Academic Search

The fault-detection properties and aliasing phenomenon for syndrome testing of digital circuits are investigated. It is shown that syndrome testing can detect all single, and all odd numbers multiple faults; but only a certain class of even numbers of multiple faults can be detected. However, under certain conditions, syndrome testing can be effectively used to detect all unidirectional errors.

RANA EJAZ AHMED

1993-01-01

106

Fault detection and diagnosis of photovoltaic systems  

NASA Astrophysics Data System (ADS)

The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

Wu, Xing

107

Catastrophic fault diagnosis in dynamic systems using bond graph methods  

SciTech Connect

Detection and diagnosis of faults has become a critical issue in high performance engineering systems as well as in mass-produced equipment. It is particularly helpful when the diagnosis can be made at the initial design level with respect to a prospective fault list. A number of powerful methods have been developed for aiding in the general fault analysis of designs. Catastrophic faults represent the limit case of complete local failure of connections or components. They result in the interruption of energy transfer between corresponding points in the system. In this work the conventional approach to fault detection and diagnosis is extended by means of bond-graph methods to a wide variety of engineering systems. Attention is focused on catastrophic fault diagnosis. A catastrophic fault dictionary is generated from the system model based on topological properties of the bond graph. The dictionary is processed by existing methods to extract a catastrophic fault report to aid the engineer in performing a design analysis.

Yarom, Tamar.

1990-01-01

108

The Detection of Fault-Prone Programs  

Microsoft Academic Search

The use of the statistical technique of discriminant analysis as a tool for the detection of fault-prone programs is explored. A principal-components procedure was employed to reduce simple multicollinear complexity metrics to uncorrelated measures on orthogonal complexity domains. These uncorrelated measures were then used to classify programs into alternate groups, depending on the metric values of the program. The criterion

John C. Munson; Taghi M. Khoshgoftaar

1992-01-01

109

Robust Fault Detection and Isolation for Stochastic Systems  

NASA Technical Reports Server (NTRS)

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.

George, Jemin; Gregory, Irene M.

2010-01-01

110

Distributed Fault Detection Using Consensus of Markov Chains  

E-print Network

Distributed Fault Detection Using Consensus of Markov Chains Dejan P. Jovanovi and Philip K. Pollett Abstract We propose a fault detection procedure appropriate for use in a variety of industrial Models. Index Terms Fault detection, consensus algorithm, mixtures of Markov chains, the EM algorithm

Pollett, Phil

111

Fault Detection, Identification and Accommodation for an Electro-hydraulic  

E-print Network

Fault Detection, Identification and Accommodation for an Electro-hydraulic System: An Adaptive@purdue.edu) Abstract: In the present work, we use an adaptive robust approach for fault detection and accommodation, such a scheme becomes a natural choice for designing robust fault detection algorithms for electro

Yao, Bin

112

Multi-directional fault detection system  

DOEpatents

An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

Archer, Charles Jens (Rochester, MN); Pinnow, Kurt Walter (Rochester, MN); Ratterman, Joseph D. (Rochester, MN); Smith, Brian Edward (Rochester, MN)

2010-11-23

113

Fault detection and diagnosis of HVAC systems  

SciTech Connect

This paper presents a model-based fault detection and diagnosis (FDD) system for building heating, ventilating, and air conditioning (HVAC). Model-based fault detection is based on the strategy of determining the difference or the residuals between the normal and the existing patterns. Their approach was to attack the problem on many levels of abstraction: from the signal level, controller programming level, and system component, all the way up to the information and knowledge processing level. The various issues of real implementation of the system and the processing of real-time on-line data in actual systems of campus buildings using the proven technology and off-the-shelf commercial tools are discussed. The research was based on input and output points and software control programs found in typical direct digital control systems used for variable-air-volume air handlers and VAV cooling and hot water reheat terminal units.

Han, C.Y.; Xiao, Y.; Ruther, C.J.

1999-07-01

114

Fault-detection technique in a WDM-PON.  

PubMed

We propose and demonstrate a new in-service fault-localization method for a wavelength division multiplexing passive optical network (WDM-PON). This scheme uses a tunable OTDR realized by a wavelength-locked Fabry-Perot laser diode. We successfully detect the faults both at the feeder fiber and the drop fibers. The resolution and the dynamic range are 100 m and 12 dB, respectively. In addition, the crosstalk induced by the OTDR signal to the transmission data is negligible. PMID:19532377

Park, Juhee; Baik, Jinserk; Lee, Changhee

2007-02-19

115

Online Fault Detection and Tolerance for Photovoltaic Energy Harvesting Systems  

E-print Network

Online Fault Detection and Tolerance for Photovoltaic Energy Harvesting Systems Xue Lin 1 , Yanzhi (PV systems) are subject to PV cell faults, which decrease the efficiency of PV systems and even shorten the PV system lifespan. Manual PV cell fault detection and elimination are expensive and nearly

Pedram, Massoud

116

Bibliography on Induction Motors Faults Detection and Diagnosis  

E-print Network

Bibliography on Induction Motors Faults Detection and Diagnosis M.E.H. Benbouzid, Member, IEEE and diagnosis techniques. However, performing reliable and accurate motor faults detection and diagnosis a comprehensive list of books, workshops, conferences, and journal papers related to induction motors faults

Brest, Université de

117

Detrended fluctuation analysis of vibration signals for bearing fault detection  

Microsoft Academic Search

Rolling element bearings are widely used in various rotary machines. Accordingly, a reliable bearing fault detection technique is critically needed in industries to prevent these machines' performance degradation, malfunction, or even catastrophic failures. Although a number of approaches have been reported in the literature, bearing fault detection, however, still remains a very challenging task because most of the bearing fault

Jie Liu

2011-01-01

118

Model based fault detection of an electro-hydraulic cylinder  

Microsoft Academic Search

One of the key issues in the design of fault detection and diagnosis (FDD) schemes for hydraulic systems is the effect of model uncertainties such as severe parametric uncertainties and unmodeled dynamics on their performance. This paper presents the application of a nonlinear model based adaptive robust observer (ARO) to the fault detection and diagnosis of some common faults that

Phanindra Garimella; Bin Yao

2005-01-01

119

Occupancy Based Fault Detection on Building Level - a Feasibility Study  

E-print Network

-line, self learning fault detection tool on building level. Taking passive user behavior into account, the tool aims to distinguish real faults from unexpected user behavior. An artificial neural network model is used to predict building energy consumption...

Tuip, B.; Houten, M.; Trcka, M.; Hensen, M.

2010-01-01

120

Fault Attack Resistant Cryptographic Hardware with Uniform Error Detection  

E-print Network

. 1 Introduction Hardware implementations of cryptographic algorithms are vulnerable to maliciousFault Attack Resistant Cryptographic Hardware with Uniform Error Detection Konrad J. Kulikowski are not optimal for the protection of hardware implementations of cryptographic hardware against fault attacks

Karpovsky, Mark

121

An iterative inversion method for transmission line fault location  

NASA Astrophysics Data System (ADS)

This dissertation discusses various transmission line forward modeling techniques in both time and frequency domains. Although time domain methods offer simplicity in most cases, the computational inefficiency and lack of fidelity make these methods less attractive. Therefore, the more efficient frequency domain technique is emphasized - a modified transmission matrix (also known as ABCD) method. One of the most difficult problems in electrical wire fault location nowadays is detecting and locating frayed wiring, where the wire is only partially damaged. This type of fault can be very small and extremely difficult to detect. Most inversion schemes used to locate faults require forward models that accurately represent detailed reflections. Resolving these very small faults requires an especially accurate forward model where not only the fault but also all the other very small changes caused by normal aspects of the wiring system are included. A very high resolution Finite Difference Time Domain (FDTD) method can be used to simulate this type of fault and details of the surrounding wiring system with enough fidelity to distinguish the small fault. However, this is very costly in terms of computational resources. This dissertation demonstrates a quick way of building the fray profile that significantly reduces the simulation time. Finally, the ultimate goal of the highly realistic forward modeling is the inversion, in which a set of measured data is given and the inversion algorithm interprets the location and the nature of fault on the wire. Multiple iterations are typically required, and thus, high efficiency is necessary. A new method introduced in this dissertation is capable of identifying multiple unknown parameters in just a few steps.

Wu, Shang Chieh

2011-12-01

122

Fault detection and diagnosis using neural network approaches  

NASA Technical Reports Server (NTRS)

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Kramer, Mark A.

1992-01-01

123

Support Vector Machines and Wavelet Packet Analysis for Fault Detection and Indentification  

Microsoft Academic Search

This paper presents a data driven fault detection and identification (FDI) method using support vector machines (SVM) and the wavelet packet transform (WPT). The primary focus of this paper is to present a robust data driven fault diagnosis scheme. The investigated scheme has the capability to detect and identify faulty components of a given system through examination of its output

Estefan Ortiz; Vassilis L. Syrmos

2006-01-01

124

On-line detection of aircraft icing: An application of optimal fault detection and isolation  

NASA Astrophysics Data System (ADS)

This dissertation describes an innovative method utilizing gain-scheduled optimal linear observers (Fault Detection and Isolation Filters) for on-line fault detection and isolation of failures in aircraft utilizing existing sensors and avionics. Aircraft icing, and tail-plane icing in particular, is a serious safety concern and current methods of detecting aircraft icing are generally inaccurate and unreliable. Ice detection is accomplished by modeling aircraft icing as an input failure to a dynamic system. The method described here will allow the retrofit of older planes because it only utilizes existing sensors. This icing detection method motivated two new methods for designing optimal fault detection and isolation filters. These methods utilize Linear Matrix Inequalities to solve for the optimal detection filter by allowing the user to specify the desired eigenstructure. The first involves a least squares approach while the second utilizes a direct parameterization of the estimator feedback gain. A Monte Carlo simulation of the proposed ice detection method shows that the improvement offered by this new design is substantial. This technology is applicable to the detection and isolation of degradation failures in all dynamical systems.

Miller, Robert Henry

125

Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants  

NASA Astrophysics Data System (ADS)

The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory in conjunction with advanced fluid mechanics to achieve the objective of developing an implementable comprehensive monitoring systems, suitable for use on space missions. First, a three-dimensional transport model is developed in order to model the dispersion of air contaminants. The flow field, which is an important input to the transport model, is obtained by solving the Navier Stokes equations for the cabin geometry and the appropriate boundary conditions, using a finite element method. Steady flow fields are computed for various conditions for both laminar and turbulent cases. Contamination dispersion studies are undertaken both for routine substances introduced through the inlet ducts and for emissions of toxics inside the cabin volume. The dispersion studies indicate that lumped models and even a two-dimensional model are sometimes inadequate to assure that the Spacecraft Maximum Allowable Concentrations (SMACs) are not exceeded locally. Since the research was targeted at real-time application aboard Spacecraft, a state estimation routine is implemented using Implicit Kalman Filtering. The routine makes use of the model predictions and measurements from the sensor system in order to arrive at an optimal estimate of the state of the system for each time step. Fault detection is accomplished through the use of analytical redundancy, where error residuals from the Kalman filter are monitored in order to detect any faults in the system, and to distinguish between sensor and process faults. Finally, a fault diagnosis system is developed, which is a combination of sensitivity analysis and an Extended Kalman Filter, which is used to estimate the location and capacity of an unknown source emission in the system. The sensitivity analysis involves pre-calculating sensitivity coefficients, which measure the response of each sensor to a source emission at each location in the cabin, and in the event of a fault, current measurements are used and inverted to arrive at an initial guess for the unknown source that is causing the fault. An Extended Implicit Kalman filter, developed especially for this application then makes use of the initial guess to arrive at an optimal estimate for the unknown source, by minimizing the squared estimation error. The fault diagnosis procedure is successfully tested for various test cases.

Narayan, Anand P.

1998-07-01

126

A new model-free method performing closed-loop fault diagnosis  

E-print Network

A new model-free method performing closed-loop fault diagnosis for an aeronautical system Julien), CNRS-SUPELEC-Univ Paris-Sud, France, eric.walter@lss.supelec.fr Abstract: Fault diagnosis for a closed detection and isolation, fault diagnosis, guidance and control. 1. INTRODUCTION In-flight securement

Paris-Sud XI, Université de

127

Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming  

Microsoft Academic Search

This paper proposes a fault diagnosis method for plant machinery in an unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). IPS is used to extract feature frequencies of each machine state from measured vibration signals for distinguishing faults by relative crossing information. Excellent symptom parameters for detecting faults are automatically generated by the GP. The excellent

Peng Chen; Masatoshi Taniguchi; Toshio Toyota; Zhengja He

2005-01-01

128

Development of New Whole Building Fault Detection and Diagnosis Techniques for Commissioning Persistence  

E-print Network

to detect abnormal whole building energy consumption, and two approaches called Cosine Similarity method and Euclidean Distance Similarity method are developed to isolate the possible fault reasons. The effectiveness of these approaches is demonstrated...

Lin, Guanjing

2012-12-07

129

Fault Detection and Classification in Transmission Lines Using ANFIS  

NASA Astrophysics Data System (ADS)

This paper presents an application of ANFIS approach for automated fault disturbance detection and classification in transmission lines using measured data from one terminal of the transmission line. The ANFIS design and implementation are aimed at high-speed processing which can provide selection real-time detection and classification of faults. The ANFIS has been proposed not only to detect all shunt faults but also to identify the type of faults for digital distance protection system. The proposed technique is able to accurately identify the phase(s) involved in all ten types of shunt faults that may occur in a transmission line. The ANFIS's were trained and tested using various sets of field data. The field data are obtained from the simulation of faults at various points of a transmission line using a computer program based on Matlab. Various fault scenarios (fault types, fault locations and fault impedance) are considered in this paper. The inputs to ANFIS's are phase current and voltage measurement available at the relay location based on Root-Mean-Square values. The outputs of ANFIS's are 1 or 0 for detection of faults and type of fault. Through simulated process, the results indicate that the speed and selectivity of the approach are quite robust and provides adequate performance for a transmission and distribution monitoring, control and protection applications.

Elbaset, Adel A.; Hiyama, Takashi

130

On Finding a Nearly Minimal Set of Fault Detection Tests for Combinational Logic Nets  

Microsoft Academic Search

A procedure is described for finding, by shortcut methods, a near-minimal set of tests for detecting all single faults in a combinational logic net. The procedure should prove useful for nets which are too large to be treated by more exact methods [2]. The set of tests so found also appears useful for diagnosing (i.e., locating) faults. The class of

D. B. Armstrong

1966-01-01

131

VCSEL fault location apparatus and method  

DOEpatents

An apparatus for locating a fault within an optical fiber is disclosed. The apparatus, which can be formed as a part of a fiber-optic transmitter or as a stand-alone instrument, utilizes a vertical-cavity surface-emitting laser (VCSEL) to generate a test pulse of light which is coupled into an optical fiber under test. The VCSEL is subsequently reconfigured by changing a bias voltage thereto and is used as a resonant-cavity photodetector (RCPD) to detect a portion of the test light pulse which is reflected or scattered from any fault within the optical fiber. A time interval .DELTA.t between an instant in time when the test light pulse is generated and the time the reflected or scattered portion is detected can then be used to determine the location of the fault within the optical fiber.

Keeler, Gordon A. (Albuquerque, NM); Serkland, Darwin K. (Albuquerque, NM)

2007-05-15

132

Early fault detection in automotive ball bearings using the minimum variance cepstrum  

NASA Astrophysics Data System (ADS)

Ball bearings in automotive wheels play an important role in a vehicle. They enable an automobile to run and simultaneously support the vehicle. Once faults are generated, even if they are small, they often grow fast even under normal driving condition and cause vibration and noise. Therefore, it is critical to detect faults as early as possible to prevent bearings from generating harsh noise and vibration. How early faults can be detected is associated with how well a detecting method finds the information of early faults from measured signal. Incipient faults are so small that the fault signal is inherently buried by noise. Minimum variance cepstrum (MVC) has been introduced for the observation of periodic impulse signal under noisy environments. We are particularly focusing on the definition of MVC that goes back to the original definition by Bogert et al. in comparison with the recently prevalent definition of cepstral analysis. In this work, the MVC is, therefore, obtained by liftering a logarithmic power spectrum, and the lifter bank is designed by the minimum variance algorithm. Furthermore, it is also shown how efficient the method is for detecting periodic fault signal made by early faults by using automotive ball bearings, with which an automobile is equipped under running conditions. We were able to detect incipient faults in 4 out of 12 normal bearings which passed acceptance test as well as in bearings that were recalled due to noise and vibration. In addition, we compared the results of the proposed method with results obtained using other older well-established early fault detection methods that were chosen from 4 groups of methods which were classified by the domain of observation. The results demonstrated that MVC determined bearing fault periods more clearly than other methods under the given condition.

Park, Choon-Su; Choi, Young-Chul; Kim, Yang-Hann

2013-07-01

133

Induction machine faults detection using stator current parametric spectral estimation  

NASA Astrophysics Data System (ADS)

Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed technique is assessed using simulation signals, issued from a coupled electromagnetic circuits approach-based simulation tool for mechanical unbalance and electrical asymmetry faults detection. It is afterward validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.

El Bouchikhi, El Houssin; Choqueuse, Vincent; Benbouzid, Mohamed

2015-02-01

134

Detection of faults and software reliability analysis  

NASA Technical Reports Server (NTRS)

Multi-version or N-version programming is 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. These versions are executed in parallel in the application environment; each receives identical inputs and each produces its version of the required outputs. The outputs are collected by a voter and, in principle, they should all be the same. In practice there may be some disagreement. If this occurs, the results of the majority are taken to be the correct output, and that is the output used by the system. A total of 27 programs were produced. Each of these programs was then subjected to one million randomly-generated test cases. The experiment yielded a number of programs containing faults that are useful for general studies of software reliability as well as studies of N-version programming. Fault tolerance through data diversity and analytic models of comparison testing are discussed.

Knight, John C.

1987-01-01

135

Toward Reducing Fault Fix Time: Understanding Developer Behavior for the Design of Automated Fault Detection Tools, the Full Report  

E-print Network

Toward Reducing Fault Fix Time: Understanding Developer Behavior for the Design of Automated Fault}@csc.ncsu.edu Abstract The longer a fault remains in the code from the time it was injected, the more time it will take to fix the fault. Increasingly, automated fault detection (AFD) tools are providing developers

Young, R. Michael

136

Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application  

NASA Astrophysics Data System (ADS)

An important step in model-based fault detection is residual evaluation, where residuals are evaluated with the aim to detect changes in their behavior caused by faults. To handle residuals subject to time-varying uncertainties and disturbances, which indeed are present in practice, a novel statistical residual evaluation approach is presented. The main contribution is to base the residual evaluation on an explicit comparison of the probability distribution of the residual, estimated online using current data, with a no-fault residual distribution. The no-fault distribution is based on a set of a priori known no-fault residual distributions, and is continuously adapted to the current situation. As a second contribution, a method is proposed for estimating the required set of no-fault residual distributions off-line from no-fault training data. The proposed residual evaluation approach is evaluated with measurement data on a residual for fault detection in the gas-flow system of a Scania truck diesel engine. Results show that small faults can be reliably detected with the proposed approach in cases where regular methods fail.

Svärd, Carl; Nyberg, Mattias; Frisk, Erik; Krysander, Mattias

2014-03-01

137

Detection of faults and software reliability analysis  

NASA Technical Reports Server (NTRS)

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.

Knight, J. C.

1987-01-01

138

Rapid detection of small oscillation faults via deterministic learning.  

PubMed

Detection of small faults is one of the most important and challenging tasks in the area of fault diagnosis. In this paper, we present an approach for the rapid detection of small oscillation faults based on a recently proposed deterministic learning (DL) theory. The approach consists of two phases: the training phase and the test phase. In the training phase, the system dynamics underlying normal and fault oscillations are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. In the diagnosis phase, rapid detection is implemented. Specially, a bank of estimators are constructed using the constant RBF neural networks to represent the training normal and fault modes. By comparing the set of estimators with the test monitored system, a set of residuals are generated, and the average L(1) norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the training normal mode and oscillation faults. The occurrence of a test oscillation fault can be rapidly detected according to the smallest residual principle. A rigorous analysis of the performance of the detection scheme is also given. The novelty of the paper lies in that the modeling uncertainty and nonlinear fault functions are accurately approximated and then the knowledge is utilized to achieve rapid detection of small oscillation faults. Simulation studies are included to demonstrate the effectiveness of the approach. PMID:21813356

Wang, Cong; Chen, Tianrui

2011-08-01

139

PSEUDO POWER SIGNATURES FOR AIRCRAFT FAULT DETECTION AND IDENTIFICATION  

E-print Network

1 PSEUDO POWER SIGNATURES FOR AIRCRAFT FAULT DETECTION AND IDENTIFICATION Min Luo, Louisiana State on the concept by applying pseudo power signatures to detect faults. We introduce a Singular Value Decomposition (SVD) approach for the computation of pseudo power signatures and discuss some of the advantages

Koppelman, David M.

140

Machine-learning Based Automated Fault Detection in Seismic Chiyuan Zhang and Charlie Frogner (MIT),  

E-print Network

Machine-learning Based Automated Fault Detection in Seismic Traces Chiyuan Zhang and Charlie no seismic data has been migrated. Our novel method is based on machine learning tech- niques and can a machine learning approach: We generate a set of seismic traces from velocity models containing faults

Poggio, Tomaso

141

Model-based fault-detection and diagnosis – status and applications  

Microsoft Academic Search

For the improvement of reliability, safety and efficiency advanced methods of supervision, fault-detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight

Rolf Isermann

2005-01-01

142

A fault detection and isolation scheme for industrial systems based on multiple operating models  

Microsoft Academic Search

In this paper, a fault diagnosis method is developed for systems described by multi-models. The main contribution consists in the design of a new fault detection and isolation (FDI) scheme through an adaptive filter for such systems. Based on the assumption that dynamic behaviour of the process is described by a multi-model approach around different operating points, a set of

M. Rodrigues; D. Theilliol; M. Adam-Medina; D. Sauter

2008-01-01

143

Distributed fault detection and isolation resilient to network model uncertainties.  

PubMed

The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented as a graph. The subsystems are represented by nodes, while the edges correspond to the interconnections between subsystems. Considering faults that may occur on the interconnections and subsystems, as our first contribution, we propose a distributed scheme to jointly detect and isolate faults occurring in nodes and edges of the system. As our second contribution, we analyze the behavior of the proposed scheme under model uncertainties caused by the addition or removal of edges. Additionally, we propose a novel distributed FDI scheme based on local models and measurements that is resilient to changes outside of the local subsystem and achieves FDI. Our third contribution addresses the complexity reduction of the distributed FDI method, by characterizing the minimum amount of model information and measurements needed to achieve FDI and by reducing the number of monitoring nodes. The proposed methods can be fused to design a scalable and resilient distributed FDI architecture that achieves local FDI despite unknown changes outside the local subsystem. The proposed approach is illustrated by numerical experiments on the IEEE 118-bus power network benchmark. PMID:25222962

Teixeira, Andre; Shames, Iman; Sandberg, Henrik; Johansson, Karl H

2014-11-01

144

Advanced Fault Diagnosis Methods in Molecular Networks  

PubMed Central

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670

Habibi, Iman; Emamian, Effat S.; Abdi, Ali

2014-01-01

145

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

Microsoft Academic Search

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.

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

1992-01-01

146

Incipient fault detection and isolation of sensors and field devices  

NASA Astrophysics Data System (ADS)

The purpose of this research is to develop a robust fault detection and isolation method, for detecting faults in process sensors, actuators, controllers and other field devices. The approach to the solution to this problem is summarized below. A novel approach for the validation of control system components and sensors was developed in this research. The process is composed of detecting a system anomaly, isolating the faulty component (such as sensors, actuators, and controllers), computing its deviation from expected value for a given system's normal condition, and finally reconstructing its output when applicable. A variant of the Group Method of Data Handling (GMDH) was developed in this research for generating analytical redundancy from relationships among different system components. A rational function approximation was used for the data-driven modeling scheme. This analytical redundancy is necessary for detecting system anomalies and isolating faulty components. A rule-base expert system was developed in order to isolate the faulty component. The rule-based was established from model-simulated data. A fuzzy-logic estimator was implemented to compute the magnitude of the loop component fault so that the operator or the controller might take corrective actions. This latter engine allows the system to be operated in a normal condition until the next scheduled shutdown, even if a critical component were detected as degrading. The effectiveness of the method developed in this research was demonstrated through simulation and by implementation to an experimental control loop. The test loop consisted of a level control system, flow, pressure, level and temperature measuring sensors, motor-operated valves, and a pump. Commonly observed device faults were imposed in different system components such as pressure transmitters, pumps, and motor-operated valves. This research has resulted in a framework for system component failure detection and isolation, allowing easy implementation of this method in any process control system (power plants, chemical industry, and other manufacturing industry). The technique would also aid the plant personnel in defining the minimal number of sensors to be installed in a process system, necessary for reliable component validation.

Ferreira, Paulo Brasko

147

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

PubMed Central

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

Moghadas, Amin A.; Shadaram, Mehdi

2010-01-01

148

Fiber Bragg Grating sensor for fault detection in radial and network transmission lines.  

PubMed

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

Moghadas, Amin A; Shadaram, Mehdi

2010-01-01

149

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

NASA Astrophysics Data System (ADS)

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.

Moghadas, Amin

2011-12-01

150

Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet  

NASA Astrophysics Data System (ADS)

Due to the character of diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns into a challenging task. Multiwavelet with two or more base functions and many excellent properties provides a possibility to detect and extract all the features of compound faults at one time. However, the fixed basis functions independent of the vibration signal may decrease the accuracy of fault detection. Moreover, the decomposition result of discrete multiwavelet transform does not possess time invariance, which is harmful to extract the feature of periodical impulses. To overcome these deficiencies, based on the Hermite splines interpolation, taking the minimum envelope spectrum entropy as the optimization objective, adaptive redundant lifting multiwavelet is developed. Additionally, in order to eliminate error propagation of decomposition results, adaptive redundant lifting multiwavelet is improved by adding the normalization factors. As an effective method, Hilbert transform demodulation analysis is used to extract the fault feature from the high frequency modulation signal. The proposed method incorporating improved adaptive redundant lifting multiwavelet (IARLM) with Hilbert transform demodulation analysis is applied to compound faults detection for the simulation experiment, rolling element bearing test bench and traveling unit of electric locomotive. Compared with some other fault detection methods, the results show the superior effectiveness and reliability on the compound faults detection.

Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Yuan, Jing

2013-07-01

151

Composite Bending Box Section Modal Vibration Fault Detection  

NASA Technical Reports Server (NTRS)

One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden faults and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected faults in composite structures. Vibration data incorporating a broad frequency modal approach, could detect significant changes prior to failure. The purpose of this report is to investigate the usefulness of frequency mode testing before and after bending and torsion loading on a composite bending Box Test section. This test article is representative of construction techniques being developed for the recent NASA Blended Wing Body Low Speed Vehicle Project. The Box section represents the construction technique on the proposed blended wing aircraft. Modal testing using an impact hammer provides an frequency fingerprint before and after bending and torsional loading. If a significant structural discontinuity develops, the vibration response is expected to change. The limitations of the data will be evaluated for future use as a non-destructive in-situ method of assessing hidden damage in similarly constructed composite wing assemblies. Modal vibration fault detection sensitivity to band-width, location and axis will be investigated. Do the sensor accelerometers need to be near the fault and or in the same axis? The response data used in this report was recorded at 17 locations using tri-axial accelerometers. The modal tests were conducted following 5 independent loading conditions before load to failure and 2 following load to failure over a period of 6 weeks. Redundant data was used to minimize effects from uncontrolled variables which could lead to incorrect interpretations. It will be shown that vibrational modes detected failure at many locations when skin de-bonding failures occurred near the center section. Important considerations are the axis selected and frequency range.

Werlink, Rudy

2002-01-01

152

Fault diagnosis method for smart substation  

Microsoft Academic Search

This paper proposed a hierarchical model for smart substation fault diagnosis. This fault diagnosis system gets fault information from SCADA and fault information system. The information from SCADA, including network topology and switch state, is modeled based on IEC61970-CIM. The protection information from the fault information system is modelled based on IEC61850, then encapsulated CIM model. When a fault happens,

Zhanjun Gao; Qing Chen; Zhaofei Li

2011-01-01

153

Detection of feed-through faults in CMOS storage elements  

NASA Technical Reports Server (NTRS)

In testing sequential circuits, internal faults in the storage elements (SE's) are sometimes modeled as stuck-at faults in the combinational circuits surrounding the SE. The detection of some transistor-level faults that cannot be modeled as stuck-at are considered. These feed-through faults cause the cell to become either data-feed-through, which makes the cell combinational, or clock-feed-through, which causes the clock signal or its complement to appear at the output. Under such faults, the cell does not function as a memory element. Here it is shown that such faults may or may not be detected depending on delays involved. Conditions under which race-ahead occurs are identified.

Al-Assadi, Waleed K.; Malaiya, Yashwant K.; Jayasumana, Anura P.

1992-01-01

154

Guaranteed robust fault detection and isolation techniques for small satellites  

NASA Astrophysics Data System (ADS)

The paper presents two generic fault detection and isolation (FDI) techniques which have shown remarkable robustness when applied to the SIMULINK model of a small satellite for thruster failures. While fundamentally different in their design approach, they both generate ?structured residuals' which accurately capture the failure mode. The diagnosis criterion in both methods relies on residuals direction rather than magnitude, which avoids the delays and expense of setting accurate thresholds for residuals magnitudes. Most importantly, this fact can account for the enhanced robustness to disturbances and sensor noise, as well as to significant parametric variations. Extensive Monte Carlo simulations are presented validating the robust performance of the two algorithms.

Valavani, L.; Tantouris, N.

2013-12-01

155

Neural net application to transmission line fault detection and classification  

E-print Network

requires a fast and simple procedure for adapting to the changing power network conditions. 4. 3. The NN Approach Fault detection and classification is defined as a multiclass problem. The eleven types of faults (a-g, b-g, c-g, a-b, b-c. c-a, ab-g, bc...

Rikalo, Igor

2012-06-07

156

Intermittent Fault Detection and Isolation System  

Microsoft Academic Search

Aging aircraft electronic boxes often pose a maintenance challenge in that often after malfunctioning during flight in the aircraft, they test good, or ldquoNo Fault Foundrdquo (NFF) during ground test. The reason many of these boxes behave in this manner is that they have intermittent faults, which are momentary opens in one or more circuits due to a cracked solder

B. Steadman; F. Berghout; N. Olsen; B. Sorensen

2008-01-01

157

ASCS online fault detection and isolation based on an improved MPCA  

NASA Astrophysics Data System (ADS)

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.

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

2014-09-01

158

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards  

SciTech Connect

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 loss 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.

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

1994-02-01

159

Observer and data-driven-model-based fault detection in power plant coal mills  

SciTech Connect

This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain.

Odgaard, P.F.; Lin, B.; Jorgensen, S.B. [University of Aalborg, Aalborg (Denmark). Dept. of Electrical Systems

2008-06-15

160

Non-intrusive fault detection in reciprocating compressors  

E-print Network

This thesis presents a set of techniques for non-intrusive sensing and fault detection in reciprocating compressors driven by induction motors. The procedures developed here are "non-intrusive" because they rely only on ...

Schantz, Christopher James

2011-01-01

161

Fault detection in rotary blood pumps using motor speed response.  

PubMed

Clinical acceptance of ventricular assist devices (VADs) as long-term heart failure therapy requires safe and effective circulatory support for a minimum of 5 years. Yet, VAD failure beyond 2 years of support is still a concern. Currently, device controllers cannot consistently predict VAD failure modes, and undetected VAD faults may lead to catastrophic device failure. To minimize this risk, a model-based algorithm for reliable VAD fault detection that only requires VAD revolutions per minute (rpm) was developed. The algorithm was tested using computer models of the human cardiovascular system simulating heart failure and axial flow (AF) or centrifugal flow (CF) VADs. Ventricular assist device rpm was monitored after a step down of motor current for normal and simulated fault conditions (>750 faults). The ability to detect fault conditions with 1%, 5%, and 10% rpm measurement noise was evaluated. All failure modes affected the VAD rpm responses to the motor current step down. Fault detection rates were >95% for AF and >89% for CF VADs, even with 10% rpm measurement noise. The VAD rpm responses were significantly altered by blood viscosity (3.5-6.2 cP), which should be accounted for in clinical application. The proposed VAD fault detection algorithm may deliver a convenient and nonintrusive way to minimize catastrophic device failures. PMID:23820281

Soucy, Kevin G; Koenig, Steven C; Sobieski, Michael A; Slaughter, Mark S; Giridharan, Guruprasad A

2013-01-01

162

Soft Computing Application in Fault Detection of Induction Motor  

SciTech Connect

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.

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr. [Electrical Engineering Department, BESUS, Shibpur (India)

2010-10-26

163

Current-based sensorless detection of stator winding turn faults in induction machines  

NASA Astrophysics Data System (ADS)

To improve the reliability of motor-driven processes, condition monitoring of electric machines has received considerable attention from industry. For small- and medium-sized machines, the focus is on low-cost sensorless schemes that use only measured voltages and currents for fault diagnostics. Turn faults arising from stator winding insulation deterioration account for a large percentage of motor failures. The objective of a turn-fault detection scheme is to provide a warning before the fault propagates further and results in ground current, causing irreversible damage to the magnetic material. In this work, a neural-network-based robust scheme for early detection of turn faults in induction machines is developed. The negative-sequence component of line currents is used as the fault signature, and a neural network is trained to compensate for the effects of unbalanced supply voltages and nonidealities in the machine or instrumentation. Novel training algorithms for self-commissioning and on-line training of the neural network have also been developed. Experimental results, obtained on a specially-rewound machine, are provided to demonstrate that the method is capable of early fault detection. Data memory and computational requirements are also minimal, making the scheme viable for commercial implementation. The method is also extended to turn-fault detection in open-loop inverter-fed induction machines. Data obtained from a thermally accelerated insulation failure experiment is also used to test the performance and sensitivity of the method, and to show that a turn fault can be detected before failure of insulation to ground.

Tallam, Rangarajan M.

164

Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition  

Microsoft Academic Search

Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which

Luis Romeral; Juan A. Ortega; Javier A. Rosero

2008-01-01

165

Fault Detection of the Tennessee Eastman Process Using Improved PCA and Neural Classifier  

Microsoft Academic Search

\\u000a This paper describes hybrid multivariate method: Principal Component Analysis improved by Genetic Algorithm. This method determines\\u000a main Principal Components can be used to detect fault during the operation of industrial process by neural classifier. This\\u000a technique is applied to simulated data collected from the Tennessee Eastman chemical plant simulator which was designed to\\u000a simulate a wide variety of faults occurring

Mostafa Noruzi Nashalji; Mahdi Aliyari Shoorehdeli; Mohammad Teshnehlab

166

Enhanced Fault Detection of Rolling Element Bearing Based on Cepstrum Editing and Stochastic Resonance  

NASA Astrophysics Data System (ADS)

By signal pre-whitening based on cepstrum editing,the envelope analysis can be done over the full bandwidth of the pre-whitened signal, and this enhances the bearing characteristic frequencies. The bearing faults detection could be enhanced without knowledge of the optimum frequency bands to demodulate, however, envelope analysis over full bandwidth brings more noise interference. Stochastic resonance (SR), which is now often used in weak signal detection, is an important nonlinear effect. By normalized scale transform, SR can be applied in weak signal detection of machinery system. In this paper, signal pre-whitening based on cepstrum editing and SR theory are combined to enhance the detection of bearing fault. The envelope spectrum kurtosis of bearing fault characteristic components is used as indicators of bearing faults. Detection results of planted bearing inner race faults on a test rig show the enhanced detecting effects of the proposed method. And the indicators of bearing inner race faults enhanced by SR are compared to the ones without enhancement to validate the proposed method.

Zhang, Xiaofei; Hu, Niaoqing; Hu, Lei; Fan, Bin; Cheng, Zhe

2012-05-01

167

A knowledge base system for rotary equipment fault detection and diagnosis  

Microsoft Academic Search

This paper studies the fault detection and diagnosis for the most common faults in the rotary equipment. Large amount of experiments are carried out on the machinery fault simulator for simulating different types of rotary machine faults. The study covers from different type of data acquisition sensors, different signal processing and feature extraction techniques. A hierarchical rule-based fault detection system

Junhong Zhou; Louis Wee; Zhao-Wei Zhong

2010-01-01

168

Similarity Ratio Analysis for Early Stage Fault Detection with Optical Emission Spectrometer in Plasma Etching Process  

PubMed Central

A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes. PMID:24755865

Yang, Jie; McArdle, Conor; Daniels, Stephen

2014-01-01

169

Detection of signal transients based on wavelet and statistics for machine fault diagnosis  

NASA Astrophysics Data System (ADS)

This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov-Smirnov (K-S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed "step-by-step detection" based on K-S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K-S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.

Zhu, Z. K.; Yan, Ruqiang; Luo, Liheng; Feng, Z. H.; Kong, F. R.

2009-05-01

170

Auxiliary signal design in fault detection and diagnosis  

NASA Astrophysics Data System (ADS)

Fault-detection and diagnosis schemes for systems represented by linear MIMO stochastic models are developed analytically, with a focus on on the design and application of auxiliary signals. The basic principles of optimal-input design are reviewed, and consideration is given to the sequential probability ratio test (SPRT), auxiliary signals for improving SPRT fault detection, and the extension of the SPRT to multiple-hypothesis testing. Two chapters are devoted to the application of the SPRT to a model chemical plant (producing anhydrous caustic soda), including model derivation, model identification, detection of type I and type II faults, and the fault-diagnosis decision-making mechanism. Numerical results are presented in graphs and briefly characterized.

Zhang, Xue Jun

171

A fault detection service for wide area distributed computations.  

SciTech Connect

The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to tradeoff timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver.

Stelling, P.

1998-06-09

172

Predictive unsupervised organisation in marine engine fault detection  

Microsoft Academic Search

This paper utilises topological learners, the self organising map in combination with the k means algorithm to organise potential engine faults and the respective location of faults, focussing on a 12 cylinder 2 stroke marine diesel engine. This method is applied to reduce the numerosity of the data presented to a user by selecting representative samples from a number of

Ian Morgan; Honghai Liu; George Turnbull; David Brown

2008-01-01

173

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

PubMed Central

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

Li, Ke; Chen, Peng

2011-01-01

174

Optimal Sensor Allocation for Fault Detection and Isolation  

NASA Technical Reports Server (NTRS)

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.

Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

2004-01-01

175

Enhanced detection of rolling element bearing fault based on stochastic resonance  

NASA Astrophysics Data System (ADS)

Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.

Zhang, Xiaofei; Hu, Niaoqing; Cheng, Zhe; Hu, Lei

2012-11-01

176

STATISTICAL SIGNAL PROCESSING APPROACHES TO FAULT DETECTION  

E-print Network

processing. It is motivated by three applications that a simple CUSUM detector in feedback loop, principal component analysis, subspace identification 1. INTRODUCTION The parity space approach to fault as Princi- pal Component Analysis (PCA). · The use and fusion of residuals from several independent models

Gustafsson, Fredrik

177

Using a microcomputer in fault detection  

NASA Astrophysics Data System (ADS)

X-Ray radiography method is a typicial human-visual testing among NDT (nondestructive testing). Due to the labor's film-identified needs and the long practical experience, the skilled labors are hard to be trained. Not only the labor's skiliness and spiritness will affect the quality of the film-identified, hut also the qualtity parameters of flaw (e.g. the size of flaw) cannot be determined by labor within short time. However, computer vision image processing system can give some good characteristics ,such as, high speed, quantitative parameters and non-human's error etc. Developing this system to assist the labor's film-identified will be certainly assuring the film quality, meanwhile, it will be the most powerful method of on-line flaw testing in the future. This paper just focuses the research topic at the identification of X-ray film for the butt welding steel materials1. First, to analize the defect's image model in the X-ray film, then by the image processing technique to build up the propper edge detecting operator and the edge detecting rule, and finally, by the derived edge detector operator to do the mask operation to the X-ray film image, and to detect the flow contour from the segmented defect image for following identification and classification. In this study, we make use of the fuzzy pattern recognition2 and hierarchy classifier to identify the welding flaws.

Wen, Kun-Li; Wu, John H.

1993-09-01

178

Composite Bending Box section Modal Vibration fault Detection  

Microsoft Academic Search

Abstract: One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden faults and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected,faults in composite structures. Vibration data incorporating a broad frequency modal approach, could detect significant changes,prior to failure. The purpose,of this report is

Rudy Werlink

179

Robust PCA-Based Abnormal Traffic Flow Pattern Isolation and Loop Detector Fault Detection  

Microsoft Academic Search

One key function of intelligent transportation systems is to automatically detect abnormal traffic phenomena and to help further investigations of the cause of the abnormality. This paper describes a robust principal components analysis (RPCA)-based abnormal traffic flow pattern isolation and loop detector fault detection method. The results show that RPCA is a useful tool to distinguish regular traffic flow from

Xuexiang Jin; Yi Zhang; Li Li; Jianming Hu

2008-01-01

180

Development of a Surge Type Fault Locator using the Hybrid Detection Type Fault Recorder  

NASA Astrophysics Data System (ADS)

We have proposed the new surge type FL using hybrid detection type fault recorder to measure the location of a line fault precisely in the power system. This new recorder has a feature that the starting detection of the recorder is not a instantanceous value of a high speed data but a effective value of low sampling data. This new recorder has a high speed sampling part and a low speed sampling part. A high speed sampling part works a recording of surge data. On the other hand, a low speed sampling part works a detecting of trasmission line fault. The authors carried out field tests with this new recorder installed at plants and substation in the 275kV power transmission system of TEPCO (Tokyo Electric Power Company). And we obtained good results. This paper presents these results.

Urano, Shoichi; Yamada, Takeshi; Ooura, Yoshifumi; Xu, Youheng; Makimura, Tatsuya; Yamaguchi, Yasutaka

181

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

NASA Technical Reports Server (NTRS)

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.

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

182

A dynamic integrated fault diagnosis method for power transformers.  

PubMed

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

Gao, Wensheng; Bai, Cuifen; Liu, Tong

2015-01-01

183

A Dynamic Integrated Fault Diagnosis Method for Power Transformers  

PubMed Central

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

Gao, Wensheng; Liu, Tong

2015-01-01

184

Localized Fault-Tolerant Event Boundary Detection in Sensor Networks  

E-print Network

Localized Fault-Tolerant Event Boundary Detection in Sensor Networks Min Ding Dechang Chen Kai Xing networks with faulty sensors. Typical applications in- clude the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor

Cheng, Xiuzhen "Susan"

185

Detecting Faults In High-Voltage Transformers  

NASA Technical Reports Server (NTRS)

Simple fixture quickly shows whether high-voltage transformer has excessive voids in dielectric materials and whether high-voltage lead wires too close to transformer case. Fixture is "go/no-go" indicator; corona appears if transformer contains such faults. Nests in wire mesh supported by cap of clear epoxy. If transformer has defects, blue glow of corona appears in mesh and is seen through cap.

Blow, Raymond K.

1988-01-01

186

High Resolution Seismic Imaging of Fault Zones: Methods and Examples From The San Andreas Fault  

NASA Astrophysics Data System (ADS)

Seismic imaging of fault zones at shallow depths is challenging. Conventional seismic reflection methods do not work well in fault zones that consist of non-planar strata or that have large variations in velocity structure, two properties that occur in most fault zones. Understanding the structure and geometry of fault zones is important to elucidate the earthquake hazard associated with fault zones and the barrier effect that faults impose on subsurface fluid flow. In collaboration with the San Francisco Public Utilities Commission (SFPUC) at San Andreas Lake on the San Francisco peninsula, we acquired combined seismic P-wave and S-wave reflection, refraction, and guided-wave data to image the principal strand of the San Andreas Fault (SAF) that ruptured the surface during the 1906 San Francisco earthquake and additional fault strands east of the rupture. The locations and geometries of these fault strands are important because the SFPUC is seismically retrofitting the Hetch Hetchy water delivery system, which provides much of the water for the San Francisco Bay area, and the delivery system is close to the SAF at San Andreas Lake. Seismic reflection images did not image the SAF zone well due to the brecciated bedrock, a lack of layered stratigraphy, and widely varying velocities. Tomographic P-wave velocity images clearly delineate the fault zone as a low-velocity zone at about 10 m depth in more competent rock, but due to soil saturation above the rock, the P-waves do not clearly image the fault strands at shallower depths. S-wave velocity images, however, clearly show a diagnostic low-velocity zone at the mapped 1906 surface break. To image the fault zone at greater depths, we utilized guided waves, which exhibit high amplitude seismic energy within fault zones. The guided waves appear to image the fault zone at varying depths depending on the frequency of the seismic waves. At higher frequencies (~30 to 40 Hz), the guided waves show strong amplification at the 1906 surface break and at about 20 m to the east, but at lower frequencies (2-5 Hz), the guided waves show strong amplification approximately 10 m east of the 1906 surface break. We attribute the difference in amplification of guided waves to an east-dipping fault strand that merges with other strands below about 10 m depth. Vp/Vs and Poisson's ratios clearly delineate multiple fault strands about 2 km north of the mapped 1906 surface break at the SFPUC intake structure. Combining these fault-imaging methods provide a powerful set of tools for mapping fault zones in the shallow subsurface in areas of complex geology.

Catchings, R. D.; Rymer, M. J.; Goldman, M.; Prentice, C. S.; Sickler, R. R.; Criley, C.

2011-12-01

187

Method and apparatus for fault tolerance  

NASA Technical Reports Server (NTRS)

A method and apparatus for achieving fault tolerance in a computer system having at least a first central processing unit and a second central processing unit. The method comprises the steps of first executing a first algorithm in the first central processing unit on input which produces a first output as well as a certification trail. Next, executing a second algorithm in the second central processing unit on the input and on at least a portion of the certification trail which produces a second output. The second algorithm has a faster execution time than the first algorithm for a given input. Then, comparing the first and second outputs such that an error result is produced if the first and second outputs are not the same. The step of executing a first algorithm and the step of executing a second algorithm preferably takes place over essentially the same time period.

Masson, Gerald M. (Inventor); Sullivan, Gregory F. (Inventor)

1993-01-01

188

Performance monitoring, fault detection, and diagnosis of reciprocating chillers  

SciTech Connect

This paper presents a methodology that uses a combination of techniques: thermodynamic modeling, pattern recognition, and expert knowledge to determine the health of a reciprocating chiller and to diagnose selected faults. The system is composed of three modules. The first one deals with the detection of faults that are more discernible when the chiller is off, such as sensor drift. The second module detects faults during start-up and deals with those related to refrigerant flow characteristics, which are generally more apparent during the transient period. Finally, the third module detects deterioration in performance followed by diagnosis when the unit is operating in a steady-state condition. The approach has been experimentally tested on one laboratory unit and results presented. It is emphasized that further data are required to establish the repeatability of the emerging patterns and validate the applicability of the approach to reciprocating chillers in general.

Stylianou, M.; Nikanpour, D. [EDRL-CANMET, Varennes, Quebec (Canada)

1996-11-01

189

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

PubMed Central

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

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

2014-01-01

190

Experimental analysis of change detection algorithms for multitooth machine tool fault detection  

NASA Astrophysics Data System (ADS)

This paper describes an industrial application of fault diagnosis method for a multitooth machine tool. Different statistical approaches have been used to detect and diagnose insert breakage in multitooth tools based on the analysis of electrical power consumption of the tool drives. Great effort has been made to obtain a robust method, able to avoid any needed re-calibration process, after, for example, a maintenance operation. From the point of view of maintenance costs, these multitooth tools are the most critical part of the machine tools used for mass production in the car industry. These tools integrate different kinds of machining operations and cutting conditions.

Reñones, Aníbal; de Miguel, Luis J.; Perán, José R.

2009-10-01

191

Detection, identification, and quantification of sensor fault in a sensor network  

NASA Astrophysics Data System (ADS)

In structural health monitoring (SHM) and control, the structure can be instrumented with an array of sensors forming a redundant sensor network, which can be utilized in sensor fault diagnosis. In this study, the objective is to detect, identify, and quantify a sensor fault using the structural response data measured with the sensor network. Seven different sensor fault types are investigated and modelled: bias, gain, drifting, precision degradation, complete failure, noise, and constant with noise. The sensor network is modelled as a Gaussian process and each sensor in the network is estimated in turn using the minimum mean square error (MMSE) estimation The sensor fault is identified and quantified using the multiple hypothesis test utilizing the generalized likelihood ratio (GLR). The proposed approach is experimentally verified with an array of accelerometers assembled on a wooden bridge. Different sensor faults are simulated by modifying a single sensor. The method is able to detect a sensor fault, identify and correct the faulty sensor, as well as identify and quantify the fault type.

Kullaa, Jyrki

2013-10-01

192

Fault detection and isolation for multisensor navigation systems  

NASA Technical Reports Server (NTRS)

Increasing attention is being given to the problem of erroneous measurement data for multisensor navigation systems. A recursive estimator can be used in conjunction with a 'snapshot' batch estimator to provide fault detection and isolation (FDI) for these systems. A recursive estimator uses past system states to form a new state estimate and compares it to the calculated state based on a new set of measurements. A 'snapshot' batch estimator uses a set of measurements collected simultaneously and compares solutions based on subsets of measurements. The 'snapshot' approach requires redundant measurements in order to detect and isolate faults. FDI is also referred to as Receiver Autonomous Integrity Monitoring (RAIM).

Kline, Paul A.; Vangraas, Frank

1991-01-01

193

Application of classification functions to chiller fault detection and diagnosis  

SciTech Connect

This paper describes the application of a statistical pattern recognition algorithm (SPRA) to fault detection and diagnosis of commercial reciprocating chillers. The developed fault detection and diagnosis module has been trained to recognize five distinct conditions, namely, normal operation, refrigerant leak, restriction in the liquid refrigerant line, and restrictions in the water circuits of the evaporator and condenser. The algorithm used in the development is described, and the results of its application to an experimental test bench are discussed. Experimental results show that the SPRA provides an effective way of classifying patterns in multivariable, multiclass problems without having to explicitly use a rule-based system.

Stylianou, M. [EDRL-CANMET, Varennes, Quebec (Canada)

1997-12-31

194

Optimization-based tuning of LPV fault detection filters for civil transport aircraft  

NASA Astrophysics Data System (ADS)

In this paper, a two-step optimal synthesis approach of robust fault detection (FD) filters for the model based diagnosis of sensor faults for an augmented civil aircraft is suggested. In the first step, a direct analytic synthesis of a linear parameter varying (LPV) FD filter is performed for the open-loop aircraft using an extension of the nullspace based synthesis method to LPV systems. In the second step, a multiobjective optimization problem is solved for the optimal tuning of the LPV detector parameters to ensure satisfactory FD performance for the augmented nonlinear closed-loop aircraft. Worst-case global search has been employed to assess the robustness of the fault detection system in the presence of aerodynamics uncertainties and estimation errors in the aircraft parameters. An application of the proposed method is presented for the detection of failures in the angle-of-attack sensor.

Ossmann, D.; Varga, A.

2013-12-01

195

Adaptive redundant multiwavelet denoising with improved neighboring coefficients for gearbox fault detection  

NASA Astrophysics Data System (ADS)

Gearbox fault detection under strong background noise is a challenging task. It is feasible to make the fault feature distinct through multiwavelet denoising. In addition to the advantage of multi-resolution analysis, multiwavelet with several scaling functions and wavelet functions can detect the different fault features effectively. However, the fixed basis functions not related to the given signal may lower the accuracy of fault detection. Moreover, the multiwavelet transform may result in Gibbs phenomena in the step of reconstruction. Furthermore, both traditional term-by-term threshold and neighboring coefficients do not consider the direct spatial dependency of wavelet coefficients at adjacent scale. To overcome these deficiencies, adaptive redundant multiwavelet (ARM) denoising with improved neighboring coefficients (NeighCoeff) is proposed. Based on symmetric multiwavelet lifting scheme (SMLS), taking kurtosis—partial envelope spectrum entropy as the evaluation objective and genetic algorithms as the optimization method, ARM is proposed. Considering the intra-scale and inter-scale dependency of wavelet coefficients, the improved NeighCoeff method is developed and incorporated into ARM. The proposed method is applied to both the simulated signal and the practical gearbox vibration signal under different conditions. The results show its effectiveness and reliance for gearbox fault detection.

Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Wang, Xiaodong

2013-07-01

196

Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants  

Microsoft Academic Search

The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory

Anand P. Narayan

1998-01-01

197

Fault Detection with Bayesian Network Verron Sylvain, Tiplica Teodor, Kobi Abdessamad  

E-print Network

18 Fault Detection with Bayesian Network Verron Sylvain, Tiplica Teodor, Kobi Abdessamad LASQUO, at each instant, the theoretical value of each sensor can be known for the normal operating state: the fault detection, the fault diagnosis and the process recovery. Many data-driven techniques for the fault

Paris-Sud XI, Université de

198

Real-time fault detection and isolation in biological wastewater treatment plants  

E-print Network

Real-time fault detection and isolation in biological wastewater treatment plants F. Baggiani and S@dsi.unifi.it Automatic fault detection is becoming increasingly important in wastewater treatment plant operation, given of an unchecked fault propagating through the plant. This paper describes the development of a real-time Fault

199

Method and system for environmentally adaptive fault tolerant computing  

NASA Technical Reports Server (NTRS)

A method and system for adapting fault tolerant computing. The method includes the steps of measuring an environmental condition representative of an environment. An on-board processing system's sensitivity to the measured environmental condition is measured. It is determined whether to reconfigure a fault tolerance of the on-board processing system based in part on the measured environmental condition. The fault tolerance of the on-board processing system may be reconfigured based in part on the measured environmental condition.

Copenhaver, Jason L. (Inventor); Jeremy, Ramos (Inventor); Wolfe, Jeffrey M. (Inventor); Brenner, Dean (Inventor)

2010-01-01

200

Fault Detection of Rotating Machinery using the Spectral Distribution Function  

NASA Technical Reports Server (NTRS)

The spectral distribution function is introduced to characterize the process leading to faults in rotating machinery. It is shown to be a more robust indicator than conventional power spectral density estimates, but requires only slightly more computational effort. The method is illustrated with examples from seeded gearbox transmission faults and an analytical model of a defective bearing. Procedures are suggested for implementation in realistic environments.

Davis, Sanford S.

1997-01-01

201

A method of fault analysis for test generation and fault diagnosis  

Microsoft Academic Search

The authors present a fault coverage analysis method for test generation and fault diagnosis of large combinational circuits. Input vectors are analyzed in pairs in two steps using a 16-valued logic system, GEMINI. Forward propagation is performed to determine, for each line in the network, the set of all possible values it can take if the network contains any single

Henry Cox; Janusz Rajski

1988-01-01

202

Coulomb and viscous friction fault detection with application to a pneumatic actuator  

Microsoft Academic Search

Generally, fault detection is the process of monitoring a physical dynamic system accompanied by conformation and assessment of any degradation of system performance. These systems are modelled and terms that are representative of a specific fault are identified and monitored for detection. In this paper, a fault detection algorithm is developed to isolate and detect friction changes in a high

W. B. Dunbar; R. A. de Callafon; J. B. Kosmatka

2001-01-01

203

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

NASA Technical Reports Server (NTRS)

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.

Tejada, Arturo

2009-01-01

204

Early Oscillation Detection for DC/DC Converter Fault Diagnosis  

NASA Technical Reports Server (NTRS)

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.

Wang, Bright L.

2011-01-01

205

POD Model Reconstruction for Gray-Box Fault Detection  

NASA Technical Reports Server (NTRS)

Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

Park, Han; Zak, Michail

2007-01-01

206

Light emitting diode fault detection using p-n junction photovoltaic effect.  

PubMed

This paper proposes an online noncontact fault detection method during light emitting diode (LED) chip packaging, which is based on the photovoltaic effect in p-n junctions. Once a LED chip bonded on a lead frame is illuminated, the photocurrent will flow through the loop circuits formed by the lead frame. Through characterization of the weak photovoltaic response in the lead frame with the 20 LED chips, five LED faults, including chip defects (chip quality and chip contamination) and bonding deficiencies (disconnection, debonding, and rebonding), can be detected before packaging. A high-sensitivity photocurrent detection instrument has been developed to detect different color (red, yellow, green, and blue) and different size LED chips (9-15 mil) on LED assembly line. A key feature of the new instrument is the capability to tune and implement the maximum output power (photocurrent) in the loop lead frame by designing the high-efficiency magnetic core, the magnetic coil and the detecting system. Experiments demonstrate that the photovoltaic behaviors for LED p-n junctions are directly related to the LED electroluminescent characteristics, and the internal optoelectronic characteristics and the external Ohmic contact performances can be derived by detecting the photocurrent of LED chips. The LED online noncontact fault detection instrument based on the photovoltaic effect can be used to substitute for the ordinary electroluminescence online contact fault detection instrument. PMID:19485535

Li, Ping; Wen, Yumei; Cai, Youhai; Li, Lian

2009-05-01

207

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation  

NASA Technical Reports Server (NTRS)

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.

Ferrell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Brown, Barbara

2010-01-01

208

SENSOR CLASSIFICATION FOR THE FAULT DETECTION AND ISOLATION PROBLEM  

E-print Network

SENSOR CLASSIFICATION FOR THE FAULT DETECTION AND ISOLATION PROBLEM Christian Commault Jean is solvable with the existing sensors, we will classify these sensors according to their importance for the solvability of the considered FDI problem. The sensors will be classified into essential sensors, useless

Paris-Sud XI, Université de

209

Multiple Sensor Fault Detection in Heat Exchanger Systems  

E-print Network

Multiple Sensor Fault Detection in Heat Exchanger Systems Abdelwahab Aïtouche* , Didier Maquin strategy is presented for a heat exchanger system for which the process model consists of a set of linear equations, and can be found in heat exchangers. The increasing importance of heat exchangers in the industry

Paris-Sud XI, Université de

210

Fault Detection Effectiveness of Spathic Test Data Jane Huffman Hayes  

E-print Network

Fault Detection Effectiveness of Spathic Test Data Jane Huffman Hayes Computer Science Department This paper presents an approach for generating test data for unit-level, and possibly integration-level, testing based on sampling over intervals of the input probability distribution, i.e., one that has been

Hayes, Jane E.

211

Aircraft Power Generators: Hybrid Modeling and Simulation for Fault Detection  

Microsoft Academic Search

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation (FDI) are necessary components for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing FDI techniques for the electrical subsystem (brushless generator) are

Ashraf Tantawy; Xenofon Koutsoukos; Gautam Biswas

2012-01-01

212

Spare capacity as a means of fault detection and diagnosis in multiprocessor systems  

SciTech Connect

A technique is described for detecting and diagnosing faults at the processor level in a multiprocessor system. In this method, a process is assigned whenever possible to two processors: the processor that it would normally be assigned to (primary) and an additional processor which would otherwise be idle (secondary). Two strategies are described and analyzed: one which is preemptive and another which is nonpreemptive. It is shown that for moderately loaded systems, a sufficient percentage of processes can be performed redundantly using the system's spare capacity to provide a basis for fault detection and diagnosis with virtually no degradation of response time. A multiprocessor is described which uses the approach for detecting faults at the processor level.

Dahbura, A.T.; Sabnani, K.K.

1989-06-01

213

Fault detection and diagnosis in an industrial fed-batch cell culture process.  

PubMed

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. PMID:17672519

Gunther, Jon C; Conner, Jeremy S; Seborg, Dale E

2007-01-01

214

Development of an Automated Fault Detection and Diagnosis tool for AHU's  

E-print Network

-commissioning HVAC systems to rectify faulty operation with savings of over 20 percent of total energy cost possible by continuously commissioning. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults...

Bruton, K.; Raftery, P.; Aughney, N.; Keane, M.; O'Sullivan, D.

2012-01-01

215

Fault detection and exclusion in multisensor navigation systems  

NASA Technical Reports Server (NTRS)

In order for a multisensor navigation system to meet integrity requirements, there must be a way of detecting erroneous measurements, using only data from those measurements. This can be accomplished using a parity space estimation algorithm. Erroneous measurements must then be removed from the position solution; the entire process is called fault detection and exclusion (FDE). A baseline FDE algorithm has been determined, and is capable of working in real time on present affordable hardware.

Bernath, Gregory N.

1995-01-01

216

Development of parameter based fault detection and diagnosis technique for energy efficient building management system  

Microsoft Academic Search

This paper presents a complete methodology for detection and diagnosis of faults in variable air volume air handling units. Three cases are considered: (a) an off-line fault detection technique for existing buildings, (b) an automatic on-line fault detection technique for integration in building management systems (BMSs) of upcoming not very complex buildings and (c) an automatic on-line fault detection as

Sanjay Kumar; S. Sinha; T. Kojima; H. Yoshida

2001-01-01

217

Robust fault detection for LPV systems using interval observers and zonotopes  

Microsoft Academic Search

In this paper, the problem of robust fault detection using an interval observer for dynamic systems characterized by LPV (linear parameter varying) models is presented. The observer faces the robustness problem using two complementary strategies. Modeling uncertainties are considered unknown but bounded by intervals. Their effect is addressed using an interval state observation method based on zonotope representation of the

Fatiha Nejjari; Vicenç Puig; Saúl Montes de Oca; Atefeh Sadeghzadeh

2009-01-01

218

Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection  

Microsoft Academic Search

Although a variety of methods have been proposed in the literature for machine fault detection, it still remains a challenge to extract prominent features from random and nonstationary vibratory signals, a typical representative of which are the resonance signatures generated by incipient defects on the rolling elements of ball bearings. Due to its random and nonstationary nature, the involved signal

Jie Liu

2012-01-01

219

Light emitting diode fault detection using p-n junction photovoltaic effect  

Microsoft Academic Search

This paper proposes an online noncontact fault detection method during light emitting diode (LED) chip packaging, which is based on the photovoltaic effect in p-n junctions. Once a LED chip bonded on a lead frame is illuminated, the photocurrent will flow through the loop circuits formed by the lead frame. Through characterization of the weak photovoltaic response in the lead

Ping Li; Yumei Wen; Youhai Cai; Lian Li

2009-01-01

220

Plant Monitoring and Fault Detection : Synergy between Data Reconciliation and Principal Component Analysis  

Microsoft Academic Search

Data reconciliation and principal component analysis are two recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). Principal component analysis can then be applied to raw process data for monitoring purpose.

G. Heyen; B. Kalitventzeff

221

Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery  

NASA Astrophysics Data System (ADS)

State identification to diagnose the condition of rotating machinery is often converted to a classification problem of values of non-dimensional symptom parameters (NSPs). To improve the sensitivity of the NSPs to the changes in machine condition, a novel feature extraction method based on adaptive multiwavelets and the synthetic detection index (SDI) is proposed in this paper. Based on the SDI maximization principle, optimal multiwavelets are searched by genetic algorithms (GAs) from an adaptive multiwavelets library and used for extracting fault features from vibration signals. By the optimal multiwavelets, more sensitive NSPs can be extracted. To examine the effectiveness of the optimal multiwavelets, conventional methods are used for comparison study. The obtained NSPs are fed into K-means classifier to diagnose rotor faults. The results show that the proposed method can effectively improve the sensitivity of the NSPs and achieve a higher discrimination rate for rotor fault diagnosis than the conventional methods.

Lu, Na; Xiao, Zhihuai; Malik, O. P.

2015-02-01

222

Probabilistic approaches to fault detection in networked discrete event systems.  

PubMed

In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example. PMID:16252815

Athanasopoulou, Eleftheria; Hadjicostis, Christoforos N

2005-09-01

223

IMPLEMENTION AND TESTING OF A FAULT DETECTION SOFTWARE TOOL FOR IMPROVING CONTROL SYSTEM  

E-print Network

IMPLEMENTION AND TESTING OF A FAULT DETECTION SOFTWARE TOOL FOR IMPROVING CONTROL SYSTEM detect faults in the controlled process. We present results from the first phase of tests being carried out in a large commercial building to evaluate the fault detection capability of the control scheme

Diamond, Richard

224

STATE OF CALIFORNIA AUTOMATIC FAULT DETECTION AND DIAGNOSTICS (FDD) FOR AIR HANDLING UNITS  

E-print Network

STATE OF CALIFORNIA AUTOMATIC FAULT DETECTION AND DIAGNOSTICS (FDD) FOR AIR HANDLING UNITS AND ZONE OF ACCEPTANCE MECH-13A NA7.5.12 Automatic Fault Detection and Diagnostics (FDD) for Air Handling Units and Zone AUTOMATIC FAULT DETECTION AND DIAGNOSTICS (FDD) FOR AIR HANDLING UNITS AND ZONE TERMINAL UNITS ACCEPTANCE

225

Fault Detection and Diagnosis in TurbineEngines using Fuzzy Logic  

E-print Network

Fault Detection and Diagnosis in TurbineEngines using Fuzzy Logic Dennice Gayme Sunil Menon Charles.Mukavetz @honevwell.com Abstract In thispaper, wepresent ajiazy Iogic basedmethod of fault detection and diagnosis fault detection and diagnosis (FDD) is vitally important to reducing airline operating costs

Gayme, Dennice

226

Inversion method of seismic forces at fault using finite element  

NASA Astrophysics Data System (ADS)

Fault slip inversion using seismic dislocation model has been discussed a lot. In this model, seismogenic fault is considered as an interface. However, geological surveys and seismic channel waves reveal that the fault usually possesses thickness. Rock compression tests also show that micro-cracks develop into a belt in which shear fracture plane takes place. Therefore, to simulate the fault as a narrow belt may be more reasonable to reflect mechanical behavior of earthquake source. This study proposes a method to inverse seismic forces at the fault with thickness. The fault is modeled by transversely isotropic material. Three-dimensional finite element models (FEMs) is used to calculate numerical Green's functions for displacements. The Green's functions are generated by imposing unit couples directly to the node pairs at the fault instead of dislocation. The unit couples are added separately in x, y, z directions of the finite element global coordinate system. A pure thrust earthquake is modeled by reducing shear modulus under tectonic stress field. Selected surface displacements induced by this earthquake are used as 'observation data' of the inversion. We combine numerical Green's functions with standard linear inverse methods with Laplace smoothing constraints to estimate seismic forces at the fault. The earthquake which is simulated by damage of shear modulus has the fault model with transversely isotropic material, therefore there exist no normal forces. When the fault material is isotropic and the earthquake is caused by the reduction of shear or Young's modulus, there are normal forces at the fault. This study shows that we can directly inverse three-dimensional seismic forces with the surface deformation caused by earthquakes. This method is feasible for heterogeneous materials and complicated geometry model. [1] Xie, Zhoumin, Inversion method of seismic stress drop by finite element scheme, Doctor Thesis, Peking University, 2013. [2] Hu, C., Zhou, Y., & Cai, Y., A new finite element model in studying earthquake triggering and continuous evolution of stress field, Science in China Series D: Earth Sciences, 2009

Liu, D.; Xie, Z.; Geng, W.; Cai, Y.

2013-12-01

227

Incipient fault detection study for advanced spacecraft systems  

NASA Technical Reports Server (NTRS)

A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.

Milner, G. Martin; Black, Michael C.; Hovenga, J. Mike; Mcclure, Paul F.

1986-01-01

228

Seismic slip propagation along a fault in the Shimanto accretionary prism detected by vitrinite reflectance studies  

NASA Astrophysics Data System (ADS)

Quantitative assessment of heat generation along faults during fault movement is of primary importance in understanding the dynamics of earthquakes. Last several years localized heat anomaly in a fault zone due to rapid seismic sliding has been detected by various analyses of fault zone materials, such as ferromagnetic resonance signal (Fukuchi et al., 2005), trace elements and isotopes (e.g., Ishikawa et al., 2008) and mineralogical change of clay (e.g., Hirono et al., 2008) and vitrinite reflectance (O'Hara, 2004). Here we report a heat anomaly found in a fault zone in the Shimanto accretionary complex by vitrinite reflectance measurements. Mature faults in nature mostly experience multiple seismic events, resulting in integrated heat anomaly. Thus, in addition to vitrinite reflectance measurements across natural faults, we performed high-velocity friction experiments on a mixture of quartz and vitrinite grains to evaluate how multiple rapid-slip events affect vitrinite reflectance in a fault zone. A localized heat anomaly is found in one of fault zones which are developed within a mélange unit in the Cretaceous Shimanto belt, SW Japan. A principle slip zone with thickness of ~5 mm forms within cataclastic damage zone with thickness of ~3 m. The slip zone is mainly composed of well-foliated clay minerals. Host rocks are characterized by a block-in-matrix texture: aligned sandstone and chert blocks embedded in mudstone matrix. We measured vitrinite reflectance across the fault zone by the same method as reported in Sakaguchi et al., (2011). The measurement reveals that the principle slip zone underwent localized temperature of more than 220°C, while background temperature of both damage zone and host rocks is ~170°C. Since fault motion along most active faults occurs seismological, that inevitably generates frictional heat, the localized heat anomaly is possibly caused by the rapid seismic slip. In order to evaluate the change in vitrinite reflectance by coseismic sliding, we conducted friction experiments on a mixture of 90 wt% quartz and 10 wt% vitrinite at slip rates of 1.3 mm/s and 1.3 m/s, normal stress of 1.0 MPa and displacement of 15 m under anoxic, nitrogen atmosphere. A series of slide-hold-slide tests are also performed to reproduce multiple seismic-slip events. Our preliminary observation of recovered specimens indicated that significant heat anomaly, especially at shear localized zone in the simulated gouge zone, can be detected by vitrinite reflectance measurement. Detailed results will be reported in our presentation.

Kitamura, M.; Mukoyoshi, H.; Hirose, T.

2011-12-01

229

A new method for fault feature extraction of analog circuit  

NASA Astrophysics Data System (ADS)

The feature extraction is one of key steps in fault diagnosis. A method of least square support vector machine (LSSVM) is put forward based on genetic optimization to restrain the end effect of empirical mode decomposition (EMD). Based on the method, a method of feature extraction is put forward. The energy of intrinsic mode functions (IMF) generated from EMD is to be the feature to distinguishing faults. The result of feature extraction experiment of analog circuit shows that the method is effective.

Hou, Qingjian; Wang, Hongli

2008-10-01

230

Sensor configuration and test for fault diagnoses of subway braking system based on signed digraph method  

NASA Astrophysics Data System (ADS)

Fault diagnosis of various systems on rolling stock has drawn the attention of many researchers. However, obtaining an optimized sensor set of these systems, which is a prerequisite for fault diagnosis, remains a major challenge. Available literature suggests that the configuration of sensors in these systems is presently dependent on the knowledge and engineering experiences of designers, which may lead to insufficient or redundant development of various sensors. In this paper, the optimization of sensor sets is addressed by using the signed digraph (SDG) method. The method is modified for use in braking systems by the introduction of an effect-function method to replace the traditional quantitative methods. Two criteria are adopted to evaluate the capability of the sensor sets, namely, observability and resolution. The sensors configuration method of braking system is proposed. It consists of generating bipartite graphs from SDG models and then solving the set cover problem using a greedy algorithm. To demonstrate the improvement, the sensor configuration of the HP2008 braking system is investigated and fault diagnosis on a test bench is performed. The test results show that SDG algorithm can improve single-fault resolution from 6 faults to 10 faults, and with additional four brake cylinder pressure (BCP) sensors it can cover up to 67 double faults which were not considered by traditional fault diagnosis system. SDG methods are suitable for reducing redundant sensors and that the sensor sets thereby obtained are capable of detecting typical faults, such as the failure of a release valve. This study investigates the formal extension of the SDG method to the sensor configuration of braking system, as well as the adaptation supported by the effect-function method.

Zuo, Jianyong; Chen, Zhongkai

2014-05-01

231

Compression of test responses techniques in fault detection and diagnosis  

SciTech Connect

The compressor's total error-masking probability Q{sub t} (the expected probability that manifestation of faults as errors in a test response will be undetected under the compression process) is defined. It follows that the drawback of the linear feedback shift register (LFSR) compressors reside in the evaluation of Q{sub t} which involved laborious computation in obtaining the distribution of errors. However, due to the simple structure of a programmable logic array (PLA), it is shown that almost all single cross-point faults in PLAs are detected by the test procedure using LFSRs such that the distribution of errors need not be obtained. A quadratic compression technique is developed to overcome the drawback of the LFSRs technique. The approach is based the concept of robust compressors which incorporates the prior knowledge on the statistics of fault-free responses to achieve a guaranteed error-masking probability independent on distributions of errors. A construction of optimal codes for the minimax criterion on error detection based on bent functions is presented. Quadratic codes provide equal protection against all error patterns, hence, offer an efficient technique for design of fault-tolerant-computing VLSI devices.

Nagvajara, P.

1989-01-01

232

Detection of High-impedance Arcing Faults in Radial Distribution DC Systems  

NASA Technical Reports Server (NTRS)

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

Gonzalez, Marcelo C.; Button, Robert M.

2003-01-01

233

Fault Detection and Isolation for Hydraulic Control  

NASA Technical Reports Server (NTRS)

Pressure sensors and isolation valves act to shut down defective servochannel. Redundant hydraulic system indirectly senses failure in any of its electrical control channels and mechanically isolates hydraulic channel controlled by faulty electrical channel so flat it cannot participate in operating system. With failure-detection and isolation technique, system can sustains two failed channels and still functions at full performance levels. Scheme useful on aircraft or other systems with hydraulic servovalves where failure cannot be tolerated.

1987-01-01

234

Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection  

NASA Astrophysics Data System (ADS)

Although a variety of methods have been proposed in the literature for machine fault detection, it still remains a challenge to extract prominent features from random and nonstationary vibratory signals, a typical representative of which are the resonance signatures generated by incipient defects on the rolling elements of ball bearings. Due to its random and nonstationary nature, the involved signal generally possesses a low signal-to-noise ratio, where the classical signal processing methods cannot be effectively applied and the extracted features are usually submerged into the severe background noise. In this paper, a novel random and nonstationary vibratory signature analysis (R&N-VSA) technique is presented to address this challenge. The original vibration signal is decomposed into fault-related and non-fault-related signal segments, and multi-level exponential moving average power filtering is suggested to guide this decomposition. Instead of analyzing the whole vibratory signal, the developed Shannon wavelet spectrum analysis is more efficiently applied on the truncated fault-related signal segments so as to enhance the features' characteristics. The effectiveness of the proposed technique is examined through a series of tests with two experimental setups, and the investigation results show that the developed R&N-VSA technique is an effective signal processing technique for incipient machine fault detection.

Liu, Jie

2012-05-01

235

Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station  

NASA Technical Reports Server (NTRS)

We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

Lee, Charles; Alena, Richard L.; Robinson, Peter

2004-01-01

236

Application of fault detection techniques to spiral bevel gear fatigue data  

NASA Technical Reports Server (NTRS)

Results of applying a variety of gear fault detection techniques to experimental data is presented. A spiral bevel gear fatigue rig was used to initiate a naturally occurring fault and propagate the fault to a near catastrophic condition of the test gear pair. The spiral bevel gear fatigue test lasted a total of eighteen hours. At approximately five and a half hours into the test, the rig was stopped to inspect the gears for damage, at which time a small pit was identified on a tooth of the pinion. The test was then stopped an additional seven times throughout the rest of the test in order to observe and document the growth and propagation of the fault. The test was ended when a major portion of a pinion tooth broke off. A personal computer based diagnostic system was developed to obtain vibration data from the test rig, and to perform the on-line gear condition monitoring. A number of gear fault detection techniques, which use the signal average in both the time and frequency domain, were applied to the experimental data. Among the techniques investigated, two of the recently developed methods appeared to be the first to react to the start of tooth damage. These methods continued to react to the damage as the pitted area grew in size to cover approximately 75% of the face width of the pinion tooth. In addition, information gathered from one of the newer methods was found to be a good accumulative damage indicator. An unexpected result of the test showed that although the speed of the rig was held to within a band of six percent of the nominal speed, and the load within eighteen percent of nominal, the resulting speed and load variations substantially affected the performance of all of the gear fault detection techniques investigated.

Zakrajsek, James J.; Handschuh, Robert F.; Decker, Harry J.

1994-01-01

237

Low cost fault detection system for railcars and tracks  

E-print Network

for the degree of MASTER OF SCIENCE Approved as to style and content by: _______________________ Reza Langari (Chair of Committee) _______________________ _______________________ Sooyong Lee Paul... and Tracks. (August 2003) Sriram T. Vengalathur, B.E., B.M.S.C.E. (University of Bangalore), India Chair of Advisory Committee: Dr. Reza Langari A ?low cost fault detection system? that identifies wheel flats and defective tracks is explored here...

Vengalathur, Sriram T.

2004-09-30

238

A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection  

NASA Astrophysics Data System (ADS)

A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection is developed. The coupled rotor/fuselage/vibration absorbers (bifilar type) system incorporates consistent structural, aerodynamic and inertial couplings. The aeroelastic analysis is based on finite element methods in space and time. The coupled rotor, absorbers and fuselage equations are transformed into the modal space and solved in the fixed coordinate system. A coupled trim procedure is used to solve the responses of rotor, fuselage and vibration absorber, rotor trim control and vehicle orientation simultaneously. Rotor system faults are modeled by changing blade structural, inertial and aerodynamic properties. Both adjustable and component faults, such as misadjusted trim-tab, misadjusted pitch-control rod (PCR), imbalanced mass and pitch-control bearing freeplay, are investigated. Detailed SH-60 helicopter fuselage NASTRAN model is integrated into the analysis. Validation study was performed using SH-60 helicopter flight test data. The prediction of fuselage natural frequencies show fairly large error compared to shake test data. Analytical predictions of fuselage baseline (without fault) 4/rev vibration and fault-induced 1/rev vibration and blade displacement deviations are compared with SH-60 flight test (with prescribed fault) data. The fault-induced 1/rev fuselage vibration (magnitude and phase) predicted by present analysis generally capture the trend of the flight test data, although prediction under-predicts. The large discrepancy of fault-induced 1/rev vibration magnitude at hover between prediction and flight test data partially comes from the variation of flight condition (not perfect hover) and partially due to the effect of the rotor-fuselage aerodynamic interaction (wake effect) at low speed which is not considered in the analysis. Also the differences in the phase prediction is not clear since only the magnitude and phase information were given instead of the original vibration time-history. The imbalanced mass fault causes higher 1/rev roll vibration that is insensitive to the airspeed. The misadjusted trim-tab fault induced 1/rev vertical vibration increases with airspeed. The misadjusted pitch-control rod fault causes high vibration at hover. A parametric study was conducted to identify key factors that affect the fault-induced fuselage vibration. Analysis show that elastic fuselage model and precise hub modeling (inclusion of vibration absorbers) are essential to the vibration pre diction. The analysis shows that a compound fault can be expressed as a linear combination of individual faults involved. Aircraft operational parameters, such as gross-weight; center of gravity location, flight speed, flight path and aircraft configuration, have significant impact on the fault-induced 1/rev vibration. Prediction show that there are certain patterns in the fault-induced 1/rev hub-loads. Thus measuring both fuselage vibration and hub loads may benefit rotor system fault detection.

Yang, Mao

239

Dynamic Structural Fault Detection and Identification  

NASA Technical Reports Server (NTRS)

Aircraft structures are designed to guarantee safety of flight in some required operational envelope. When the aircraft becomes structurally impaired, safety of flight may not be guaranteed within that previously safe operational envelope. In this case the safe operational envelope must be redefined in-flight and a means to prevent excursion from this new envelope must be implemented. A specific structural failure mode that may result in a reduced safe operating envelope, the exceedance of which could lead to catastrophic structural failure of the aircraft, will be addressed. The goal of the DFEAP program is the detection of this failure mode coupled with flight controls adaptation to limit critical loads in the damaged aircraft structure. The DFEAP program is working with an F/A-18 aircraft model. The composite wing skins are bonded to metallic spars in the wing substructure. Over time, it is possible that this bonding can deteriorate due to fatigue. In this case, the ability of the wing spar to transfer loading between the wing skins is reduced. This failure mode can translate to a reduced allowable compressive strain on the wing skin and could lead to catastrophic wing buckling if load limiting of the wing structure is not applied. The DFEAP program will make use of a simplified wing strain model for the healthy aircraft. The outputs of this model will be compared in real-time to onboard strain measurements at several locations on the aircraft wing. A damage condition is declared at a given location when the strain measurements differ sufficiently from the strain model. Parameter identification of the damaged structure wing strain parameters will be employed to provide load limiting control adaptation for the aircraft. This paper will discuss the simplified strain models used in the implementation and their interaction with the strain sensor measurements. Also discussed will be the damage detection and identification schemes employed and the means by which the damaged aircraft parameters will be used to provide load limiting that keeps the aircraft within the safe operational envelope.

Smith, Timothy; Reichenbach, Eric; Urnes, James M.

2009-01-01

240

A CONTROLLER FOR HVAC SYSTEMS WITH FAULT DETECTION CAPABILITIES BASED ON SIMULATION MODELS  

E-print Network

1 A CONTROLLER FOR HVAC SYSTEMS WITH FAULT DETECTION CAPABILITIES BASED ON SIMULATION MODELS T. I describes a control scheme with fault detection capabilities suitable for application to HVAC systems as a reference of correct operation. Faults that occur in the HVAC system under control cause the PI

241

FAULT DETECTION IN HVAC SYSTEMS USING MODEL-BASED FEEDFORWARD CONTROL  

E-print Network

1 FAULT DETECTION IN HVAC SYSTEMS USING MODEL- BASED FEEDFORWARD CONTROL T. I. Salsbury, Ph.D. R. C and detect faults in the controlled process. The scheme uses static simulation models of the system under control action, the models act as a reference of correct operation. Faults that occur in the system under

Diamond, Richard

242

STATE OF CALIFORNIA FAULT DETECTION AND DIAGNOSTICS (FDD) FOR PACKAGED DIRECT EXPANSION UNITS  

E-print Network

STATE OF CALIFORNIA FAULT DETECTION AND DIAGNOSTICS (FDD) FOR PACKAGED DIRECT EXPANSION UNITS CEC-MECH-12A (Revised 08/09) CALIFORNIA ENERGY COMMISSION CERTIFICATE OF ACCEPTANCE MECH-12A NA7.5.11 Fault Signed: Position With Company (Title): #12;STATE OF CALIFORNIA FAULT DETECTION AND DIAGNOSTICS (FDD

243

Fundamental Fault Detection Limitations in Linear Non-Gaussian Systems Gustaf Hendeby  

E-print Network

Fundamental Fault Detection Limitations in Linear Non-Gaussian Systems Gustaf Hendeby Division Linköpings universitet, SE-581 83 Linköping, SWEDEN fredrik@isy.liu.se Abstract-- Sophisticated fault detection (FD) algorithms often include nonlinear mappings of observed data to fault decisions

Gustafsson, Fredrik

244

One-class support vector machines—an application in machine fault detection and classification  

Microsoft Academic Search

Fast incipient machine fault diagnosis is becoming one of the key requirements for economical and optimal process operation management. Artificial neural networks have been used to detect machine faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for machine fault detection and classification in electro-mechanical machinery

Hyun Joon Shin; Dong-Hwan Eom; Sung-Shick Kim

2005-01-01

245

Documentation of the current fault detection, isolation and reconfiguration software of the AIPS fault-tolerant processor  

NASA Technical Reports Server (NTRS)

Documentation is presented of the December 1986 version of the ADA code for the fault detection, isolation, and reconfiguration (FDIR) functions of the Advanced Information processing System (AIPS) Fault-Tolerant Processor (FTP). Because the FTP is still under development and the software is constantly undergoing changes, this should not be considered final documentation of the FDIR software of the FTP.

Lanning, David T.; Shepard, Allen W.; Johnson, Sally C.

1987-01-01

246

Classification techniques for fault detection and diagnosis of an air-handling unit  

SciTech Connect

The objective of this study is to demonstrate the application of several classification techniques to the problem of detecting and diagnosing faults in data generated by a variable-air-volume air-handling unit simulation model and to describe the strengths and weaknesses of the techniques considered. Artificial neural network classifiers, nearest neighbor classifiers, nearest prototype classifiers, a rule-based classifier, and a Bayes classifier are considered for both fault detection and diagnostics. Based on the performance of the classification techniques, the Bayes classifier appears to be a good choice for fault detection. It is a straightforward method that requires limited memory and computational effort, and it consistently yielded the lowest percentage of incorrect diagnosis. For fault diagnosis, the rule-based method is favored for classification problems such as the one considered here, where the various classes of faulty operation are well separated and can be distinguished by a single dominant symptom or feature. Results also indicate that the success or failure of classification techniques hinges to a large degree on an ability to separate different classes of operation in some feature (temperature, pressure, etc.) space. Hence, preprocessing of data to extract dominant features is as important as the selection of the classifier.

House, J.M.; Lee, W.Y.; Shin, D.R.

1999-07-01

247

Design Method of Fault Detector for Injection Unit  

NASA Astrophysics Data System (ADS)

An injection unit is considered as a speed control system utilizing a reaction-force sensor. Our purpose is to design a fault detector that detects and isolates actuator and sensor faults under the condition that the system is disturbed by a reaction force. First described is the fault detector's general structure. In this system, a disturbance observer that estimates the reaction force is designed for the speed control system in order to obtain the residual signals, and then post-filters that separate the specific frequency elements from the residual signals are applied in order to generate the decision signals. Next, we describe a fault detector designed specifically for a model of the injection unit. It is shown that the disturbance imposed on the decision variables can be made significantly small by appropriate adjustments to the observer bandwidth, and that most of the sensor faults and actuator faults can be detected and some of them can be isolated in the frequency domain by setting the frequency characteristics of the post-filters appropriately. Our result is verified by experiments for an actual injection unit.

Ochi, Kiyoshi; Saeki, Masami

248

Fault detection and multiclassifier fusion for unmanned aerial vehicles (UAVs)  

NASA Astrophysics Data System (ADS)

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a pilot aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naieve Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers) show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

Yan, Weizhong

2001-03-01

249

Time Series for Fault Detection in AN Industrial Pilot Plant  

NASA Astrophysics Data System (ADS)

Forecasting the evolution of industrial processes can be useful to discover faults. Several techniques based on analysis of time series are used to forecast the evolution of certain critical variables; however, the amount of variables makes difficult the analysis. In this way, the use of dimensionality reduction techniques such as the SOM (Self-Organizing Map) allows us to work with less data to determine the evolution of the process. SOM is a data mining technique widely used for supervision and monitoring. Since the SOM is projects data from a high dimensional space into a 2-D, the SOM reduces the number of variables. Thus, time series with the variables of the low dimensional projection can be created to make easier the prediction of future values in order to detect faults.

Morán, Antonio; Fuertes, Juan J.; Alonso, Serafín; Del Canto, Carlos; Domínguez, Manuel

2012-10-01

250

A Study on Transfer Switching in Consideration of Fault Detection in FRIENDS  

NASA Astrophysics Data System (ADS)

The authors have proposed a new concept of a distribution system ‘Flexible, Reliable and Intelligent ENergy Delivery System (FRIENDS)’ with a view of solving problems in near future and providing several services for electric companies and customers. The main idea of FRIENDS is to install new facilities named Quality Control Center (QCC) in the neighborhood of customers to realize various functions, e.g. Customized Power Quality Service. In addition, these QCCs make a network of electricity and information below distribution substations. The configuration of the network can be changed frequently depending on the system and load conditions. This frequent reconfiguration of the network requires fast and reliable Transfer Switching in QCC to ease an effect on customers. The reconfiguration with the aim of removing a fault wire also requires fast Fault Detection. This paper presents a new method for controlling Transfer Switching and a method of Fault Detection in order to realize a fast and reliable reconfiguration of QCC network in an ordinary state, and even in a fault. Besides, this paper analyzes the methods in terms of instantaneous values calculated by PSCAD/EMTDC.

Suzuki, Toshihiro; Hara, Ryoichi; Kita, Hiroyuki; Tanaka, Eiichi; Hasegawa, Jun; Iyoda, Isao

251

Coverage Estimation Methods for Stratified Fault-Injection  

E-print Network

is computationally tractable. We then consider Bayesian estimation methods for stratified sampling. Two methods space is partitioned into classes or strata (stratified sampling). These methods were first appliedCoverage Estimation Methods for Stratified Fault-Injection Michel Cukier, Member, IEEE, David

Powell, David

252

A Model-Based Probabilistic Inversion Framework for Wire Fault Detection Using TDR  

NASA Technical Reports Server (NTRS)

Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

2010-01-01

253

Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks  

NASA Astrophysics Data System (ADS)

A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Zhao, Jiangbin; Peng, Zhongxiao

2011-03-01

254

Exact Computation of Maximally Dominating Faults and Its Application to -Detection Tests  

E-print Network

Exact Computation of Maximally Dominating Faults and Its Application to -Detection Tests Ilia, IN 47907, USA Abstract £ -detection test sets for stuck-at faults have been shown to be useful in detecting an important role in controlling the increase in the size of an £ -detection test set as £ is increased

Polian, Ilia

255

Data preprocessing and output evaluation of an autoassociative neural network model for online fault detection in virginiamycin production.  

PubMed

In this study, an artificial autoassociative neural network (AANN) was used online to detect deviations from normal antibiotic production fermentation using conventional process variables. To improve the efficiency of extracting hidden information contained in multidimensional process variables, and to finally render the AANN adequate for fault detection, we explored the following methods: selection of process variables; preprocessing of data that involved normalizing the training data of the AANN; and evaluation of data that involved assessing the output of the AANN. A method for fault detection in virginiamycin M and S production by Streptomyces virginiae was successfully developed based on these techniques. PMID:16233272

Huang, Jihua; Shimizu, Hiroshi; Shioya, Suteaki

2002-01-01

256

An expert system for fault detection and diagnosis  

E-print Network

: Line Voltage Amplitudes at Bus 1 AB Fault at M12: Line Voltages at Bus 8 137 138 AB Fault at M12: Line Voltage Amplitudes at Bus 8 138 xvn FIGURE I'age 97 ABG Fault at NBELT: Curreul, s st, KING 144 98 AHG Fault at NHELT: Current Arnplitucles... at KliJG 144 99 AHG Fault at WiiART: Currents at KING 145 100 ABG I'suit at WIIART: Current Amplitudes at, KING 145 101 ABG Fault at M12: Currents at Bus 1 I46 102 ABG Fault at M12: Current Amplitudes at Bus 1 146 103 AHG Fault at MI 2: Currents...

Spasojevic, Predrag

2012-06-07

257

Customized multiwavelets for planetary gearbox fault detection based on vibration sensor signals.  

PubMed

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

258

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals  

PubMed Central

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

259

On Methods for the Formal Specification of Fault Tolerant Systems  

E-print Network

On Methods for the Formal Specification of Fault Tolerant Systems Manuel Mazzara School specifications has been made experimenting on case studies and an example is presented. Keywords-Formal Methods intentions and actions. Formal methods in system specification look to be an approachable solution. Object

Southampton, University of

260

IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 16, NO. 6, DECEMBER 2000 689 Fault Detection for Robot Manipulators with  

E-print Network

to manipulator fault detection. The normal (fault-free) dynaIEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 16, NO. 6, DECEMBER 2000 689 Fault Detection, Member, IEEE Abstract--In this paper, we introduce a new approach to fault detection for robot

Dixon, Warren

261

The Study of Changes of Physical — Mechanical Properties of Materials in a Condensed State under Hydrogen Influence Using Fault Detection Acoustic Microscopy Methods  

Microsoft Academic Search

The paper deals with the perspectives of the application of acoustic microscopy methods for studying of changes of physical\\u000a — mechanical properties of materials in a condensed state under hydrogen influence. The basic principles of the methods as\\u000a well as the results of the experiments of studying the structure of materials in a condensed state and its transformation\\u000a upon changing

A. V. Budanov; A. I. Kustov; I. A. Migel

262

Fault detection, diagnosis, and data-driven modeling in HVAC chillers  

Microsoft Academic Search

Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and

Setu M. Namburu; Jianhui Luo; Mohammad Azam; Kihoon Choi; Krishna R. Pattipati

2005-01-01

263

Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis  

E-print Network

Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis-based automated framework dealing with local faults occurring on bearings in the transmission of a helicopter frequencies of bearing faults may be shifted. They may also be masked by parasitical frequencies because

Boyer, Edmond

264

Process monitoring using causal map and multivariate statistics: fault detection and identification  

Microsoft Academic Search

Data-driven techniques based on multivariate statistics (such as principal component analysis (PCA) and partial least squares (PLS)) have been applied widely to chemical processes and their effectiveness for fault detection is well recognized. There is an inherent limitation on the ability for purely data-driven techniques to identify and diagnose faults, especially when the abnormal situations are associated with unknown faults

Leo H. Chiang; Richard D. Braatz

2003-01-01

265

Hidden Markov models for fault detection in dynamic systems  

NASA Technical Reports Server (NTRS)

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J. (inventor)

1993-01-01

266

Hidden Markov models for fault detection in dynamic systems  

NASA Technical Reports Server (NTRS)

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J. (inventor)

1995-01-01

267

Non-linear arx model-based kullback index for fault detection of a screw compressor  

Microsoft Academic Search

A non-linear model-based fault detection scheme for a single screw compressor is described. This fault detection scheme can detect faults without prior experience with them. It employs genetic algorithms to identify non-linear difference equations that model the dynamics of the compressor from measured motor current and angular velocity. First, the scheme identifies a baseline model from the compressor which is

Y. C. Jeon; C. James Li

1995-01-01

268

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis  

Microsoft Academic Search

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection\\/diagnosis process and successful fault detection\\/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN\\/FZ) inference systems, namely, fuzzy adaptive learning control\\/decision

Sinan Altug; Mo-Yuen Chen; H. Joel Trussell

1999-01-01

269

Planetary gearbox fault diagnosis using an adaptive stochastic resonance method  

NASA Astrophysics Data System (ADS)

Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness.

Lei, Yaguo; Han, Dong; Lin, Jing; He, Zhengjia

2013-07-01

270

Sequential optimal detection and isolation of faults in systems with random disturbances  

Microsoft Academic Search

The problem of optimal detecting and isolating faults in systems with random disturbances is discussed. The fault (change) detection problem has received extensive research attention. On the contrary, the change isolation is still an unsolved problem. A system with abrupt changes and random disturbances is considered. A joint criterion of optimality for the detection\\/isolation problem is introduced and a change

Igor V. Nikiforov

1994-01-01

271

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

NASA Technical Reports Server (NTRS)

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.

Keesler, E. L.

1974-01-01

272

FINDS: A fault inferring nonlinear detection system. User's guide  

NASA Technical Reports Server (NTRS)

The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.

Lancraft, R. E.; Caglayan, A. K.

1983-01-01

273

Typical fault diagnosis method for high-speed turbopump of Liquid Rocket Engine  

Microsoft Academic Search

Turbopump is a high-fault-rate component in Liquid Rocket Engine (LRE). In this paper, the reasons of the typical fault of turbopump rotor blade abruption and abscission are analyzed. And, by the method of dynamic analysis, the vibration features of the fault are studied to select the frequency features diagnosing effectively the fault. Then, the extraction method of the features is

Lurui Xia; Niaoqing Hu; Guojun Qin; Ming Gao

2008-01-01

274

Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper  

NASA Astrophysics Data System (ADS)

Fault detection and isolation (FDI) in real-time systems can provide early warnings for faulty sensors and actuator signals to prevent events that lead to catastrophic failures. The main objective of this paper is to develop FDI and fault tolerant control techniques for base isolation systems with magneto-rheological (MR) dampers. Thus, this paper presents a fixed-order FDI filter design procedure based on linear matrix inequalities (LMI). The necessary and sufficient conditions for the existence of a solution for detecting and isolating faults using the H_{\\infty } formulation is provided in the proposed filter design. Furthermore, an FDI-filter-based fuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed to preserve the pre-specified performance of the system in the presence of various unknown faults. Simulation and experimental results demonstrated that the designed filter can successfully detect and isolate faults from displacement sensors and accelerometers while maintaining excellent performance of the base isolation technology under faulty conditions.

Wang, Han; Song, Gangbing

2011-08-01

275

A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery  

PubMed Central

This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

Wang, Huaqing; Chen, Peng

2009-01-01

276

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition  

PubMed Central

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

2014-01-01

277

Some methods of estimating a coverage parameter. [for fault tolerant computing systems  

NASA Technical Reports Server (NTRS)

To demonstrate the high reliability of fault-tolerant computing systems, experimental testing can be performed by injecting faults into their hardware and observing the times needed to detect and isolate failed components and reconfigure the good components. Coverage, a parameter measuring continued system success, is the probability that recovery occurs before other, perhaps catastrophic, component failures occur. Methods of estimating coverage are given for the case of randomly selecting a subset of the pins or chips for testing. Pin-level samples of times to recovery are treated by the Type I censoring model often used in life testing.

Lee, L. D.

1983-01-01

278

An improved PCA method with application to boiler leak detection.  

PubMed

Principal component analysis (PCA) is a popular fault detection technique. It has been widely used in process industries, especially in the chemical industry. In industrial applications, achieving a sensitive system capable of detecting incipient faults, which maintains the false alarm rate to a minimum, is a crucial issue. Although a lot of research has been focused on these issues for PCA-based fault detection and diagnosis methods, sensitivity of the fault detection scheme versus false alarm rate continues to be an important issue. In this paper, an improved PCA method is proposed to address this problem. In this method, a new data preprocessing scheme and a new fault detection scheme designed for Hotelling's T2 as well as the squared prediction error are developed. A dynamic PCA model is also developed for boiler leak detection. This new method is applied to boiler water/steam leak detection with real data from Syncrude Canada's utility plant in Fort McMurray, Canada. Our results demonstrate that the proposed method can effectively reduce false alarm rate, provide effective and correct leak alarms, and give early warning to operators. PMID:16082787

Sun, Xi; Marquez, Horacio J; Chen, Tongwen; Riaz, Muhammad

2005-07-01

279

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification  

NASA Technical Reports Server (NTRS)

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,

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

2002-01-01

280

Online sensor fault detection based on an improved strong tracking filter.  

PubMed

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

281

Fault detection and isolation in aircraft gas turbine engines. Part 1: underlying concept  

E-print Network

of the blade-tip clearance in the turbine, labyrinth seal leakage, wear and erosion, and corrosion in the hot307 Fault detection and isolation in aircraft gas turbine engines. Part 1: underlying concept: aircraft propulsion, gas turbine engines, fault detection and isolation, statistical pattern recognition 1

Ray, Asok

282

Fault detection for salinity sensors in the Columbia estuary Cynthia Archer  

E-print Network

Fault detection for salinity sensors in the Columbia estuary Cynthia Archer Department of Computer, salinity measurement Citation: Archer, C., A. Baptista, and T. K. Leen, Fault detection for salinity [U.S. Army Corps of Engineers, 2001]. [3] CORIE salinity sensors deployed in the harsh estuary

Leen, Todd K.

283

Automatic Fault Detection and Recovery in Real Time Switched Ethernet Networks  

E-print Network

­4400 (srinidhi, chiueh)@cs.sunysb.edu Abstract EtheReal is a real­time Fast Ethernet switch architectureAutomatic Fault Detection and Recovery in Real Time Switched Ethernet Networks Srinidhi Varadarajan implementation. The heart of EtheReal's fault detection and recovery mechanism is a fast spanning tree

Chiueh, Tzi-cker

284

Limitations for detecting small-scale faults using the coherency analysis of seismic data  

E-print Network

processed with a temporal window length less than one period. A fault could be detected by coherency when the signal-to-noise ratio was >3. A fault could also be detected as long as the throw-to-wavelength ratio was >5% or two-way traveltime-toperiod >10...

Barnett, David Benjamin

2006-08-16

285

Probabilistic Model of Fault Detection in Quantum Circuits  

NASA Astrophysics Data System (ADS)

Since the introduction of quantum computation, several protocols (such as quantum cryptography, quantum algorithm, quantum teleportation) have established quantum computing as a superior future technology. Each of these processes involves quantum circuits, which are prone to different kinds of faults. Consequently, it is important to verify whether the circuit hardware is defective or not. The systematic procedure to do so is known as fault testing. Normally testing is done by providing a set of valid input states and measuring the corresponding output states and comparing the output states with the expected output states of the perfect (fault less) circuit. This particular set of input vectors are known as test set [6]. If there exists a fault then the next step would be to find the exact location and nature of the defect. This is known as fault localization. A model that explains the logical or functional faults in the circuit is a fault model. Conventional fault models include (i) stuck at faults, (ii) bridge faults, and (iii) delay faults. These fault models have been rigorously studied for conventional irreversible circuit. But with the advent of reversible classical computing and quantum computing it has become important to enlarge the domain of the study on test vectors.

Banerjee, A.; Pathak, A.

286

Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method  

NASA Astrophysics Data System (ADS)

Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.

Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

2011-10-01

287

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

PubMed Central

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

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

2014-01-01

288

Fault detection in variable speed machinery: Statistical parameterization  

NASA Astrophysics Data System (ADS)

Variable speed machinery presents a particular challenge to automated condition-monitoring systems; changes in speed have a strong relation to the vibration response collected by accelerometers—the effect of which may mask fault conditions in standard condition monitoring techniques. In order to account for the effects of this measurable variable, the vibration response will be segmented into speed bins with a small range of speed. The mean and covariance matrix for the feature vectors in each speed bin will be computed in order to derive a statistical novelty boundary for that bin. Each component of these statistical parameters can then be interpolated or regressed in order to derive boundaries for speed segments where no training data is available. A comparison of the use of a statistical decision boundary and support vector boundaries, whose inputs have been centralized and whitened with these statistical parameters, will reveal a stronger classification approach. These methods were validated on data gathered from an experimental gearbox and motor apparatus operating at variable speeds; the results indicate a high degree of separability between data from healthy and faulted states—providing exceptional classification error.

McBain, Jordan; Timusk, Markus

2009-11-01

289

Exoplanet Detection Methods  

NASA Astrophysics Data System (ADS)

This chapter reviews various methods of detecting planetary companions to stars from an observational perspective, focusing on radial velocities, astrometry, direct imaging, transits, and gravitational microlensing. For each method, this chapter first derives or summarizes the basic observable phenomena that are used to infer the existence of planetary companions as well as the physical properties of the planets and host stars that can be derived from the measurement of these signals. This chapter then outlines the general experimental requirements to robustly detect the signals using each method, by comparing their magnitude to the typical sources of measurement uncertainty. This chapter goes on to compare the various methods to each other by outlining the regions of planet and host star parameter space where each method is most sensitive, stressing the complementarity of the ensemble of the methods at our disposal. Finally, there is a brief review of the history of the young exoplanet field, from the first detections to current state-of-the-art surveys for rocky worlds.

Wright, Jason T.; Gaudi, B. Scott

290

Online Fault Detection of Permanent Magnet Demagnetization for IPMSMs by Nonsingular Fast Terminal-Sliding-Mode Observer.  

PubMed

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-LABplatform. 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

Zhao, Kai-Hui; Chen, Te-Fang; Zhang, Chang-Fan; He, Jing; Huang, Gang

2014-01-01

291

Online Fault Detection of Permanent Magnet Demagnetization for IPMSMs by Nonsingular Fast Terminal-Sliding-Mode Observer  

PubMed Central

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

Zhao, Kai-Hui; Chen, Te-Fang; Zhang, Chang-Fan; He, Jing; Huang, Gang

2014-01-01

292

Method and system for fault accommodation of machines  

NASA Technical Reports Server (NTRS)

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Goebel, Kai Frank (Inventor); Subbu, Rajesh Venkat (Inventor); Rausch, Randal Thomas (Inventor); Frederick, Dean Kimball (Inventor)

2011-01-01

293

A hybrid fault diagnosis method using morphological filter-translation invariant wavelet and improved ensemble empirical mode decomposition  

NASA Astrophysics Data System (ADS)

Defective rolling bearing response is often characterized by the presence of periodic impulses, which are usually immersed in heavy noise. Therefore, a hybrid fault diagnosis approach is proposed. The morphological filter combining with translation invariant wavelet is taken as the pre-filter process unit to reduce the narrowband impulses and random noises in the original signal, then the purified signal will be decomposed by improved ensemble empirical mode decomposition (EEMD), in which a new selection method integrating autocorrelation analysis with the first two intrinsic mode functions (IMFs) having the maximum energies is put forward to eliminate the pseudo low-frequency components of IMFs. Applying the envelope analysis on those selected IMFs, the defect information is easily extracted. The proposed hybrid approach is evaluated by simulations and vibration signals of defective bearings with outer race fault, inner race fault, rolling element fault. Results show that the approach is feasible and effective for the fault detection of rolling bearing.

Meng, Lingjie; Xiang, Jiawei; Wang, Yanxue; Jiang, Yongying; Gao, Haifeng

2015-01-01

294

An energy kurtosis demodulation technique for signal denoising and bearing fault detection  

NASA Astrophysics Data System (ADS)

Rolling element bearings are commonly used in rotary machinery. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-related features are non-stationary in nature. In this work, an energy kurtosis demodulation (EKD) technique is proposed for bearing fault detection especially for non-stationary signature analysis. The proposed EKD technique firstly denoises the signal by using a maximum kurtosis deconvolution filter to counteract the effect of signal transmission path so as to highlight defect-associated impulses. Next, the denoised signal is modulated over several frequency bands; a novel signature integration strategy is proposed to enhance feature characteristics. The effectiveness of the proposed EKD fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions.

Wang, Wilson; Lee, Hewen

2013-02-01

295

Identification, Prediction and Detection of the Process Fault in a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique  

Microsoft Academic Search

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an

Masoud Sadeghian; Alireza Fatehi

2009-01-01

296

Sensor fault detection for uninterruptible power supply (UPS) control system using fast fuzzy-neural network and immune network  

Microsoft Academic Search

In power electronic systems, many researchers have been investigating troubles caused by a sensor failure. Sensorless vector control of induction motor drives has attracted researchers' attention for a long time. The paper describes a sensor fault detection method for UPS current and voltage feedback systems. Once a certain sensor fails, then its influence propagates through the whole system and may

Shigeharu Taniguchi; Yasuhiko Dote

2001-01-01

297

Research on envelope analysis for bearings fault detection  

Microsoft Academic Search

In order to overcome the shortcomings in the traditional envelope analysis, the methods based on the wavelet transform and morphological filters are introduced for detecting defects in rolling element bearings. The method based on the analytic wavelet can be applied to non-linear and non-stationary bearing vibration signals. The method based on the morphological filters has the advantage of less computation,

Wentao Sui; Dan Zhang

2010-01-01

298

Fault Detection and Prediction of Clocks and Timers Based on Computer Audition and Probabilistic Neural Networks  

Microsoft Academic Search

\\u000a This paper investigates the fault detection and prediction of rhythmically soniferous products, such as clocks, watches and\\u000a timers. Such products with fault cannot work steadily or probably cause malfunction. The authors extend the concept of computer\\u000a audition and establish an architectural model of product fault prediction system based on probabilistic neural networks. The\\u000a system listens to the product sound by

S. Y. Chen; C. Y. Yao; G. Xiao; Y. S. Ying; W. L. Wang

2005-01-01

299

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework  

NASA Technical Reports Server (NTRS)

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

1994-01-01

300

Method for detecting biomolecules  

DOEpatents

A method for detecting and measuring the concentration of biomolecules in solution, utilizing a conducting electrode in contact with a solution containing target biomolecules, with a film with controllable pore size distribution characteristics applied to at least one surface of the conducting electrode. The film is functionalized with probe molecules that chemically interact with the target biomolecules at the film surface, blocking indicator molecules present in solution from diffusing from the solution to the electrode, thereby changing the electrochemical response of the electrode

Huo, Qisheng (Albuquerque, NM); Liu, Jun (Albuquerque, NM)

2008-08-12

301

Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller  

NASA Astrophysics Data System (ADS)

This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.

Kopka, Ryszard

2014-12-01

302

Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition  

NASA Astrophysics Data System (ADS)

Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detection approach for seeded bearing faults. Vibration signals from normal bearings and bearings with three different fault locations, as well as different fault sizes and loading conditions are examined. The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set. Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition. The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.

Georgoulas, George; Loutas, Theodore; Stylios, Chrysostomos D.; Kostopoulos, Vassilis

2013-12-01

303

Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations  

NASA Astrophysics Data System (ADS)

Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

Ahmed, M.; Gu, F.; Ball, A. D.

2012-05-01

304

Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors  

Microsoft Academic Search

This paper presents an application of recurrent neuro-fuzzy systems to fault detection and isolation in nuclear reactors. A general framework is adopted, in which a fuzzification module is linked to an inference module that is actually a neural network adapted to the recognition of the dynamic evolution of process variables and related faults. Process data is fuzzified in order to

Alexandre Evsukoff; Sylviane Gentil

2005-01-01

305

Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine  

E-print Network

Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction Assad, R. Karam, and S. Farah Abstract--This paper deals with the diagnosis of Wind Tur- bines based simulation experiments and compared for several types of fault, including air-gap eccentricities, broken

Paris-Sud XI, Université de

306

Event Region Fault-Tolerant Detection Based on Distributed Weight for Wireless Sensor Networks  

Microsoft Academic Search

A number of faulty sensors would lead to inaccuracy of event region detection and deterioration of network quality of service in wireless sensor networks. This paper proposed a distributed weighted fault-tolerant algorithm (DWFA) for nodes regular deployment and an optimal fault-tolerant mechanism based on weighted distance (WDOFM) for nodes irregular deployment. Both of them exploit spatial correlations among nearby sensors,

Hong Li; Ping Li; Zheng Xie; Min Wu

2008-01-01

307

Fault detection in brushless DC motors using Discrete Square Root Filtering and fuzzy logic  

Microsoft Academic Search

Brushless DC (BLDC) motors are used extensively in the industry for a wide variety of applications. In the Aerospace industry, they are mainly used in the control actuation system in launch vehicles. Different types of faults can develop in a BLDC motor. It is essential to detect these faults in the early stage itself before they develop into system failures.

Meagan Mathew; Abdul Jaleel

2012-01-01

308

Automated Diagnosis for Fault Detection, Identification and Recovery in Autosub 6000  

E-print Network

be explained as the effects of ocean currents on the vehicle. For faults of this type the discrete approach to the main engines of the X-37 experimental re-entry vehicle where it was used to detect faults in the actuators for control surfaces and associated power system components. The software and its successor, Liv

Yao, Xin

309

Detection, Isolation and Management of Actuator Faults in Parabolic PDEs under Uncertainty and Constraints  

E-print Network

Detection, Isolation and Management of Actuator Faults in Parabolic PDEs under Uncertainty-varying uncertain variables, actuator constraints and faults. The design is based on an approximate, finite transformation, obtained with judicious actuator placement, is used to transform the approximate system

Sontag, Eduardo

310

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

NASA Technical Reports Server (NTRS)

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.

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

2010-01-01

311

Sensor Fault Detection in Power Plants Andrew Kusiak1  

E-print Network

sensors monitor assembly quality Li and Chen 2006 . In a safety- critical process e.g., a nuclear power the energy production cost and emission of pollutants. Performance of any controlled process is greatly the sensor faults, a system- atic approach is needed. Traditionally, two ways to deal with sen- sory faults

Kusiak, Andrew

312

APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION  

Microsoft Academic Search

The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gearbox. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal from a helicopter gearbox is used to demonstrate the application of the suggested wavelet by a simple computer algorithm. The

W. J. Wang; P. D. McFadden

1996-01-01

313

A novel sensing method of fault in moving machine  

NASA Astrophysics Data System (ADS)

Fault in rotating parts of a machine such as bearings and gears often causes periodic impulses and they are transmitted to adjacent parts while it is moving with a constant speed. It has been an issue, therefore, to find a best means that can tell us the existence of periodic impulse and the period as early as possible. Previous researches mainly use accelerometers since it can easily measure the vibration due to impulse. They normally require considerable measurement time and inconvenience, especially if we have to use them for many different machines. This is straightforward consequence because the sensor is to be removed from and attached to the machine elements as many time as required. This paper proposes a novel method to sense the periodic impulse of moving machinery, by using a non-contact sensor such as a microphone. The method uses the periodic impulsive sound radiated by the fault instead of the impulsive vibration. It is not only more convenient than using the accelerometers, but it can also promptly test a lot of machines; they only have to pass by the microphone during the measurement. However, because the machine under test is moving, the measured impulsive signal is not periodic due to Doppler effect. This makes it difficult to estimate the period of impulses as well as to find the existence of fault. In order to solve this, we firstly model and analyze the characteristics of the moving periodic impulsive sound. Based on this, a method to sense the existence of fault is introduced by utilizing characteristics of moving periodic impulsive sound. The performance is tested by theory and simulation with respect to the signal to noise ratio.

Seo, Dae-Hoon; Jeon, Jong-Hoon; Kim, Yang-Hann

2014-03-01

314

Development, Implementation, and Testing of Fault Detection Strategies on the National Wind Technology Center's Controls Advanced Research Turbines  

SciTech Connect

The National Renewable Energy Laboratory's National Wind Technology Center dedicates two 600 kW turbines for advanced control systems research. A fault detection system for both turbines has been developed, analyzed, and improved across years of experiments to protect the turbines as each new controller is tested. Analysis of field data and ongoing fault detection strategy improvements have resulted in a system of sensors, fault definitions, and detection strategies that have thus far been effective at protecting the turbines. In this paper, we document this fault detection system and provide field data illustrating its operation while detecting a range of failures. In some cases, we discuss the refinement process over time as fault detection strategies were improved. The purpose of this article is to share field experience obtained during the development and field testing of the existing fault detection system, and to offer a possible baseline for comparison with more advanced turbine fault detection controllers.

Johnson, K. E.; Fleming, P. A.

2011-06-01

315

A method for automatic evaluation of fault effects in the advanced intelligent network  

Microsoft Academic Search

The method described here provides network operators with criteria for deciding the priorities with which services degraded by network faults should be restored. This method consists of four processes: the first lists the unavailable services of customers affected by the fault, the second predicts the mean time needed to repair the fault, the third predicts the traffic trends for the

Hikaru Suzuki; Hitoshi Kawamura; Takamichi Akiyama; Narumi Takahashi

1993-01-01

316

Case studies of electrical and electromagnetic methods applied to mapping active faults beneath the thick quaternary  

Microsoft Academic Search

It is of considerable importance to explore the geological structure around active faults, especially near-surface unconsolidated layers, to estimate the faults' activity. There are numerous case studies to investigate active faults using geophysical exploration methods; however, only a few cases have been verified in detail by comparison with other geological information. We have applied electric and electromagnetic methods, which can

Koichi Suzuki; Shinji Toda; Kenichiro Kusunoki; Yasuhiro Fujimitsu; Tohru Mogi; Akira Jomori

2000-01-01

317

Three-dimensional Distinct Element Method (DEM) modelling of oblique-slip normal faulting  

NASA Astrophysics Data System (ADS)

Normal faults frequently exhibit a strike-slip displacement component, which can arise for example from oblique reactivation or from fault strike changes, e.g. along bends. It is well known from both natural examples and analogue experiments that fault zones developing above oblique normal faults are typically comprised of systematically stepping fault segments. However, dependencies of fault segment orientation and segmentation on fault obliquity and mechanical properties during faulting are poorly understood. For example, it is not clear whether systematically stepping fault segments link preferentially via footwall or hanging wall breaching. Moreover, the persistence of fault bends throughout mechanically layered sequences is another yet unexplored topic. Here we use three-dimensional Distinct Element Method (DEM) modelling to elucidate the geometry and kinematics of fault zones developing above oblique normal faults. We systematically vary both fault obliquity and confining pressure. Fault zone structure (e.g. segment orientations, drag, etc.) is quantified from horizon maps generated at different levels within the model. Irrespective of fault obliquity, fault zones become better localised with increasing confining pressure. Analysis of displacement partitioning at branch-points illustrates that neither footwall nor hanging wall breaching is the preferred mode of segment linkage. Fault segment orientations exhibit a systematic fault obliquity dependence, which can be rationalised using infinitesimal strain theory for transtensional shear zones. Our models therefore suggest that the orientation of fault segments developing above oblique normal faults may be used to estimate the extension direction, as suggested nearly 30 years ago by A.M. McCoss [1986, J. Struct. Geol. 8(6), p. 715-718].

Schöpfer, Martin; Childs, Conrad; Manzocchi, Tom; Walsh, John

2014-05-01

318

Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis  

NASA Astrophysics Data System (ADS)

The objective of this paper is to propose a vibration-based automated framework dealing with local faults occurring on bearings in the transmission of a helicopter. The knowledge of the shaft speed and kinematic computation provide theoretical frequencies that reveal deteriorations on the inner and outer races, on the rolling elements or on the cage. In practice, the theoretical frequencies of bearing faults may be shifted. They may also be masked by parasitical frequencies because the numerous noisy vibrations and the complexity of the transmission mechanics make the signal spectrum very profuse. Consequently, detection methods based on the monitoring of the theoretical frequencies may lead to wrong decisions. In order to deal with this drawback, we propose to readjust the fault frequencies from the theoretical frequencies using the redundancy introduced by the harmonics. The proposed method provides the confidence index of the readjusted frequency. Minor variations in shaft speed may induce random jitters. The change of the contact surface or of the transmission path brings also a random component in amplitude and phase. These random components in the signal destroy spectral localization of frequencies and thus hide the fault occurrence in the spectrum. Under the hypothesis that these random signals can be modeled as cyclostationary signals, the envelope spectrum can reveal that hidden patterns. In order to provide an indicator estimating fault severity, statistics are proposed. They make the hypothesis that the harmonics at the readjusted frequency are corrupted with an additive normally distributed noise. In this case, the statistics computed from the spectra are chi-square distributed and a signal-to-noise indicator is proposed. The algorithms are then tested with data from two test benches and from flight conditions. The bearing type and the radial load are the main differences between the experiences on the benches. The fault is mainly visible in the spectrum for the radially constrained bearing and only visible in the envelope spectrum for the "load-free" bearing. Concerning results in flight conditions, frequency readjustment demonstrates good performances when applied on the spectrum, showing that a fully automated bearing decision procedure is applicable for operational helicopter monitoring.

Girondin, Victor; Pekpe, Komi Midzodzi; Morel, Herve; Cassar, Jean-Philippe

2013-07-01

319

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

NASA Technical Reports Server (NTRS)

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.

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

2005-01-01

320

System for detecting and limiting electrical ground faults within electrical devices  

DOEpatents

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.

Gaubatz, Donald C. (Cupertino, CA)

1990-01-01

321

On the Intelligent Fault Diagnosis Method for Marine Diesel Engine  

Microsoft Academic Search

The marine diesel engine is a complex system. Its mapping process of fault diagnosis has multi-fault attributes, which means input and output of fault pattern attribute are the multi-mapping relations. An approach of intelligent fault diagnosis using fuzzy neural networks and genetic algorithms to optimize and train is studied in this paper for this system. The structure and the model

Peng Li; Baoku Su

2008-01-01

322

a New Online Distributed Process Fault Detection and Isolation Approach Using Potential Clustering Technique  

NASA Astrophysics Data System (ADS)

Most of process fault monitoring systems suffer from offline computations and confronting with novel faults that limit their applicabilities. This paper presents a new online fault detection and isolation (FDI) algorithm based on distributed online clustering approach. In the proposed approach, clustering algorithm is used for online detection of a new trend of time series data which indicates faulty condition. On the other hand, distributed technique is used to decompose the overall monitoring task into a series of local monitoring sub-tasks so as to locally track and capture the process faults. This algorithm not only solves the problem of online FDI, but also can handle novel faults. The diagnostic performances of the proposed FDI approach is evaluated on the Tennessee Eastman process plant as a large-scale benchmark problem.

Bahrampour, Soheil; Moshiri, Behzad; Salahshoor, Karim

2009-08-01

323

A neural network prototype for fault detection and diagnosis of heating systems  

SciTech Connect

An artificial neural network (ANN) prototype for fault detection and diagnosis (FDD) in complex heating systems is presented in this paper. The six operating modes with faults used to develop this prototype stemmed from a detailed investigation in cooperation with heating systems maintenance experts, and are among the most important operating faults for this type of system. The prototype has been developed by using the daily values obtained by a preprocessing procedure of the simulation data of one reference heating system, and then generalizing to four heating systems not used during the training phase. This paper demonstrates the feasibility of using ANNs for detecting and diagnosing faults in heating systems provided that training data representative of the behavior of the systems with and without faults are available.

Li, X.; Visier, J.C.; Vaezi-Nejad, H. [French Scientific and Technical Building Center, Marne-la-Vallee (France). HVAC Dept.

1997-12-31

324

Implementation and Testing of a Fault Detection Software Tool for Improving Control System Performance in a Large Commercial Building  

E-print Network

Implementation and Testing of a Fault Detection Software Tool for Improving Control System that can detect faults in the controlled process and improve control performance over traditional PID-forward control action, which acts as a reference of correct operation. Faults that occur in the system cause

325

Probability of detecting single faults in Hadamard and SWAP gates of a quantum computer after several operations  

E-print Network

, Hadamard gate, Quantum decoherence, undetectable faults, quan- tum computer hardware INTRODUCTIONc DRAFT Probability of detecting single faults in Hadamard and SWAP gates of a quantum computer is the main factor which causes fault in a quantum computer. In this article, the probability of detecting

326

Lessons Learned on Implementing Fault Detection, Isolation, and Recovery (FDIR) in a Ground Launch Environment  

NASA Technical Reports Server (NTRS)

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).

Ferell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Goerz, Jesse; Brown, Barbara

2010-01-01

327

Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN  

E-print Network

At first the basic research about FDD is summarized, and a detection model based on ANN is initially set up. The paper presents experiments that simulate seven faults, including change flow rate of chilled water, cooling water and refrigerant...

Zhou, Y.; Zheng, J.; Liu, Z.; Yang, C.; Peng, P.

2006-01-01

328

A Qualitive Modeling Approach for Fault Detection and Diagnosis on HVAC Systems  

E-print Network

This paper describes the basics and first test results of a model based approach using qualitative modeling to perform Fault Detection and Diagnostics (FDD) on HVAC and R systems. A quantized system describing the qualitative behavior of a...

Muller, T.; Rehault, N.; Rist, T.

2013-01-01

329

H? mode-dependent fault detection filter design for stochastic Markovian jump systems with time-varying delays and parameter uncertainties.  

PubMed

This paper deals with the problem of robust H? fault detection for a class of stochastic Markovian jump systems (SMJSs) The aim is to design a linear mode-dependent fault detection filter such that the fault detection system is not only stochastically asymptotically stable in the large, but also satisfies a prescribed H?-norm level for all admissible uncertainties. By using Lyapunov stability theory and generalized Itô formula, some novel mode-dependent and delay-dependent sufficient conditions in terms of linear matrix inequality (LMI) are proposed to insure the existence of the desired fault detection filter. A simulation example and an industrial nonisothermal continuous stirred tank reactor (CSTR) system are employed to show the effectiveness of the proposed method. PMID:24929630

Zhuang, Guangming; Xia, Jianwei; Chu, Yuming; Chen, Fu

2014-07-01

330

Identifiability of Additive Actuator and Sensor Faults by State Augmentation  

NASA Technical Reports Server (NTRS)

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.

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

2014-01-01

331

Detecting and tolerating Byzantine faults in database systems  

E-print Network

This thesis describes the design, implementation, and evaluation of a replication scheme to handle Byzantine faults in transaction processing database systems. The scheme compares answers from queries and updates on multiple ...

Vandiver, Benjamin Mead, 1978-

2008-01-01

332

Detecting and Tolerating Byzantine Faults in Database Systems  

E-print Network

This thesis describes the design, implementation, and evaluation of a replication scheme to handle Byzantine faults in transaction processing database systems. The scheme compares answers from queries and updates on multiple ...

Vandiver, Benjamin Mead

2008-06-30

333

An adaptive algorithm for the detection of high impedance faults on power distribution systems  

E-print Network

AN ADAPTIVE ALGORITHM FOR THE DETECTION OF HIGH IMPEDANCE FAULTS ON POWER DISTRIBUTION SYSTEMS A Thesis by KURT ERIC MCCALL Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE December 1990 Major Subject, : Electrical Engineering AN ADAPTIVE ALGORITHM FOR THE DETECTION OF HIGH IMPEDANCE FAULTS ON POWER DISTRIBUTION SYSTEMS A thesis by KURT ERIC MCCALL Approved as to style and content by...

McCall, Kurt Eric

1990-01-01

334

An adaptive PMU based fault detection\\/location technique for transmission lines. I. Theory and algorithms  

Microsoft Academic Search

An adaptive fault detection\\/location technique based on a phasor measurement unit (PMU) for an EHV\\/UHV transmission line is presented. A fault detection\\/location index in terms of Clarke components of the synchronized voltage and current phasors is derived. The line parameter estimation algorithm is also developed to solve the uncertainty of parameters caused by aging of transmission lines. This paper also

Joe-Air Jiang; Jun-Zhe Yang; Ying-Hong Lin; Chih-Wen Liu; Jih-Chen Ma

2000-01-01

335

Fault detection system for distribution lines using a DSP for filtering and data manipulation  

E-print Network

FAULT DETECTION SYSTEM FOR DISTRIBUTION LINES USING A DSP FOR FILTERING AND DATA MANIPULATION A Thesis by Kevin L. Schultz Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE May 1991 Major Subject: Electrical Engineering FAULT DETECTION SYSTEM FOR DISTRIBUTION LINES USING A DSP FOR FILTERING AND DATA MANIPULATION A Thesis by Kevin L. Schultz Approved as to style and content by: Karan...

Schultz, Kevin L

2012-06-07

336

Fault detection and prognosis of assembly locating systems using piezoelectric transducers  

Microsoft Academic Search

Fixture faults have been identified as a principal root cause of defective products in assembly lines; however, there exists\\u000a a lack of fast and accurate monitoring tools to detect fixture fault damage. Locating fixture damage causes a decrease in\\u000a product quality and production throughput due to the extensive work required to detect and diagnosis a faulty fixture. In\\u000a this paper,

Jeremy L. Rickli; Jaime A. Camelio; Jason T. Dreyer; Sudhakar M. Pandit

337

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

NASA Technical Reports Server (NTRS)

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.

Collins, Emmanuel G.

2000-01-01

338

Fault detection and diagnosis of power converters using artificial neural networks  

SciTech Connect

Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission systems.

Swarup, K.S.; Chandrasekharaiah, H.S. [Indian Inst. of Science, Bangalore (India). Dept. of High Voltage Engineering

1995-12-31

339

Airborne LiDAR detection of postglacial faults and Pulju moraine in Palojärvi, Finnish Lapland  

NASA Astrophysics Data System (ADS)

Postglacial faults (PGFs) are indicative of young tectonic activity providing crucial information for nuclear repository studies. Airborne LiDAR (Light Detection And Ranging) data revealed three previously unrecognized late- or postglacial faults in northernmost Finnish Lapland. Under the canopies of mountain birch (Betula pubescens ssp. czerepanovii) we also found clusters of the Pulju moraine, typically found on the ice-divide zone of the former Fennoscandian ice sheet (FIS), to be spatially associated with the fault-scarps. Tilt derivative (TDR) filtered LiDAR data revealed the previously unknown Palojärvi fault that, by the NE-SW orientation parallels with the well documented Lainio-Suijavaara PGF in northern Sweden. This suggests that PGFs are more extensive features than previously recognized. Two inclined diamond drill holes verified the fractured system of the Palojärvi fault and revealed clear signs of postglacial reactivation. Two other previously unrecognized PGFs, the W-E trending Paatsikkajoki fault and the SE-NW trending Kultima fault, differ from the Palojärvi faulting in orientation and possibly also with regard to age. The Pulju moraine, a morphological feature showing transitions from shallow (< 2-m-high) circular/arcuate ridges to sinusoidal/anastomosing esker networks was found to be concentrated within 6 km from the Kultima fault-scarp. We advocate that some of the past seismic events took place under the retreating wet-base ice sheet and the increased pore-water pressure triggered the sediment mass flows and formation of the Pulju moraine-esker landscape.

Sutinen, Raimo; Hyvönen, Eija; Middleton, Maarit; Ruskeeniemi, Timo

2014-04-01

340

A Formal Method for Developing Provably Correct Fault-Tolerant Systems  

E-print Network

normal behavior and the other(s) the required fault-handling behavior. The specification of the required be expressed as an extension of a system with normal behavior by adding a set of fault-handling componentsA Formal Method for Developing Provably Correct Fault-Tolerant Systems Using Partial Refinement

341

A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant  

E-print Network

A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant D. In order to improve the automatic process control, it is important to develop fault diagnosis strategy. For fault diagnosis, a knowledge based procedure is required. In addition to analytic symptoms, heuristic

Paris-Sud XI, Université de

342

Improved Methods for Fault Diagnosis in Scan-Based BIST Ismet Bayraktaroglu  

E-print Network

Improved Methods for Fault Diagnosis in Scan-Based BIST £ Ismet Bayraktaroglu Computer Science for fault diagnosis in Scan-Based BIST is proposed. The incorporation of the superposition principle benefits, a limitation in its further adop- tion as the main test methodology is inherent fault diagnosis

Bayraktaroglu, Ismet

343

A fault diagnosis method for rolling bearing based on empirical mode decomposition and homomorphic filtering demodulation  

Microsoft Academic Search

A new fault diagnosis method based on empirical mode decomposition (EMD) and homomorphic filtering demodulation is proposed for rolling bearing. The vibration signal of fault rolling bearing is decomposed into a series of intrinsic mode functions (IMFs) by EMD, then extract the envelopes from the outstanding IMFs with various fault characteristic information by homomorphic filtering demodulation and Hilbert envelope demodulation,

Junfa Leng; Shuangxi Jing; Wei Hua

2010-01-01

344

Robust fault tolerant control based on sliding mode method for uncertain linear systems with quantization.  

PubMed

This paper is concerned with the problem of robust fault-tolerant compensation control problem for uncertain linear systems subject to both state and input signal quantization. By incorporating novel matrix full-rank factorization technique with sliding surface design successfully, the total failure of certain actuators can be coped with, under a special actuator redundancy assumption. In order to compensate for quantization errors, an adjustment range of quantization sensitivity for a dynamic uniform quantizer is given through the flexible choices of design parameters. Comparing with the existing results, the derived inequality condition leads to the fault tolerance ability stronger and much wider scope of applicability. With a static adjustment policy of quantization sensitivity, an adaptive sliding mode controller is then designed to maintain the sliding mode, where the gain of the nonlinear unit vector term is updated automatically to compensate for the effects of actuator faults, quantization errors, exogenous disturbances and parameter uncertainties without the need for a fault detection and isolation (FDI) mechanism. Finally, the effectiveness of the proposed design method is illustrated via a model of a rocket fairing structural-acoustic. PMID:23701895

Hao, Li-Ying; Yang, Guang-Hong

2013-09-01

345

Waveguide disturbance detection method  

DOEpatents

A method for detection of a disturbance in a waveguide comprising transmitting a wavefield having symmetric and antisymmetric components from a horizontally and/or vertically polarized source and/or pressure source disposed symmetrically with respect to the longitudinal central axis of the waveguide at one end of the waveguide, recording the horizontal and/or vertical component or a pressure of the wavefield with a vertical array of receivers disposed at the opposite end of the waveguide, separating the wavenumber transform of the wavefield into the symmetric and antisymmetric components, integrating the symmetric and antisymmetric components over a broad frequency range, and comparing the magnitude of the symmetric components and the antisymmetric components to an expected magnitude for the symmetric components and the antisymmetric components for a waveguide of uniform thickness and properties thereby determining whether or not a disturbance is present inside the waveguide.

Korneev, Valeri A. (Albany, CA); Nihei, Kurt T. (Oakland, CA); Myer, Larry R. (Benicia, CA)

2000-01-01

346

Fault detection and isolation of aircraft air data/inertial system  

NASA Astrophysics Data System (ADS)

A method for failure detection and isolation (FDI) for redundant aircraft sensors is presented. The outputs of the concerned sensors are involved in the computation of flight control laws, and the objective is to eliminate any fault before propagation in the control loop when selecting a unique flight parameter among a set (generally, three) of redundant measurements. The particular case of an oscillatory failure is investigated. The proposed method allows an accurate FDI of erroneous sensor and computes a consolidated parameter based on the fusion of data from remaining valid sensors. The benefits of the presented method are to enhance the data fusion process with FDI techniques which improve the performance of the fusion when only few sources (less than three) are initially valid.

Berdjag, D.; Cieslak, J.; Zolghadri, A.

2013-12-01

347

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

NASA Technical Reports Server (NTRS)

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.

Smyth, Padhraic

1994-01-01

348

Holocene paleoearthquakes of the Daqingshan fault detected from knickpoint identification and alluvial soil profile  

NASA Astrophysics Data System (ADS)

Are there any effective methods to reveal paleoearthquakes on normal faults except traditional trenching technique? In this paper, we study Holocene paleoearthquakes of the Daqingshan fault which is a normal fault along the Daqingshan piedmont of Inner Mongolia in China. We identify knickpoints from stream profiles and study alluvial soil profiles to reconstruct the Holocene paleoearthquakes of the fault. From the fault's footwall we extract 25 gullies from IRS-P5 DEM data, and identify knickpoints in the profile that result from fault motion disturbing each channel. We combine the retreat distances and the knickpoint retreat rates to determine each knickpoint's forming time. We study alluvial fan outcrops that contain various paleosol sequences. As three distinct Holocene paleosols developed in the Daqingshan piedmont alluvial fans, we assume that the soil profile development was interrupted by fault activity preserved by interbedded gravel between the paleosols. The gravel layer between two adjacent paleosol layers represents material transported there after a paleoseismic event. Thus we date paleosol layers which are above and below the gravel layer to constrain paleoseismic events. Since trenches had been made by our predecessors along the fault to reveal the Holocene paleoearthquakes, we identify the Holocene paleoearthquake records from both sides of the fault, and then compare the results with the results from the trenches. The final result demonstrates that the knickpoints' sequence in the footwall and the paleosols' ages in the hanging wall correspond very closely with the Holocene paleoearthquakes along the Daqingshan piedmont fault. Methods in this paper have future application value to study paleoearthquakes on other normal faults with similar structure to the Daqingshan fault.

He, Zhongtai; Ma, Baoqi

2015-02-01

349

Gravity interpretation of dipping faults using the variance analysis method  

NASA Astrophysics Data System (ADS)

A new algorithm is developed to estimate simultaneously the depth and the dip angle of a buried fault from the normalized gravity gradient data. This algorithm utilizes numerical first horizontal derivatives computed from the observed gravity anomaly, using filters of successive window lengths to estimate the depth and the dip angle of a buried dipping fault structure. For a fixed window length, the depth is estimated using a least-squares sense for each dip angle. The method is based on computing the variance of the depths determined from all horizontal gradient anomaly profiles using the least-squares method for each dip angle. The minimum variance is used as a criterion for determining the correct dip angle and depth of the buried structure. When the correct dip angle is used, the variance of the depths is always less than the variances computed using wrong dip angles. The technique can be applied not only to the true residuals, but also to the measured Bouguer gravity data. The method is applied to synthetic data with and without random errors and two field examples from Egypt and Scotland. In all cases examined, the estimated depths and other model parameters are found to be in good agreement with the actual values.

Essa, Khalid S.

2013-02-01

350

Simulation of growth normal fault sandbox tests using the 2D discrete element method  

NASA Astrophysics Data System (ADS)

A fault slip can cause the deformation of shallow soil layers and destroy infrastructures. The Shanchiao Fault on the west side of the Taipei Basin is one such fault. The activities of the Shanchiao Fault have caused the quaternary sediment beneath the Taipei Basin to become deformed, damaging structures, traffic construction, and utility lines in the area. Data on geological drilling and dating have been used to determine that a growth fault exists in the Shanchiao Fault. In an experiment, a sandbox model was built using noncohesive sandy soil to simulate the existence of a growth fault in the Shanchiao Fault and forecast the effect of the growth fault on shear-band development and ground differential deformation. The experimental results indicated that when a normal fault contains a growth fault at the offset of the base rock, the shear band develops upward beside the weak side of the shear band of the original-topped soil layer, and surfaces considerably faster than that of the single-topped layer. The offset ratio required is approximately one-third that of the single-cover soil layer. In this study, a numerical simulation of the sandbox experiment was conducted using a discrete element method program, PFC2D, to simulate the upper-covering sand layer shear-band development pace and the scope of a growth normal fault slip. The simulation results indicated an outcome similar to that of the sandbox experiment, which can be applied to the design of construction projects near fault zones.

Chu, Sheng-Shin; Lin, Ming-Lang; Huang, Wen-Chao; Nien, Wei-Tung; Liu, Huan-Chi; Chan, Pei-Chen

2015-01-01

351

Potential of Electrical Resistivity Tomography to Detect Fault Zones in Limestone and Argillaceous Formations in the Experimental Platform of Tournemire, France  

NASA Astrophysics Data System (ADS)

The Experimental platform of Tournemire (Aveyron, France) developed by IRSN (French Institute for Radiological Protection and Nuclear Safety) is located in a tunnel excavated in a clay-rock formation interbedded between two limestone formations. A well-identified regional fault crosscuts this subhorizontal sedimentary succession, and a subvertical secondary fault zone is intercepted in the clay-rock by drifts and boreholes in the tunnel at a depth of about 250 m. A 2D electrical resistivity survey was carried out along a 2.5 km baseline, and a takeout of 40 m was used to assess the potential of this method to detect faults from the ground surface. In the 300 m-thick zone investigated by the survey, electrical resistivity images reveal several subvertical low-resistivity discontinuities. One of these discontinuities corresponds to the position of the Cernon fault, a major regional fault. One of the subvertical conductive discontinuities crossing the upper limestone formation is consistent with the prolongation towards the ground surface of the secondary fault zone identified in the clay-rock formation from the tunnel. Moreover, this secondary fault zone corresponds to the upward prolongation of a subvertical fault identified in the lower limestone using a 3D high-resolution seismic reflection survey. This type of large-scale electrical resistivity survey is therefore a useful tool for identifying faults in superficial layers from the ground surface and is complementary to 3D seismic reflection surveys.

Gélis, C.; Revil, A.; Cushing, M. E.; Jougnot, D.; Lemeille, F.; Cabrera, J.; de Hoyos, A.; Rocher, M.

2010-11-01

352

Practical Methods for Estimating Software Systems Fault Content and Location  

NASA Technical Reports Server (NTRS)

Over the past several years, we have developed techniques to discriminate between fault-prone software modules and those that are not, to estimate a software system's residual fault content, to identify those portions of a software system having the highest estimated number of faults, and to estimate the effects of requirements changes on software quality.

Nikora, A.; Schneidewind, N.; Munson, J.

1999-01-01

353

Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays.  

PubMed

A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches. PMID:16903375

Zheng, Ying; Fang, Huajing; Wang, Hua O

2006-08-01

354

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors.  

PubMed

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented. PMID:22400008

Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

2009-01-01

355

Application of Wavelet Packet Analysis for Fault Detection in Electro-Mechanical Systems Based on Torsional Vibration Measurement  

NASA Astrophysics Data System (ADS)

This paper primarily focuses on detecting electrical faults in turbine generator sets by monitoring torsional vibrations with the help of the non-contact measurement technique and analysing the data acquired from torsional vibration meter. Torsional vibrations in shaft trains can be excited by periodic excitation due to a variety of electromagnetic disturbances or unsteady flow in large steam turbine generator sets and may cause failure in shaft trains. A method is presented to use wavelet packet analysis for fault detection. It is achieved by decomposing the torsional vibration signals in the wavelet packet space at different levels to give finer details. Shannon wavelets have compact supports in frequency domain, it allows the analysis to be carried out in frequency bands of interest. Its effectiveness is verified by experimental results. Moreover, the application of the proposed method can be extended to analysis for transient conditions.

LI, X.; QU, L.; WEN, G.; LI, C.

2003-11-01

356

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors  

PubMed Central

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented. PMID:22400008

Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

2009-01-01

357

Automatic and Scalable Fault Detection for Mobile Applications  

E-print Network

, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications ("apps- ability. For mobile apps, improving reliability is less about making sure that "mission critical" software of reviews on mo- bile app stores shows that an app that crashes is likely to garner poor reviews. Mobile app

Gummadi, Ramakrishna

358

Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method  

NASA Astrophysics Data System (ADS)

Fault diagnosis of rotating machinery is receiving more and more attentions. Vibration signals of rotating machinery are commonly analyzed to extract features of faults, and the features are identified with classifiers, e.g. artificial neural networks (ANNs) and support vector machines (SVMs). Due to nonlinear behaviors and unknown noises in machinery, the extracted features are varying from sample to sample, which may result in false classifications. It is also difficult to analytically ensure the accuracy of fault diagnosis. In this paper, a feature extraction and evaluation method is proposed for fault diagnosis of rotating machinery. Based on the central limit theory, an extraction procedure is given to obtain the statistical features with the help of existing signal processing tools. The obtained statistical features approximately obey normal distributions. They can significantly improve the performance of fault classification, and it is verified by taking ANN and SVM classifiers as examples. Then the statistical features are evaluated with a decoupling technique and compared with thresholds to make the decision on fault classification. The proposed evaluation method only requires simple algebraic computation, and the accuracy of fault classification can be analytically guaranteed in terms of the so-called false classification rate (FCR). An experiment is carried out to verify the effectiveness of the proposed method, where the unbalanced fault of rotor, inner race fault, outer race fault and ball fault of bearings are considered.

Li, Wei; Zhu, Zhencai; Jiang, Fan; Zhou, Gongbo; Chen, Guoan

2015-01-01

359

Dynamic model-based fault detection and diagnosis residual considerations for vapor compression systems  

Microsoft Academic Search

This paper presents a first look at the dynamic impact of faults on vapor compression systems. Low-order control-oriented dynamic models of subcritical vapor compression cycles are used to develop sensitivity tools that enhance the residual design procedure of dynamic model-based fault detection and diagnosis algorithms. Also, experimental results are presented that confirm the sensitive outputs usefulness in an FDD algorithm.

Michael C. Keir; Andrew G. Alleyne

2006-01-01

360

Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms  

Microsoft Academic Search

As a kind of complicated mechanical component, rolling element bearing plays a significant role in rotating machines, and\\u000a bearing fault detection benefits decision-making of maintenance and avoids undesired downtime cost. However, extraction of\\u000a fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposes\\u000a an improved combination of the Hilbert and wavelet

Dong Wang; Qiang Miao; Xianfeng Fan; Hong-Zhong Huang

2009-01-01

361

Bridge Fault Simulation Strategies for CMOS Integrated Circuits Brian Chess  

E-print Network

Bridge Fault Simulation Strategies for CMOS Integrated Circuits Brian Chess Tracy Larrabee \\Lambda present a theorem for detecting feedback bridge faults. We discuss two different methods of bridge fault of the two methods. We con­ clude that the new simulation method, Wire Memory bridge fault simulation

Larrabee, Tracy

362

A ship propulsion system as a benchmark for fault-tolerant control  

Microsoft Academic Search

Fault-tolerant control combines fault detection and isolation techniques with supervisory control, to achieve the autonomous accommodation of faults before they develop into failures. While fault detection and isolation (FDI) methods have matured during the past decade, the extension to fault-tolerant control is a fairly new area. This paper presents a ship propulsion system as a benchmark that should be useful

Roozbeh Izadi-Zamanabadi; Mogens Blanke

1999-01-01

363

Pumping system fault detection and diagnosis utilizing pattern recognition and fuzzy inference techniques  

SciTech Connect

An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.

Singer, R.M.; Gross, K.C. (Argonne National Lab., IL (USA)); Humenik, K.E. (Maryland Univ., Baltimore, MD (USA). Dept. of Computer Science)

1991-01-01

364

An improved distributed Bayesian algorithm for fault-tolerant detection in electromagnetic spectrum monitoring sensor networks  

Microsoft Academic Search

Electromagnetic spectrum monitoring sensor networks (ESMSNs) have become a new distributed solution for the electromagnetic spectrum monitoring and attracted a large scholars' attention due to its better detection performance. However, the detection performance of ESMSNs will decrease rapidly when the faults occur to the monitoring sensor nodes, which result from the node device itself and the harsh or hostile environment

Zhang Yu; Zhao Hangsheng; Liu Qiongli

2011-01-01

365

Fault detection of a flight control computer in a harsh electromagnetic environment  

Microsoft Academic Search

Verifying functional integrity of flight control computers (FCC) in harsh electromagnetic environments is a key issue in development, certification, and operation of systems performing flight critical functions. A strategy is being developed for real-time detection of control command errors caused by electromagnetic environments in FCCs during validation testing. A system level approach to FCC fault detection and mitigation in real

Kenneth W. Eure

2001-01-01

366

Neural network-based detection of mechanical, sensor and biological faults in deep-trough  

E-print Network

fast detection, an on-line identification system is necessary. Detection of a stressed plant is very situations of the plants), namely the ``transpiration fault''. The neural network methodology was proved. The influences of the conditions of the plants to the measured root zone variables are also investigated

Selman, Bart

367

Interval-based Fault Detection and Identification applied to Global Positioning  

E-print Network

Interval-based Fault Detection and Identification applied to Global Positioning Vincent Drevelle a real-time robust positioning system based on interval analysis and constraint propagation that computes position domains, and that is able to detect and reject erroneous measurements. GPS pseudorange

Paris-Sud XI, Université de

368

An innovative Fiber Bragg Grating sensor capable of fault detection in radial power systems  

Microsoft Academic Search

In this paper, a fiber optic based sensor capable of fault detection in power systems is presented. This sensor uses Bragg wavelength shift to measure current in power systems. Magnetic fields generated by currents in power transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). Interrogators sense the reflected FBG signals, and

Amin Moghadas; Ronald Barnes; Mehdi Shadaram

2010-01-01

369

An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO  

NASA Astrophysics Data System (ADS)

This paper presents an intelligent fault identification method of rolling bearings based on least squares support vector machine optimized by improved particle swarm optimization (IPSO-LSSVM). The method adopts a modified PSO algorithm to optimize the parameters of LSSVM, and then the optimized model could be established to identify the different fault patterns of rolling bearings. Firstly, original fault vibration signals are decomposed into some stationary intrinsic mode functions (IMFs) by empirical mode decomposition (EMD) method and the energy feature indexes extraction based on IMF energy entropy is analyzed in detail. Secondly, the extracted energy indexes serve as the fault feature vectors to be input to the IPSO-LSSVM classifier for identifying different fault patterns. Finally, a case study on rolling bearing fault identification demonstrates that the method can effectively enhance identification accuracy and convergence rate.

Xu, Hongbo; Chen, Guohua

2013-02-01

370

Tuning and comparing fault diagnosis methods for aeronautical systems via kriging-based optimization  

NASA Astrophysics Data System (ADS)

Many approaches address fault detection and isolation (FDI) based on analytical redundancy. To rank them, it is necessary to define performance indices and realistic sets of test cases on which they will be evaluated. For the ranking to be fair, each of the methods under consideration should have its internal parameters tuned optimally. The work presented uses a combination of tools developed in the context of computer experiments to achieve this tuning from a limited number of numerical evaluations. The methodology is then extended so as to provide a robust tuning in the worst-case sense.

Marzat, J.; Piet-Lahanier, H.; Damongeot, F.; Walter, E.

2013-12-01

371

Determination of the fault slip distribution of the 1976 Tangshan earthquake by the finite element method  

Microsoft Academic Search

A general method for the determination of the coseismic fault slip distribution by inversion of geodetic data is presented. One type of inverse problem and its solutions are investigated by the finite element and regularization methods. The coseismic fault slip vector is expressed by the solutions of the inverse problem of partial differential equations. The proposed method is used to

Shaorong Zhao; Dingbo Chao

1995-01-01

372

Autoregressive based diagnostics scheme for detection of bearing faults  

Microsoft Academic Search

An investigation into the vibration characteristics of a 'Roots and Claws' based dry vacuum pump under different operating conditions was conducted. An AutoRegressive (AR)-based condition monitoring algorithm was developed and tested on both a fault-free and a pump with an implanted ceramic bearing with an inner race defect at the High Vacuum (HV) end. The investigation provided some in-depth understanding

Suguna Thanagasundram; Fernando Soares Schlindwein

373

Applications of pattern recognition techniques to online fault detection  

SciTech Connect

A common problem to operators of complex industrial systems is the early detection of incipient degradation of sensors and components in order to avoid unplanned outages, to orderly plan for anticipated maintenance activities and to assure continued safe operation. In such systems, there usually are a large number of sensors (upwards of several thousand is not uncommon) serving many functions, ranging from input to control systems, monitoring of safety parameters and component performance limits, system environmental conditions, etc. Although sensors deemed to measure important process conditions are generally alarmed, the alarm set points usually are just high-low limits and the operator`s response to such alarms is based on written procedures and his or her experience and training. In many systems this approach has been successful, but in situations where the cost of a forced outage is high an improved method is needed. In such cases it is desirable, if not necessary, to detect disturbances in either sensors or the process prior to any actual failure that could either shut down the process or challenge any safety system that is present. Recent advances in various artificial intelligence techniques have provided the opportunity to perform such functions of early detection and diagnosis. In this paper, the experience gained through the application of several pattern-recognition techniques to the on-line monitoring and incipient disturbance detection of several coolant pumps and numerous sensors at the Experimental Breeder Reactor-II (EBR-II) which is located at the Idaho National Engineering Laboratory is presented.

Singer, R.M.; Gross, K.C. [Argonne National Lab., IL (United States); King, R.W. [Argonne National Lab., Idaho Falls, ID (United States)

1993-11-01

374

Gearbox fault diagnosis method based on wavelet packet analysis and support vector machine  

Microsoft Academic Search

This paper presents an intelligent method for gear fault diagnosis based on wavelet packet analysis and support vector machine (SVM). For this purpose, two experiments were selected to verify the proposed method. One is a spur gear of the motorcycle gearbox system. Slight-worn, medium-worn, and broken-tooth were selected as the faults. In fault simulating, two very similar models of worn

Jianshe Kanga; Xinghui Zhanga; Jianmin Zhaoa; Hongzhi Teng; Duanchao Caoa

2012-01-01

375

SOM Neural Network Fault Diagnosis Method of Polymerization Kettle Equipment Optimized by Improved PSO Algorithm  

PubMed Central

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective. PMID:25152929

Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

2014-01-01

376

Preliminary Study on Acoustic Detection of Faults Experienced by a High-Bypass Turbofan Engine  

NASA Technical Reports Server (NTRS)

The vehicle integrated propulsion research (VIPR) effort conducted by NASA and several partners provided an unparalleled opportunity to test a relatively low TRL concept regarding the use of far field acoustics to identify faults occurring in a high bypass turbofan engine. Though VIPR Phase II ground based aircraft installed engine testing wherein a multitude of research sensors and methods were evaluated, an array of acoustic microphones was used to determine the viability of such an array to detect failures occurring in a commercially representative high bypass turbofan engine. The failures introduced during VIPR testing included commanding the engine's low pressure compressor (LPC) exit and high pressure compressor (HPC) 14th stage bleed values abruptly to their failsafe positions during steady state

Boyle, Devin K.

2014-01-01

377

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine  

PubMed Central

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

378

Electrical properties and detection methods for CMOS IC defects  

Microsoft Academic Search

CMOS failure modes and mechanisms and the test vector and parametric test requirements for the detection are reviewed. The CMOS stuck-open fault is discussed from a physical viewpoint, with results given from failure analysis of ICs having this failure mode. The results show that among functional, stuck-at, stuck-open, and IDDQ test strategies, no single method guarantees detection of all types

Jerry M. Soden; Charles F. Hawkins

1989-01-01

379

Keys and pitfalls in mesoscale fault analysis and paleostress reconstructions, the use of Angelier's methods  

NASA Astrophysics Data System (ADS)

Whereas most of the stress inversion methods using fault slip data only minimize the angle between the measured striation and a computed shear stress to find the best fitting reduced stress tensor, Angelier (1990) proposed an alternative method named INVD that also takes into account the relative shear stress magnitude which allows the fault to move. Using artificial datasets and particular fault geometries we compare this method with one of the classical methods based on the minimization of the shear-slip angles (R4DT; Angelier, 1984) and we show that in most cases the new method has improved the quality of the results. Furthermore, as proposed by Angelier, we point out that the quality of the stress inversion primarily depends on the quality of the field data. We give advice and warn about some pitfalls concerning determination of sense of slip on fault planes, recognition of successive faulting events and their chronology, drawer (or wedge) faults, stress permutations, faults in vertical bedding. We also argue that, in case of tilted sequences, fault diagrams should not be presented without bedding planes. But we show that stress inversions, when realized with caution and with the correct method, can have much more applications than reconstructing stress fields, like for determining: the paleo-horizontal, the nature and the sense of motion of large faults, the chronology and age of large structures.

Hippolyte, Jean-Claude; Bergerat, Françoise; Gordon, Mark B.; Bellier, Olivier; Espurt, Nicolas

2012-12-01

380

Strike-slip faults imaging from galleries with seismic waveform imaging methods  

NASA Astrophysics Data System (ADS)

Deep argillaceous formations are potential host media for radioactive waste due to their physical properties such as low intrinsic permeability and radionuclide retention (Boisson et al 2001). The experimental station of Tournemire is composed of an old tunnel excavated in 1885 in a 250m thick Toarcien argilitte layer, and of several galleries excavated more recently in directions perpendicular and parallel to the tunnel. This station is operated by the French Institute for Radiological protection and Nuclear Safety (IRSN) in order to expertise possible projects of radioactive waste disposal in a geological clay formation. The presence of secondary strike-slip faults in argillaceous formations must be well assessed since they could change any rock properties such as permeability. The ones with small vertical offsets as observed in the station cannot be seen from the surface, indeed we investigate on new approaches to image them directly from the underground works. We investigate here on the potential of new imaging methods that take advantage of the full seismic waveforms in order to optimise the imaging performances: Full Waveform Inversion (FWI) and Reverse Time Migration (RTM). We try to assess the capacities and limits of those methods in this specific context, and to determine the optimum acquisition and processing parameters. The subvertical fault in the nearly homogeneous subhorizontal structure of the clay layer allows us to consider a 2D imaging problem with no anisotropy where the fault is surrounded by three galleries. The waveform inversion strategy used is based on the frequency domain formulation proposed by Pratt et al. (1990). Non linearity is mitigated by introducing sequentially information from 50Hz to 1000Hz and starting from an homogeneous medium as initial model. Preliminary tests on synthetic data (fig. 1) show the ability of FWI to quantitatively image the fault zone and illustrate the impact of the illumniation configuration. RTM suceeds to detect the interfaces. The various assumptions were experimentaly validated (fig. 2) and the limitations of the imaging process are evaluated.

Bretaudeau, F.; Gélis, C.; Leparoux, D.; Cabrera, J.; Côte, P.

2011-12-01

381

Geophysical imaging of near subsurface layers to detect fault and fractured zones in the Tournemire Experimental Platform, France.  

NASA Astrophysics Data System (ADS)

IRSN (the French Institute for Radiological Protection and Nuclear Safety) is in charge of the expertise of the safety report of the French deep geological disposal site project in the East of France. With the goal of understanding the various transport and mechanical properties of clay-rocks, IRSN has conducted several research programs at the Tournemire Experimental Platform (TEP, in the Department of Aveyron in the South of France). Three major sub-horizontal layers characterize the sedimentary Jurassic formations of the TEP. At the base of the stratigraphic column, we find a sequence of limestones and dolomites, that is overlain by an argillaceous formation composed of a 250 m thick clay-rock layer. Above this layer, there is another sequence of limestones and dolomites. The TEP is characterized by a 2 km long tunnel, which allows in situ access to the Toarcian clay- rock layer. In addition to the main Cernon fault, secondary fault zones affect the clay-rock formation and have been observed in the galleries and also identified in several underground boreholes. These sub-vertical fault zones or fracture network display mainly subhorizontal offset (decametric scale) and a small vertical one (meter scale). In the upper limestone, these fault zones widen and fracturing becomes more scattered. In an attempt to detect fault zones in clay-rock layers such as the one described above, IRSN carried out in 2001 a 3D high-resolution seismic survey from the surface in collaboration with CGG. A sub-vertical fault was successfully picked out by the seismic data at the interface between the clay-rock formation and the underlying limestones. This fault is interpreted as the downward continuation of one of the fault zones identified in the tunnel. However, because of the weak seismic impedance contrast in the clay-rock layer and the small vertical offset of sub-vertical fault zones, these fault zones could not be identified in the clay-rock formation. No fault or fracture zone could either be detected in the upper limestone formation because of the acquisition geometry. In order to better image the clay-rock and upper limestone layers, IRSN, Mines ParisTech and UPPA conducted large-scale 2D and 3D very high-resolution seismic surveys in 2010 and 2011 from the surface in the framework of the GNR TRASSE. We analyze this new dataset with the first arrival traveltime tomography method in order to assess its potential to detect fault and fracture zones in near subsurface layers. For this purpose, we develop a new fast inversion algorithm that allows introducing a priori information and choosing a specific model parameterization. We validate our approach based on the Simultaneous Iterative Reconstruction Technique with synthetic data and present the first results of the new real dataset processing. We finally compare these results to a 2D high-resolution electrical resistivity profile acquired at the same location. These electrical resistivity data could also be considered as some a priori information in our inversion scheme.

Nhu Ba, Elise, Vi; Noble, Mark; Gélis, Céline; Gesret, Alexandrine; Cabrera, Justo

2013-04-01

382

Fault and dyke detectability in high resolution seismic surveys for coal: a view from numerical modelling*  

NASA Astrophysics Data System (ADS)

Modern underground coal mining requires certainty about geological faults, dykes and other structural features. Faults with throws of even just a few metres can create safety issues and lead to costly delays in mine production. In this paper, we use numerical modelling in an ideal, noise-free environment with homogeneous layering to investigate the detectability of small faults by seismic reflection surveying. If the layering is horizontal, faults with throws of 1/8 of the wavelength should be detectable in a 2D survey. In a coal mining setting where the seismic velocity of the overburden ranges from 3000 m/s to 4000 m/s and the dominant seismic frequency is ~100 Hz, this corresponds to a fault with a throw of 4-5 m. However, if the layers are dipping or folded, the faults may be more difficult to detect, especially when their throws oppose the trend of the background structure. In the case of 3D seismic surveying we suggest that faults with throws as small as 1/16 of wavelength (2-2.5 m) can be detectable because of the benefits offered by computer-aided horizon identification and the improved spatial coherence in 3D seismic surveys. With dykes, we find that Berkhout's definition of the Fresnel zone is more consistent with actual experience. At a depth of 500 m, which is typically encountered in coal mining, and a 100 Hz dominant seismic frequency, dykes less than 8 m in width are undetectable, even after migration.

Zhou, Binzhong 13Hatherly, Peter

2014-10-01

383

Methods of DNA methylation detection  

NASA Technical Reports Server (NTRS)

The present invention provides for methods of DNA methylation detection. The present invention provides for methods of generating and detecting specific electronic signals that report the methylation status of targeted DNA molecules in biological samples.Two methods are described, direct and indirect detection of methylated DNA molecules in a nano transistor based device. In the direct detection, methylated target DNA molecules are captured on the sensing surface resulting in changes in the electrical properties of a nano transistor. These changes generate detectable electronic signals. In the indirect detection, antibody-DNA conjugates are used to identify methylated DNA molecules. RNA signal molecules are generated through an in vitro transcription process. These RNA molecules are captured on the sensing surface change the electrical properties of nano transistor thereby generating detectable electronic signals.

Maki, Wusi Chen (Inventor); Filanoski, Brian John (Inventor); Mishra, Nirankar (Inventor); Rastogi, Shiva (Inventor)

2010-01-01

384

The Marshall Space Flight Center Fault Detection Diagnosis and Recovery Laboratory  

NASA Technical Reports Server (NTRS)

The Fault Detection Diagnosis and Recovery Lab (FDDR) has been developed to support development of,fault detection algorithms for the flight computer aboard the Ares I and follow-on vehicles. It consists of several workstations using Ethernet and TCP/IP to simulate communications between vehicle sensors, flight computers, and ground based support computers. Isolation of tasks between workstations was set up intentionally to limit information flow and provide a realistic simulation of communication channels within the vehicle and between the vehicle and ground station.

Burchett, Bradley T.; Gamble, Jonathan; Rabban, Michael

2008-01-01

385

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

386

Abstract-A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter drives during faulty condition  

E-print Network

Abstract-A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter can be used as a diagnostic signal to detect faults and their locations. AI-based techniques are used to perform the fault classification. A neural network (NN) classification is applied to the fault diagnosis

Tolbert, Leon M.

387

EUROPEAN CONFERENCE FOR AEROSPACE SCIENCES Tuning and comparing fault diagnosis methods  

E-print Network

is particularly well-suited to computationally-intensive performance indices, which are the rule if the test cases on fault detection in aeronautics, for instance applied to an air-to-air missile (see Secti

Paris-Sud XI, Université de

388

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks  

PubMed Central

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

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

389

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection  

NASA Astrophysics Data System (ADS)

This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.

Schlechtingen, Meik; Ferreira Santos, Ilmar

2011-07-01

390

Decentralized Detection of a Class of Non-Abrupt Faults With Application to Formations of Unmanned Airships  

Microsoft Academic Search

We propose a decentralized non-abrupt fault detection (DNaFD) scheme for leader-to-follower formations of unmanned airships. Non-abrupt faults are those that result in slow performance degradation and in undesirable drift, which can propagate from one vehicle to another, and therefore can adversely affect mission integrity, potentially destabilizing multivehicle formations, while being difficult to detect. As opposed to model-based fault detectors, which

N. Lechevin; C. A. Rabbath

2009-01-01

391

Combined expert system/neural networks method for process fault diagnosis  

DOEpatents

A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

Reifman, J.; Wei, T.Y.C.

1995-08-15

392

Combined expert system/neural networks method for process fault diagnosis  

DOEpatents

A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

Reifman, Jaques (Westchester, IL); Wei, Thomas Y. C. (Downers Grove, IL)

1995-01-01

393

To err is robotic, to tolerate immunological: fault detection in multirobot systems.  

PubMed

Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space. PMID:25642825

Tarapore, Danesh; Lima, Pedro U; Carneiro, Jorge; Christensen, Anders Lyhne

2015-01-01

394

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

NASA Technical Reports Server (NTRS)

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.

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

2010-01-01

395

Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage  

NASA Technical Reports Server (NTRS)

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.

Lewicki, David G.; Dempsey, Paula J.; Heath, Gregory F.; Shanthakumaran, Perumal

2009-01-01

396

Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis  

PubMed Central

When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks. PMID:25353982

Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

2014-01-01

397

Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis.  

PubMed

When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks. PMID:25353982

Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

2014-01-01

398

Travelling wave fault location in power transmission lines using statistic data analysis methods  

NASA Astrophysics Data System (ADS)

A method used for determination of the distance to the location of a fault in a power transmission line is considered. The method is based on separation of traveling waves upon a short circuit and use of statistic analysis methods for determination of the wave front. The efficiency of the proposed method is verified using a mathematical model of a power transmission line. The results of testing the devices for implementation of the proposed method for fault location are cited.

Lachugin, V. F.; Panfilov, D. I.; Smirnov, A. N.

2014-12-01

399

Development of a fault diagnosis method for heating systems using neural networks  

SciTech Connect

The application of artificial neural networks (ANNs) for developing a fault diagnosis (FD) method in complex heating systems is presented in this paper. The six operating modes with faults used to develop this FD method came from the results of a detailed investigation in cooperation with heating system maintenance experts and are among the most important operating faults for this type of system. Because a daily diagnosis is generally sufficient, the ANNs have been developed using the daily values obtained by a preprocessing of the numerical simulation data. This paper presents the first step of the method development. It demonstrates the feasibility of using ANNs for fault diagnosis of a specific heating, ventilating, and air-conditioning (HVAC) system provided training data representative of the behavior of the system with and without faults are available. The next step will consist of developing a generic method that requires less training data.

Li, X.; Vaezi-Nejad, H.; Visier, J.C. [Centre Scientifique et Technique du Batiment, Marne La Vallee (France). HVAC Dept.

1996-11-01

400

Method of Detecting Simple-shear  

NASA Astrophysics Data System (ADS)

We have derived a method of detecting simple-shear (MODES), which is characteristic of faults and shear zones, using three-dimensional displacements or velocities. In this poster, we present the theory of MODES and illustrate how it works by analyzing a set of displacements measured with the Global Positioning System in a quadrilateral of stations across the 1999 Chi-Chi earthquake ground rupture south of T'ai-chung City, Taiwan [Yu et al., 2001]. The results are illustrated by means of a three-dimensional diagram, the spherical hamburger, which is reminiscent of the seismologist's ``beach-ball.'' The basic assumption of MODES is that the components of a deformation tensor are continuous within a domain of the earth's surface containing survey stations where three components of displacement have been measured. There are no assumptions made about the styles of deformation or the orientation of shear zones and faults. Instead, these quantities are determined by MODES, which consists of three parts: (1) analysis of a deformation tensor to determine whether it contains simple shear and if so determine the orientation of the simple-shear zone in terms of coordinates where S is the direction, ST is the plane, and N is the normal to the plane of simple-shear, (2) calculation of the deformation tensor in the (S, N, T) coordinates, and (3) determination of the importance of the simple shear by comparison of the amount of simple-shear to the amount of pure-shear.

Griffiths, J. H.; Johnson, A. M.

2005-12-01

401

An Intelligent Fault Detection and Isolation Architecture for Antenna Arrays  

NASA Astrophysics Data System (ADS)

This article describes a general architecture for fault modeling, diagnosis, and isolation of the DSN antenna array based on computationally intelligent techniques(neural networks and fuzzy logic). It encompasses a suite of intelligent test and diagnosis algorithms in software. By continuously monitoring the health of the highly complex and nonlinear array observables, the automated diagnosis software will be able to identify and isolate the most likely causes of system failure in cases of faulty operation. Furthermore, it will be able to recommend a series of corresponding corrective actions and effectively act as an automated real-time and interactive system supervisor. In so doing, it will enhance the array capability by reducing the operational workload, increasing science information availability, reducing the overall cost of operation by reducing system downtimes, improving risk management, and making mission planning much more reliable. Operation of this architecture is illustrated using examples from observables available from the 34-meter arraying task.

Rahnamai, K.; Arabshahi, P.; Yan, T.-Y.; Pham, T.; Finley, S. G.

1997-10-01

402

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

PubMed Central

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).

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

2012-01-01

403

ROBUST FAULT DETECTION BASED ON MULTIPLE FUNCTIONAL SERIES TAR MODELS FOR STRUCTURES WITH TIME-DEPENDENT  

E-print Network

aeroelastic effects [1, 2]. For this reason, the FDI in structures with non-stationary vibration response Patras, Greece. ldavendanov@upatras.gr; fassois@mech.upatras.gr ABSTRACT Vibration-based Structural, analyzed and compared within the problem of vibration based fault detection on operating wind turbines

Boyer, Edmond

404

Rapid Deployment with Confidence: Calibration and Fault Detection in Environmental Sensor Networks  

E-print Network

Rapid Deployment with Confidence: Calibration and Fault Detection in Environmental Sensor Networks for Embedded Networked Sensing, UCLA Department of Civil and Environmental Engineering, MIT {nithya, kohler The presence of arsenic in groundwater has led to the largest environmental poisoning in history; tens

Nowak, Robert

405

Distributed Sensor Analysis for Fault Detection in Tightly-Coupled Multi-Robot Team Tasks  

E-print Network

Distributed Sensor Analysis for Fault Detection in Tightly-Coupled Multi-Robot Team Tasks Xingyan Li and Lynne E. Parker Proc. of IEEE International Conference on Robotics and Automation, Kobe, Japan multi-robot team tasks. While the centralized version of SAFDetection was shown to be successful

Parker, Lynne E.

406

APPLICATION OF WAVELET PACKET ANALYSIS FOR FAULT DETECTION IN ELECTROMECHANICAL SYSTEMS BASED ON TORSIONAL VIBRATION MEASUREMENT  

Microsoft Academic Search

This paper primarily focuses on detecting electrical faults in turbine generator sets by monitoring torsional vibrations with the help of the non-contact measurement technique and analysing the data acquired from torsional vibration meter. Torsional vibrations in shaft trains can be excited by periodic excitation due to a variety of electromagnetic disturbances or unsteady flow in large steam turbine generator sets

X. Li; L. Qu; G. Wen; C. Li

2003-01-01

407

High impedance fault detection in EHV series compensated lines using the wavelet transform  

Microsoft Academic Search

Coupling capacitive voltage transformers behave as low pass filters which reject the high frequencies associated with voltage signals, so the effect of HIF on voltage signals is neglected. In addition, using series capacitors (SCs) equipped with metal oxide varistors (MOVs) increases the protection relaying problems and complicates the trip decision. This paper presents a high impedance fault detection algorithm for

E. S. T. Eldin; D. k. Ibrahim; E. M. Aboul-Zahab; S. M. Saleh

2009-01-01

408

Electromagnetic detection of plate hydration due to bending faults at the Middle America Trench  

E-print Network

Electromagnetic detection of plate hydration due to bending faults at the Middle America Trench Keywords: subduction zones marine geophysics electromagnetics conductivity a b s t r a c t Water plays-source electromagnetic imaging to map the electrical resistivity of the crust and uppermost mantle along a 220 km profile

Constable, Steve

409

Joint faults detection in LV switchboard and its global diagnosis, through a Temperature Monitoring System.  

E-print Network

Joint faults detection in LV switchboard and its global diagnosis, through a Temperature Monitoring of monitoring and diagnosis of LV switchboards based on the measurements of currents, ambient temperatures and local temperatures of electrical joints. This system meets the needs to prevent the breakdowns of LV

Paris-Sud XI, Université de

410

A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals  

Microsoft Academic Search

This paper introduces a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. First features are extracted from amplitude demodulated vibration signals obtained from both normal and faulty bearings. The features are based on the reflection coefficients of the polynomial transfer function of the autoregressive model of the vibration signal. These features are

Hasan OCAK; Kenneth A. LOPARO

2001-01-01

411

Coulomb and viscous friction fault detection with application to a pneumatic actuator  

E-print Network

changes. The procedure is illustrated on a high precision servo pneumatic cylinder that drives sensor to the load of the pneumatic cylinder. As W.B. Dunbar is currently a doctoral studentCoulomb and viscous friction fault detection with application to a pneumatic actuator W.B. Dunbar

Dunbar, William

412

The use of Artificial Neural Networks for Sensor Fault Detection, Isolation, and Accommodation in Automotive Engines  

Microsoft Academic Search

The paper describes the hybrid solution, based on Artificial Neural Networks, ANNs, and production rule adopted in the realization of an Instrument Fault Detection, Isolation, and Accommodation scheme for automotive applications. Details on the ANN architectures and training are given together with diagnostic and dynamic performance of the scheme. In the last decade an increasing number of sensors and actuators

Domenico Capriglione; Consolatina Liguori; Cesare Pianese; Antonio Pietrosanto

413

Detection of Fault Zones at Depth Using Low Frequency Induced Sources  

NASA Astrophysics Data System (ADS)

The locations and properties of small fault zones and fractures are of interest to industries including Radioactive Waste Disposal and Deep Underground Mining. At present there is limited knowledge regarding imaging of fault zones with sizes 1 to 100m. We explore the potential of seismic noise, such as that present in tunnels due to excavation processes, to image fractures at depth. Microseismic monitoring is a powerful tool but resolution depends on the characteristics e.g. wavelength and frequency, of the recorded seismic signals. In this study we investigate the role of those characteristics in the identification of fault zones at depth. We use finite element analysis to model a small fault zone within a crystalline host rock; a potential host rock for geological disposal. After an optimization analysis, a 25Hz short duration pulse was used to simulate a seismic source in a rock mass of dimensions 500m x 500m. The thickness and pressure wave speed (Vp) of the fault zone, its orientation, and the location of the pulse were varied. The fault core thickness was varied from <1m to 5m, Vp from 500m/s to 1500m/s while the host rock Vp remained constant at 5000m/s. Two orientations were considered for the fault zone: 1) horizontal and 2) vertical allowing two extremities to be evaluated. The location of the source was considered, 1) directly below the fault zone and 2) at some distance away from the fault zone (100m). Our analysis shows that the frequency of the pulse changes as the wave reflects and refracts due to material property changes as it propagates. The peak wave velocity on arrival at predefined monitoring points demonstrates reduction, giving an indication of attenuation. We show that the original frequency converges to a certain threshold value e.g. approximately 11Hz for a Vp of 5000m/s and 4Hz for a Vp of 500m/s. This threshold is characteristic of the material and the thickness of the layer through which the pulse is propagating. There is a strong linear relationship (R2 > 0.99) between wave propagation velocity in the rock, frequency threshold value and the distance from the seismic source at which this frequency threshold is achieved. Our results suggest that we are able to detect the presence of materials with very different properties, e.g. a fault zone within a rock mass. For example, a 1 m wide fault zone is detectable if there is at least a 30% contrast between the Vp values of the fault zone and the host rock. This requirement is not constant i.e. the minimum difference in Vp reduces as the thickness of the fault zone increases. Our results have direct implications for the novel application of seismic monitoring systems for field detection of sub-seismic scale fracture and fault zones.

Mazzoni, C.; Pytharouli, S.; Lunn, R. J.

2013-12-01

414

Test of two methods for faulting on finite-difference calculations  

USGS Publications Warehouse

Tests of two fault boundary conditions show that each converges with second order accuracy as the finite-difference grid is refined. The first method uses split nodes so that there are disjoint grids that interact via surface traction. The 3D version described here is a generalization of a method I have used extensively in 2D; it is as accurate as the 2D version. The second method represents fault slip as inelastic strain in a fault zone. Offset of stress from its elastic value is seismic moment density. Implementation of this method is quite simple in a finite-difference scheme using velocity and stress as dependent variables.

Andrews, D.J.

1999-01-01

415

Fault detection for hydraulic pump based on chaotic parallel RBF network  

NASA Astrophysics Data System (ADS)

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.

Lu, Chen; Ma, Ning; Wang, Zhipeng

2011-12-01

416

Evaluation of error detection schemes using fault injection by heavy-ion radiation  

Microsoft Academic Search

Several concurrent error detection schemes suitable for a watch-dog processor were evaluated by fault injection. Soft errors were induced into a MC6809E microprocessor by heavy-ion radiation from a Californium-252 source. Recordings of error behavior were used to characterize the errors as well as to determine coverage and latency for the various error detection schemes. The error recordings were used as

U. Gunneflo; Johan Karlsson; Jan Torin

1989-01-01

417

Research on fault line detection device in digital substation of non-effectively grounded system  

Microsoft Academic Search

There are three different structures between process level and bay level in digital substations below 35kV nowadays; they are IEC61850-9-1 standard, IEC61850-9-2 standard and small analog signal transmission standard. Accounting for the features of three kinds of structures and special requirements of non-effectively grounded system fault line detection device, the design of line detection device is suggested in digital substation

Qi Zheng; Bai Ruixuan; Yang Yihan

2010-01-01

418

H_/H? fault detection observer for linear parameter varying systems  

NASA Astrophysics Data System (ADS)

This paper addresses the mixed H_/H? fault detection observer design issue for a class of linear parameter varying (LPV) system. Based on the quadratic H? performance , as well as the corresponding quadratic H_ index performance for measuring the worst-case fault sensitivity of the underlying LPV system, the existence conditions of such an observer are given in terms of linear matrix inequalities (LMIs). An algorithm is given to achieve the solutions. An example is studied to demonstrate the effectiveness of the proposed algorithms.

Liu, Lihua; Wei, Xiukun

2011-10-01

419

Fault detection in railway track using piezoelectric impedance  

NASA Astrophysics Data System (ADS)

In this research, piezoelectric transducers are incorporated in an impedance-based damage detection approach for railway track health monitoring. The impedance-based damage detection approach utilizes the direct relationship between the mechanical impedance of the track and electrical impedance of the piezoelectric transducer bonded. The effect of damage is shown in the change of a healthy impedance curve to an altered, damaged curve. Using a normalized relative difference outlier analysis, the occurrences of various damages on the track are determined. Furthermore, the integration of inductive circuitry with the piezoelectric transducer is found to be able to considerably increase overall damage detection sensitivity.

Cremins, M.; Shuai, Qi; Xu, Jiawen; Tang, J.

2014-04-01

420

Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform  

NASA Astrophysics Data System (ADS)

In order to enhance the desired features related to some special type of machine fault, a technique based on the dual-tree complex wavelet transform (DTCWT) is proposed in this paper. It is demonstrated that DTCWT enjoys better shift invariance and reduced spectral aliasing than second-generation wavelet transform (SGWT) and empirical mode decomposition by means of numerical simulations. These advantages of the DTCWT arise from the relationship between the two dual-tree wavelet basis functions, instead of the matching of the used single wavelet basis function to the signal being analyzed. Since noise inevitably exists in the measured signals, an enhanced vibration signals denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of vibration signals resulting from a crack gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared to those DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, excavation of comprehensive signatures embedded in the vibration signals is of practical importance to clearly clarify the roots of the fault, especially the combined faults. In the case of multiple features detection, diagnosis results of rolling element bearings with combined faults and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and consistently outperforms SGWT and fast kurtogram, which are widely used recently. Moreover, it must be noted, the proposed method is completely suitable for on-line surveillance and diagnosis due to its good robustness and efficient algorithm.

Wang, Yanxue; He, Zhengjia; Zi, Yanyang

2010-01-01

421

A fault tolerant approach to state estimation and failure detection in nonlinear systems  

NASA Technical Reports Server (NTRS)

The design problem considered in the present investigation involves a nonlinear discrete time stochastic system where replicated sensors provide redundant observations of inputs and outputs of the system, compensate for sensor 'normal operating' bias levels, and generate reliable estimates for the plant states in the presence of possible sensor failures. The resulting fault tolerant design should utilize inherent analytical redundancy and be capable of detecting many different types and levels of sensor failures. In addition, it should have minimal complexity. In connection with these goals, a sensor fault tolerant system design methodology is developed. The performance of the considered approach to an application is discussed, taking into account the design of a sensor fault tolerant system using analytic redundancy for the Terminal Configured Vehicle research aircraft in a microwave landing system environment.

Lancraft, R. E.; Caglayan, A. K.

1982-01-01

422

Induction Motors Bearing Failures Detection and Diagnosis Using a RBF ANN Park Pattern Based Method  

E-print Network

Induction Motors Bearing Failures Detection and Diagnosis Using a RBF ANN Park Pattern Based Method of bearing failure detection and diagnosis in induction motors. The proposed approach is a sensor to automate the fault detection and diagnosis process. Experimental tests with artificial bearing damages

Paris-Sud XI, Université de

423

COMPARING DETECTION METHODS OF AFLATOXIN AND EXPLORING AFLATOXIN DECONTAMINATION METHODS  

E-print Network

COMPARING DETECTION METHODS OF AFLATOXIN AND EXPLORING AFLATOXIN DECONTAMINATION METHODS By Rebecca DETECTION METHODS OF AFLATOXIN AND EXPLORING AFLATOXIN DECONTAMINATION METHODS By Rebecca Burgett. Ashli Brown Title of Study: COMPARING DETECTION METHODS OF AFLATOXIN AND EXPLORING AFLATOXIN

Ray, David

424

Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures  

NASA Technical Reports Server (NTRS)

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

Matthews, Bryan L.; Srivastava, Ashok N.

2010-01-01

425

Fault Detection in an Ethernet Network Using Anomaly Signature Matching  

Microsoft Academic Search

In an Ethernet network, a common type of failure is the temporary of extended loss of bandwidth, or failure as it is referred to in the literature. Though the causes of soft failures vary, to the network user such failures are perceived as noticeably degraded or anomalous performance.This work uses anomaly detection as a means to signal performance degradations that

Frank Feather; Daniel P. Siewiorek; Roy A. Maxion

1993-01-01

426

Fault-Tolerant Concept Detection in Information Networks  

E-print Network

and their properties, how can we automatically discover that Aspirin has blood-thinning properties, and thus prevents, and the other describing their chemical behavior, how can we discover whether, for ex- ample, Aspirin has blood uses of Aspirin (Acetylsalicylic acid) that have been detected by our proposed algorithm. In a nutshell

427

A robust data fusion scheme for integrated navigation systems employing fault detection methodology augmented with fuzzy adaptive filtering  

NASA Astrophysics Data System (ADS)

Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be applied to the navigation system of aircraft or unmanned aerial vehicle (UAV).

Ushaq, Muhammad; Fang, Jiancheng

2013-10-01

428

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

PubMed

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. PMID:22902083

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

2012-11-01

429

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

SciTech Connect

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.

Zhang Yumin; Lum, Kai-Yew [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Wang Qingguo [Depa. Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

2009-03-05

430

Spin-system dynamics and fault detection in threshold networks  

SciTech Connect

We consider an agent on a fixed but arbitrary node of a known threshold network, with the task of detecting an unknown missing link. We obtain analytic formulas for the probability of success when the agent's tool is the free evolution of a single excitation on an XX spin system paired with the network. We completely characterize the parameters, which allows us to obtain an advantageous solution. From the results emerges an optimal (deterministic) algorithm for quantum search, from which a quadratic speedup with respect to the optimal classical analog and in line with well-known results in quantum computation is gained. When attempting to detect a faulty node, the chosen setting appears to be very fragile and the probability of success too small to be of any direct use.

Kirkland, Steve; Severini, Simone [Hamilton Institute, National University of Ireland, Maynooth County Kildare (Ireland); Department of Physics and Astronomy, University College London, WC1E 6BT London (United Kingdom)

2011-01-15

431

512 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fault Detection and Diagnosis in an Induction  

E-print Network

the diagnosis process focuses on the operation of the inverter. Faults that may occur within the machine512 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fault Detection and Diagnosis in an Induction Machine Drive: A Pattern Recognition Approach Based on Concordia Stator Mean

Paris-Sud XI, Université de

432

Fault detection, identification and estimation in the electro-hydraulic actuator system using EKF-based multiple-model estimation  

Microsoft Academic Search

In this paper, a fault detection, identification and estimation approach has been developed for the condition monitoring of the electro-hydraulic actuator (EHA) system using the multiple-model (MM) estimation algorithm. The MM estimation algorithm makes use of the extended Kalman filter (EKF) technique to generate estimates of states and key physical parameters, which are related to faults in the EHA system.

Xudong Wang; Vassilis L. Syrmos

2008-01-01

433

Isolability of faults in sensor fault diagnosis  

NASA Astrophysics Data System (ADS)

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.

Sharifi, Reza; Langari, Reza

2011-10-01

434

Comparing Detection Methods for Software Requirements Inspections: A Replicated Experiment  

Microsoft Academic Search

Software requirements specifications (SRS) are often validated manually. One such process is inspection, in which several reviewers independently analyze all or part of the specification and search for faults. These faults are then collected at a meeting of the reviewers and author(s). Usually, reviewers use Ad Hoc or Checklist methods to uncover faults. These methods force all reviewers to rely

Adam A. Porter; Lawrence G. Votta; Victor R. Basili

1995-01-01

435

Fault detection, isolation, and recovery for autonomous parafoils  

E-print Network

Autonomous precision airdrop systems are widely used to deliver supplies to remote locations. This aerial delivery method provides a safety and logistical advantage over traditional ground- or helicopter-based payload ...

Stoeckle, Matthew Robert

2014-01-01

436

Chaotic Extension Neural Network Theory-Based XXY Stage Collision Fault Detection Using a Single Accelerometer Sensor  

PubMed Central

The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced. PMID:25405512

Hsieh, Chin-Tsung; Yau, Her-Terng; Wu, Shang-Yi; Lin, Huo-Cheng

2014-01-01

437

Chaotic extension neural network theory-based XXY stage collision fault detection using a single accelerometer sensor.  

PubMed

The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced. PMID:25405512

Hsieh, Chin-Tsung; Yau, Her-Terng; Wu, Shang-Yi; Lin, Huo-Cheng

2014-01-01

438

Disk Crack Detection for Seeded Fault Engine Test  

NASA Technical Reports Server (NTRS)

Work was performed to develop and demonstrate vibration diagnostic techniques for the on-line detection of engine rotor disk cracks and other anomalies through a real engine test. An existing single-degree-of-freedom non-resonance-based vibration algorithm was extended to a multi-degree-of-freedom model. In addition, a resonance-based algorithm was also proposed for the case of one or more resonances. The algorithms were integrated into a diagnostic system using state-of-the- art commercial analysis equipment. The system required only non-rotating vibration signals, such as accelerometers and proximity probes, and the rotor shaft 1/rev signal to conduct the health monitoring. Before the engine test, the integrated system was tested in the laboratory by using a small rotor with controlled mass unbalances. The laboratory tests verified the system integration and both the non-resonance and the resonance-based algorithm implementations. In the engine test, the system concluded that after two weeks of cycling, the seeded fan disk flaw did not propagate to a large enough size to be detected by changes in the synchronous vibration. The unbalance induced by mass shifting during the start up and coast down was still the dominant response in the synchronous vibration.

Luo, Huageng; Rodriguez, Hector; Hallman, Darren; Corbly, Dennis; Lewicki, David G. (Technical Monitor)

2004-01-01

439

Fault detection of aircraft system with random forest algorithm and similarity measure.  

PubMed

Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. PMID:25057508

Lee, Sanghyuk; Park, Wookje; Jung, Sikhang

2014-01-01

440

Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure  

PubMed Central

Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. PMID:25057508

Park, Wookje; Jung, Sikhang

2014-01-01

441

A microprocessor-based digital feeder monitor with high-impedance fault detection  

SciTech Connect

The high impedance fault detection technology developed at Texas A&M University after more than a decade of research, funded in large part by the Electric Power Research Institute, has been incorporated into a comprehensive monitoring device for overhead distribution feeders. This digital feeder monitor (DFM) uses a high waveform sampling rate for the ac current and voltage inputs in conjunction with a high-performance reduced instruction set (RISC) microprocessor to obtain the frequency response required for arcing fault detection and power quality measurements. Expert system techniques are employed to assure security while maintaining dependability. The DFM is intended to be applied at a distribution substation to monitor one feeder. The DFM is packaged in a non-drawout case which fits the panel cutout for a GE IAC overcurrent relay to facilitate retrofits at the majority of sites were electromechanical overcurrent relays already exist.

Patterson, R.; Tyska, W. [GE Protection and Control, Malvern, PA (United States); Russell, B.D. [Texas A& M Univ., College Station, TX (United States)] [and others

1994-12-31

442

A microprocessor-based digital feeder monitor with high-impedance & fault detection  

SciTech Connect

The high impedance fault detection technology developed at Texas A&M University after more than a decade of research has been incorporated into a comprehensive monitoring device for overhead distribution feeders. This digital feeder monitor (DFM) uses a high waveform sampling rate for the ac current and voltage inputs in conjunction with a high-performance reduced instruction set (RISC) microprocessor to obtain the frequency response required for arcing fault detection and power quality measurements. Expert system techniques are employed to assure security while maintaining dependability. The DFM is intended to be applied at a distribution substation to monitor one feeder. The DFM is packaged in a non-drawout case which fits the panel cutout for a GE IAC overcurrent relay to facilitate retrofits at the majority of sites where electromechanical overcurrent relays already exist.

Patterson, R.; Tyska, W. [GE Protection and Control, Malvern, PA (United States); Russell, B.D.; Aucoin, B.M.

1994-12-31

443

Application of particle swarm optimization and proximal support vector machines for fault detection  

Microsoft Academic Search

This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence\\u000a (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary\\u000a PSO algorithms have been considered along with linear and nonlinear versions of PSVM. The time domain vibration signals of\\u000a a rotating machine with normal and defective bearings

Biswanath Samanta; Chandrasekhar Nataraj

2009-01-01

444

A New Single-Phase-to-Ground Fault-Detecting Relay  

Microsoft Academic Search

In the application of differential relays the need for a supervising relay which will detect the existence of a single-phase-to-ground fault condition to the exclusion of all others has frequently arisen. Heretofore, the only scheme available has been to utilize a relay energized by zero-sequence quantities. Generally, the relay has been energized by a current transformer connected in the station

W. K. Sonnemann

1942-01-01

445

Paleostress reconstruction from calcite twin and fault-slip data using the multiple inverse method in the East Walanae fault zone: Implications for the Neogene contraction in South Sulawesi, Indonesia  

NASA Astrophysics Data System (ADS)

A new approach for paleostress analysis using the multiple inverse method with calcite twin data including untwinned e-plane was performed in the East Walanae fault (EWF) zone in South Sulawesi, Indonesia. Application of untwinned e-plane data of calcite grain to constrain paleostress determination is the first attempt for this method. Stress states caused by the collision of the south-east margin of Sundaland with the Australian microcontinents during the Pliocene were successfully detected from a combination of calcite-twin data and fault-slip data. This Pliocene NE-SW-to-E-W-directed maximum compression activated the EWF as a reverse fault with a dextral component of slip with pervasive development of secondary structures in the narrow zone between Bone Mountain and Walanae Depression.

Jaya, Asri; Nishikawa, Osamu

2013-10-01

446

Tunnel Detection Using Seismic Methods  

Microsoft Academic Search

Surface seismic methods have shown great promise for use in detecting clandestine tunnels in areas where unauthorized movement beneath secure boundaries have been or are a matter of concern for authorities. Unauthorized infiltration beneath national borders and into or out of secure facilities is possible at many sites by tunneling. Developments in acquisition, processing, and analysis techniques using multi-channel seismic

R. Miller; C. B. Park; J. Xia; J. Ivanov; D. W. Steeples; N. Ryden; R. F. Ballard; J. L. Llopis; T. S. Anderson; M. L. Moran; S. A. Ketcham

2006-01-01

447

Fault Detection in Distributed Climate Sensor Networks using Dynamic Bayesian Networks  

SciTech Connect

The Atmospheric Radiation Measurement program operated by U.S. Department of Energy is one of the largest climate research programs dedicated to the collection of long-term continuous measurements of cloud properties and other key components of the earth’s climate system. Given the critical role that collected ARM data plays in the analysis of atmospheric processes and conditions and in the enhancement and evaluation of global climate models, the production and distribution of high-quality data is one of ARM’s primary mission objectives. Fault detection in ARM’s distributed sensor network is one critical ingredient towards maintaining high quality and useful data. We are modeling ARM’s distributed sensor network as a dynamic Bayesian network where key measurements are mapped to Bayesian network variables. We then define the conditional dependencies between variables by discovering highly correlated variable pairs from historical data. The resultant dynamic Bayesian network provides an automated approach to identifying whether certain sensors are malfunctioning or failing in the distributed sensor network. A potential fault or failure is detected when an observed measurement is not consistent with its expected measurement and the observed measurements of other related sensors in the Bayesian network. We present some of our experiences and promising results with the fault detection dynamic Bayesian network.

Chin, George; Choudhury, Sutanay; Kangas, Lars J.; McFarlane, Sally A.; Marquez, Andres

2010-12-07

448

Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design  

E-print Network

.5.2 System implementation.............................................................................. 121 5.5.3 Normal working status ............................................................................... 123 5.5.4 Abnormality diagnosis... on assembly-1 ........................................... 98 Figure 24 Abnormal event detection logic .................................................................. 100 Figure 25 PN diagnoser for fault to close gripper...

Wu, Zhenhua

2012-07-16

449

Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas  

E-print Network

Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon.j.daigle,xenofon.koutsoukos,gautam.biswas@vanderbilt.edu Abstract-- Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems fault isolation in systems with complex continuous dynamics. This paper presents a novel discrete- event

Koutsoukos, Xenofon D.

450

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

NASA Technical Reports Server (NTRS)

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.

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

2007-01-01

451

Fault detection and isolation of PEM fuel cell system based on nonlinear analytical redundancy. An application via parity space approach  

NASA Astrophysics Data System (ADS)

This paper presents a procedure dealing with the issue of fault detection and isolation (FDI) using nonlinear analytical redundancy (NLAR) technique applied in a proton exchange membrane (PEM) fuel cell system based on its mathematic model. The model is proposed and simplified into a five orders state space representation. The transient phenomena captured in the model include the compressor dynamics, the flow characteristics, mass and energy conservation and manifold fluidic mechanics. Nonlinear analytical residuals are generated based on the elimination of the unknown variables of the system by an extended parity space approach to detect and isolate actuator and sensor faults. Finally, numerical simulation results are given corresponding to a faults signature matrix.

Aitouche, A.; Yang, Q.; Ould Bouamama, B.

2011-05-01

452

Fault diagnosis method for power transformer based on ant colony SVM classifier  

Microsoft Academic Search

Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector

Niu Wu; Xu Liangfa; Hu Sanguo

2010-01-01

453

Fault classification method for inverter based on hybrid support vector machines and wavelet analysis  

Microsoft Academic Search

A new classification method for fault waveform is proposed based on discrete orthogonal wavelet transform (DOWT) and hybrid\\u000a support vector machine (hybrid SVM) for fault type of a three-phase voltage inverter. The waveforms of output voltage obtained\\u000a from the faulty inverter are decomposed by DOWT into wavelet coefficient matrices, through which we can obtain singular value\\u000a vectors acted as features

Zhi-kun Hu; Wei-hua Gui; Chun-hua Yang; Peng-cheng Deng; Steven X. Ding

2011-01-01

454

Application of data fusion method to fault diagnosis of nuclear power plant  

NASA Astrophysics Data System (ADS)

The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.

Xie, Chun-Li; Xia, Hong; Liu, Yong-Kuo

2005-03-01

455

Fault-tolerant linear optics quantum computation by error-detecting quantum state transfer  

E-print Network

A scheme for linear optical implementation of fault-tolerant quantum computation is proposed, which is based on an error-detecting code. Each computational step is mediated by transfer of quantum information into an ancilla system embedding error-detection capability. Photons are assumed to be subjected to both photon loss and depolarization, and the threshold region of their strengths for scalable quantum computation is obtained, together with the amount of physical resources consumed. Compared to currently known results, the present scheme reduces the resource requirement, while yielding a comparable threshold region.

Jaeyoon Cho

2007-10-07

456

Leak detection method and apparatus  

SciTech Connect

A method and apparatus are described for using sulfur hexafluoride to detect leaks in fluid processing systems. Leak detection can be performed with the processing system continuing in operation. This apparatus detects leakage through a partition separating a portion of a first path from portion of a second path in a fluid processing system, while operation of the system is continued. The apparatus comprises a combination of 1) means for introducing a known quantity of sulfur hexafluoride into fluid flowing in the first path upstream of a partition; 2) means for continuously removing a sample of fluid flowing in the second path at a locus downstream of the partition; 3) means for removing normally liquid components from the sample; 4) means for testing the sample to determine the presence of sulfur hexafluoride; and 5) means for indicating the amount of sulfur hexafluoride in the sample. 2 claims.

Fries, B.A.

1982-05-11

457

Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage  

NASA Technical Reports Server (NTRS)

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 [Ed. 's note: See Appendix A-Definitions D 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 perfomu:d average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant 8lDOunt 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.

Lewicki, David G.; Dempsey, Paula J.; Heath, Gregory F.; Shanthakumaran, Perumal

2010-01-01

458

Method for detecting toxic gases  

DOEpatents

A method is disclosed which is capable of detecting low concentrations of a pollutant or other component in air or other gas. This method utilizes a combination of a heating filament having a catalytic surface of a noble metal for exposure to the gas and producing a derivative chemical product from the component. An electrochemical sensor responds to the derivative chemical product for providing a signal indicative of the product. At concentrations in the order of about 1-100 ppm of tetrachloroethylene, neither the heating filament nor the electrochemical sensor is individually capable of sensing the pollutant. In the combination, the heating filament converts the benzyl chloride to one or more derivative chemical products which may be detected by the electrochemical sensor. 6 figures.

Stetter, J.R.; Zaromb, S.; Findlay, M.W. Jr.

1991-10-08

459

Automatic Channel Fault Detection and Diagnosis System for a Small Animal APD-Based Digital PET Scanner  

E-print Network

Fault detection and diagnosis is critical to many applications in order to ensure proper operation and performance over time. Positron emission tomography (PET) systems that require regular calibrations by qualified scanner operators are good candidates for such continuous improvements. Furthermore, for scanners employing one-to-one coupling of crystals to photodetectors to achieve enhanced spatial resolution and contrast, the calibration task is even more daunting because of the large number of independent channels involved. To cope with the additional complexity of the calibration and quality control procedures of these scanners, an intelligent system (IS) was designed to perform fault detection and diagnosis (FDD) of malfunctioning channels. The IS can be broken down into four hierarchical modules: parameter extraction, channel fault detection, fault prioritization and diagnosis. Of these modules, the first two have previously been reported and this paper focuses on fault prioritization and diagnosis. The purpose of the fault prioritization module is to help the operator to zero in on the faults that need immediate attention. The fault diagnosis module will then identify the causes of the malfunction and propose an explanation of the reasons that lead to the diagnosis. The FDD system was implemented on a LabPET avalanche photodiode (APD)-based digital PET scanner. Experiments demonstrated a FDD Sensitivity of 99.3 % (with a 95% confidence interval (CI) of: [98.7, 99.9]) for major faults. Globally, the Balanced Accuracy of the diagnosis for varying fault severities is 92 %. This suggests the IS can greatly benefit the operators in their maintenance task.

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

2014-06-16

460

Analysis of Space Shuttle Ground Support System Fault Detection, Isolation, and Recovery Processes and Resources  

NASA Technical Reports Server (NTRS)

As part of the FDIR (Fault Detection, Isolation, and Recovery) Project for the Constellation Program, a task was designed within the context of the Constellation Program FDIR project called the Legacy Benchmarking Task to document as accurately as possible the FDIR processes and resources that were used by the Space Shuttle ground support equipment (GSE) during the Shuttle flight program. These results served as a comparison with results obtained from the new FDIR capability. The task team assessed Shuttle and EELV (Evolved Expendable Launch Vehicle) historical data for GSE-related launch delays to identify expected benefits and impact. This analysis included a study of complex fault isolation situations that required a lengthy troubleshooting process. Specifically, four elements of that system were considered: LH2 (liquid hydrogen), LO2 (liquid oxygen), hydraulic test, and ground special power.

Gross, Anthony R.; Gerald-Yamasaki, Michael; Trent, Robert P.

2009-01-01