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The mine hoist operation status is closely related to the vibration signal of the hoist various components. using optical fiber sensing technology, this paper designed a hoist faultdiagnosis system based on vibration spectrum analysis. Through rapid demodulation of real-time vibration signal, the system realized vibration spectrum analysis to various parts of the hoist. The test results show that the system can achieve effective monitoring of the various parts of the hoist operating status, provide an important basis for faultdiagnosis.
Torsional vibration signals are theoretically free from the amplitude modulation effect caused by time variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore their spectral structure are simpler than transverse vibration signals. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via torsional vibration signal analysis. We give explicit equations to model torsional vibration signals, considering both distributed gear faults (like manufacturing or assembly errors) and local gear faults (like pitting, crack or breakage of one tooth), and derive the characteristics of both the traditional Fourier spectrum and the proposed demodulated spectra of amplitude envelope and instantaneous frequency. These derivations are not only effective to diagnose single gear fault of planetary gearboxes, but can also be generalized to detect and locate multiple gear faults. We validate experimentally the signal models, as well as the Fourier spectral analysis and demodulation analysis methods.
There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The faultdiagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, faultdiagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A novel method for modelling the vibration signal based on the adaptive wavelet transform (AWT) is proposed. The linear combination of wavelets is introduced as wavelet itself and adapted for the particular vibration signal, which goes beyond adapting parameters of a fixed-shape wavelet. The AWT procedure based on the parametric optimisation by genetic algorithm (GA) is developed. The model-based method by AWT is applied to extract the features in the faultdiagnosis of the water hydraulic motor. This technique for de-noising the corrupted simulation signal shows that it can improve the signal-to-noise ratio of the vibration signal. The results of the experimental signal demonstrate the characteristic vibration signal details in fine resolution. The magnitude plots of the continuous wavelet transform (CWT) show the characteristic signal's energy in time and frequency domain which can be used as feature values for faultdiagnosis of water hydraulic motor.
This paper presents an approach for the faultdiagnosis in induction motors by using Dempster-Shafer theory. Features are extracted from motor stator current and vibration signals and with reducing data transfers. The technique makes it possible for on-line application. Neural network is trained and tested by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real-time applications.
A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for faultdiagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for faultdiagnosis.
Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.
Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery faultdiagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery faultdiagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in faultdiagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery faultdiagnosis the method is superior to two traditional denoising methods. PMID:24379045
The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.
Condition monitoring and faultdiagnosis is an important issue for gearbox maintenance and safety. The critical process involved in such activities is to extract reliable features representative of the condition of the gears or gearbox. In this paper a framework is presented for the application of bispectrum to the analysis of gearbox vibration. The bispectrum of a composite signal consisting of multiple periodic components has peaks at the bifrequencies that correspond to closely related components which can be produced by any nonlinearity. As a result, biphase verification is necessary to decrease false-alarming for any bispectrum-based method. A model based on modulated signals is adopted to reveal the bispectrum characteristics for the vibration of a faulty gear, and the corresponding amplitude and phase of the bispectrum expression are deduced. Therefore, a diagnostic approach based on the theoretical result is derived and verified by the analysis of a set of vibration signals from a helicopter gearbox.
Guoji, Shen; McLaughlin, Stephen; Yongcheng, Xu; White, Paul
Bilinear time-frequency transformation can possess a simultaneous high resolution both in the time domain and the frequency domain. It can be utilised to detect weak transient vibration signals generated by early mechanical faults in complex background and thus is of great importance to early mechanical fault diagnoses. It has been found that the spectrogram has low resolution, and there is strong cross-terms in Wigner-Ville distribution and frequency aliasing and information loss in Choi-Williams distribution (CWD). Hence, they cannot achieve satisfied transient signal detection results. To enhance the capability of detecting transient vibration signals, based on the analysis of exponent distribution, this paper presents some novel alias-free time-frequency distributions. These distributions can avoid the information loss in CWD while suppressing the cross-terms. Moreover, they have high simultaneous resolutions in both the time and frequency domain. Digital simulation and gearbox faultdiagnosis experiments prove that these new distributions can effectively detect transient components from complicated mechanical vibration signals.
Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based faultdiagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the faultdiagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949
This paper examines the faultdiagnosis (including detection and localization) of a Flux Switching permanent magnet Motor (FSM). Electrical and mechanical models of fault due to a partial short-circuit on one phase is described and used on a numerical simulation in order to detect and localize the fault. Fault detection is realized by a diagnostic observer (Kalman filter) which generates
Stator and rotor vibration characteristics of generator are analyzed when the rotor winding inter-turn short circuit fault and the imbalance fault occur. The rotor vibration of fundamental frequency increases and the stator vibration of second frequency decreases when the rotor winding inter-turn short circuit fault occurs. But when the imbalance fault occurs, the rotor vibration of fundamental frequency increases and
According to turbine-generator vibration characteristic spectrum, a discretized generator fault attribute decision table and condition. attribute set reduction method based on rough set theory are presented in this paper, though the key character which influences classifying is picked up. BP network input dimension is reduced and training time is saved. Experiment shows that the result is effective.
Ou Jian; Sun Cai-xin; Bi Weimin; Zhang Bide; Liao Ruijin
A DSP-based measurement system dedicated to the vibration analysis on rotating machines was designed and realized. Vibration signals are on-line acquired and processed to obtain a continuous monitoring of the machine status. In case of fault, the system is capable of isolating the fault with a high reliability. The paper describes in detail the approach followed to built up fault
Giovanni Betta; Consolatina Liguori; A. Paolillo; A. Pietrosanto
A summary of several research projects in the nonlinear faultdiagnosis is given. Several alternative algorithms for the solution of the nonlinear faultdiagnosis problem are presented, together with a diagnosibility theory, and a set of criteria which an...
R. W. Liu K. Nakajima P. Olivier Q. D. Ngo R. Saeks
A DSP-based measurement system dedicated to the vibration analysis on rotating machines was designed and realized. Vibration signals are on-line acquired and processed to obtain a continuous monitoring of the machine status. In case of a fault, the system is capable of isolating the fault with a high reliability. The paper describes in detail the approach followed to built up
Giovanni Betta; Consolatina Liguori; Alfredo Paolillo; Antonio Pietrosanto
In this dissertation, two approaches are studied for the case of bearing anomaly detection. One approach is to regard it as a blind source separation (cocktail party) problem and take advantage of statistical and mathematical methods developed for this purpose, primarily independent component analysis (ICA), to separate signals coming from different sources. The other approach is to avoid making the effort to 'separate' the signals and relate them to different components (sources) and instead make use of the specification and characteristics of vibration signals produced by the different components in normal and faulty conditions. In the first approach, a common difficulty with applying blind source separation techniques (or, in general any mathematical methods) to separation of vibration sources is that no standard measure exists to assess the quality of separation and validate the results. In fact, for an ideal assessment the true original signals produced by each component must be available as a prerequisite. This requires gathering signals from each component in strict isolation during operation in a lab environment which, if not impossible, is very costly and difficult. To alleviate this difficulty, a novel method is developed that presents the distribution of vibration energy with regard to the respective locations of vibration sources and sensors, and takes into consideration the mechanical attributes of the structure. This method uses some key concepts from statistical energy analysis (SEA) to support the fact that each sensor collects a different version of the oscillations produced in the system with respect to its location in the system. Therefore, by comparing the spectral signature of the vibration signals and making use of a priori knowledge of the spatial distribution of sensors and components, a schematic representation of the spectral signature of the vibration sources are obtained. This method is verified using a series of experiments with synthetic and real data. If a standard evaluation metric is available, more rigorous evaluation of blind source separation techniques can be achieved. The foremost existing solution to blind source separation is Independent Component Analysis (ICA). In ICA it is assumed that the source signals are statistically independent from one another and can therefore be recovered by formulating the independence. There are, however, two dominant ambiguities and indeterminacies associated with ICA results. One ambiguity is that the original index or permutation of the recovered source signals is unknown. The other ambiguity is that the actual scale of the source signals cannot be determined. ICA can be applied in both time and frequency domains. In this dissertation, a new technique is proposed based mainly on the mechanical attributes of the system rather than unrealistic mathematical or statistical assumptions. This technique is developed based on the presumption that the mixing mechanism for neighboring frequency bins varies only slightly from one bin to another. Therefore, by numerically tying and relating the mixing matrices of contiguous frequency bins, local permutation and scale indeterminacy problems are resolved. This method is studied experimentally using laboratory data and the results are also compared with the evaluation metric presented in the previous study. Accordance between the results confirmed the efficacy of the proposed method. In the second approach, the effectiveness of cyclic spectral analysis is assessed for detecting bearing faults in complex machinery. Bearing faults are known to produce vibration with recurring impulsiveness in the energy which is referred to as cyclostationarity. Cyclic spectral analysis is a powerful tool to measure the cyclostationarity of a signal in different frequency ranges. For this tool to be effective in applications related to complex machinery, two requirements are identified. One requirement is that the tool must be capable of detecting defects from a weak signal as it passes and attenuates through its transmission path.
This paper presents a method for faultdiagnosis based on Mahalanobis-Taguchi system (MTS), which is applied to practical faultdiagnosis for rolling element bearing. Firstly, this method utilizes time\\/frequency domain analysis for feature extraction from the vibration data. Then, a computational scheme based on Mahalanobis distance (MD) is used for fault clustering. In addition, Taguchi methods are employed to reduce
Rotor winding interturn short circuit fault is one of the common faults in generator, and analyzing fault mechanism and diagnosis method is very necessary. This paper analyzes generator rotor and stator vibration characteristic caused by the fault. Firstly calculates air-gap magnetomotive force distribution, air-gap magnetic field energy and magnetic flux density on the fault. Then analyzes the frequency characteristic of
Wan Shuting; Xu Zhaofeng; Li Yonggang; Hou Zili; Li Heming
The use of Field Effect Transistor (FET) devices in logic design has changed the design emphasis from networks composed of single logic gates to networks composed of complex functional modules. Faultdiagnosis techniques which have been discussed in the l...
In this paper we study the problem of faultdiagnosis in the context of dense-time automata. Our work is inspired from (SSL+95, SSL+96), who have studied the problem in the context of discrete event systems (DES) (RW87). We stick to the terminology used in the above papers, although we find the term fault detection, rather than diagnosis, more appropriate. Indeed,
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.
A new concept, the prime fault, is introduced for the study of multiple faultdiagnosis in combinational logic networks. It is shown that every multiple fault in a network can be represented by a functionally equivalent fault with prime faults as its only...
In this paper the authors are dealing with the detection of different mechanical faults (unbalance and misalignment) under a wide range of working conditions of speed and load. The conditions tested in a test bench are similar to the ones that can be found in different kinds of machines like for example wind turbines. The authors demonstrate how to take advantage of the information on vibrations from the mechanical system under study in a wide range of load and speed conditions. Using such information the prognosis and detection of faults is faster and more reliable than the one obtained from an analysis over a restricted range of working conditions (e.g. nominal).
Villa, Luisa F.; Reñones, Aníbal; Perán, Jose R.; de Miguel, Luis J.
Rolling element bearings are widely used in industrial applications. This paper presents a faultdiagnosis method for rolling element bearings based on support vector machine (SVM). Firstly, the features are extracted from the vibration signals by the five-level wavelet packet decomposition algorithm using db2 wavelet. Then, the principal component analysis (PCA) is performed for feature reduction. Secondly, the multiclass SVM
This paper proposes a faultdiagnosis 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
The Knitting, Lace and Net Industry Training Board has developed a training innovation called faultdiagnosis training. The entire training process concentrates on teaching based on the experiences of troubleshooters or any other employees whose main tasks involve faultdiagnosis and rectification. (Author/DS)
This paper describes a microprocessor-based system for the automatic faultdiagnosis of a switching regulator. It covers the system from a test philosophy to a working breadboard that correctly identifies single simulated faults in the switching regulator. In addition to open circuit, short circuit, and stuck at faults, the system is capable of diagnosing faults due to excessive leakage, drift in critical components, and system instability.
This work aims at presenting the detection and diagnosis of electrical faults in the stator winding of three-phase induction motors using magnetic flux and vibration analysis techniques. A relationship was established between the main electrical faults (inter-turn short circuits and unbalanced voltage supplies) and the signals of magnetic flux and vibration, in order to identify the characteristic frequencies of those faults. The experimental results showed the efficiency of the conjugation of these techniques for detection, diagnosis and monitoring tasks. The results were undoubtedly impressive and can be adapted and used in real predictive maintenance programs in industries.
We consider the problem of constructing optimal and near-optimal test sequences for multiple faultdiagnosis. The computational complexity of solving the optimal multiple-fault isolation problem is super exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and AND\\/OR graph search and by exploiting the single
Mojdeh Shakeri; Vijaya Raghavan; Krishna R. Pattipati; Ann Patterson-hine
We propose a theory of how individuals diagnose faults, and we report two experiments that tested its application to the diagnosis of faults in simple Boolean systems. Participants were presented with simple network diagrams in which a signal was transmitted from a set of input nodes to an output node, via a set of connecting nodes. Their task was to
In order to maintain complex technical systems e.g. a telecommunication network, a rapid and precise recognition of faults and critical situations is required. But the large number of different components, the high degree of interdependencies among the components and the permanent changes in these systems make the diagnosis of faults and critical situations difficult.
his paper deals with residual generation for the diagnosis of faults in the presence of disturbances. The emphasis is on modelling errors, represented as multiplicative disturbances, and on parametric faults. These are both characterized as discrepancies in a set of underlying parameters. The residuals are obtained using parity equations. To address the situation when the number of uncertain parameters is
Experimental results obtained with the use of measurement reduction for statistical IC faultdiagnosis are described. The reduction method used involves data pre-processing in a fashion consistent with a specific definition of parametric faults. The effects of this preprocessing are examined.
Existing techniques and methodologies for faultdiagnosis are surveyed. The techniques run the gamut from theoretical artificial intelligence work to conventional software engineering applications. They are shown to define a spectrum of implementation alternatives where tradeoffs determine their position on the spectrum. Various tradeoffs include execution time limitations and memory requirements of the algorithms as well as their effectiveness in addressing the faultdiagnosis problem.
Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.
Current expert system technology does not permit complete automatic faultdiagnosis; significant levels of human intervention are still required. This requirement dictates a need for a division of labor that recognizes the strengths and weaknesses of both human and machine diagnostic skills. Relevant findings from the literature on human cognition are combined with the results of reviews of aircrew performance with highly automated systems to suggest how the interface of a fault diagnostic expert system can be designed to assist human operators in verifying machine diagnoses and guiding interactive faultdiagnosis. It is argued that the needs of the human operator should play an important role in the design of the knowledge base.
Over the past two decades, condition monitoring and faultsdiagnosis in rotating machinery have been widely studied and reported. In the present paper an algorithm for faultdiagnosis in industrial rotating machines facing new operating conditions emergence is developed on the basis of input indicators, extracted from vibrations spectrums. Indicators selection is used to improve diagnosis performances by the help of a hybrid approach using several selection criteria and different classifiers. To validate the performances of this algorithm, experimental tests were conducted on two industrial systems with various operating conditions. The results have proved the effectiveness of the developed algorithm compared to the "J48 decision tree" and also reveal the need to re-select the indicators for reliable monitoring of working conditions.
Khelf, Ilyes; Laouar, Lakhdar; Bouchelaghem, Abdelaziz M.; Rémond, Didier; Saad, Salah
This paper presents a novel method for faultdiagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs). The method consists of three stages. First, prior to feature extraction, some preprocessing techniques, like filtration, demodulation and EMD are performed on vibration signals to acquire more fault characteristic information.
In this paper, an improved Mahalanobis-Taguchi system based faultdiagnosis scheme is presented, vibration signals are used as the signal resource. Mahalanobis-Taguchi System is utilized for fault clustering method in order to classify faults into different categories, Lipschitz Exponents are used to extract characteristic vectors. Firstly, the procedure of implementing Mahalanobis-Taguchi System is introduced, a multi-class faults classification method is
The problem of faultdiagnosis in multiprocessor systems is considered under a uniformly probabilistic model in which processors are faulty with probability p. 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. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. The number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis technique.
Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.
In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.
Knill, E. (Los Alamos National Lab., NM (United States)); Cox, P.T.; Pietrzykowski, T. (Technical Univ., NS (Canada))
Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.
Choi, Kihoon; Namuru, Setu M.; Azam, Mohammad S.; Luo, Jianhui; Pattipati, Krishna R.; Patterson-Hine, Ann
In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox faultdiagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox faultdiagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. PMID:24424467
The report describes the methods of malfunction diagnosis of rotating machinery by vibration analysis. The frequency analysis of vibration signals is a very effective tool for diagnosing mechanical problems in rotating machinery and the use of this techni...
This article presents experimental results which show feedforward neural networks are well-suited for analog IC faultdiagnosis. Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers and feedforward neural networks trained with various measurement selection criteria. The feedforward networks consistently perform as
The Failure Mode and Effects Analysis (FMEA) design discipline involves the examination at design time of the consequences of potential component failures on the functionality of a system. It is clear that this type of information could also prove useful for diagnostic purposes. Unfortunately, this information cannot be fully utilised for diagnosis when FMEA has been performed by human engineers,
This paper presents new techniques for locating and diagnosing faults on electric power distribution feeders. The proposed fault location and diagnosis scheme is capable of accurately identifying the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database. The developed fault location and diagnosis
A low fidelity simulator of a marine powerplant, was developed and used to study expert marine engineers' faultdiagnosis performance. Based on the data collected, factors affecting the faultdiagnosis performance were identified. They are: the initial fe...
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
Faultdiagnosis is a major problem in industrial systems, and is of primary interest for mobile and industrial robotics where electric motors are used. In this paper faultdiagnosis with the use of the Kalman filter is compared to faultdiagnosis based on particle filter. The Kalman filter assumes linear model representation and Gaussian measurement noise whereas the particle filter
The design and analysis of faultdiagnosis architectures using the model-based analyticalredundancy approach has received considerable attention during the last two decades. One ofthe key issues in the design of such faultdiagnosis schemes is the effect of modeling uncertaintieson their performance. This paper describes a faultdiagnosis algorithm for a class of nonlineardynamic systems with modeling uncertainties when not
For making up the deficiency of faultdiagnosis method of train-ground wireless communication unit of communication based train control (CBTC), a global faultdiagnosis method based on model building is introduced. Global faultdiagnosis model is mainly comprised of fault symptom, faultdiagnosis rule and fault types. Fault symptom is seemed as input space and fault types are seemed as
Multi-fault identification is a challenge for rotating machinery faultdiagnosis. The vibration signals measured from rotating machinery usually are complex, non-stationary and nonlinear. Especially, the useful multi-fault features are too weak to be identified at the early stage. In this paper, a novel method called improved EEMD with multiwavelet packet for rotating machinery multi-faultdiagnosis is proposed. Using multiwavelet packet as the pre-filter to improve EEMD decomposition results, multiwavelet packet decomposes the vibration signal into a series of narrow frequency bands and enhances the weak multi-fault characteristic components in the different narrow frequency bands. By selecting the proper added noise amplitude according to the vibration characteristics, EEMD is further improved to increase the accuracy and effectiveness of its decomposition results. The proposed method is applied to analyze the multi-fault of a blade rotor experimental setup and an industrial machine set, and the results confirm the advantage of the proposed method over EEMD, EEMD with multiwavelet packet, Hilbert-Huang transform and multiwavelet packet transform for multi-faultdiagnosis.
A new diagnostic fault simulator is described that diagnoses both feedback and nonfeedback bridge faults in combinational circuits while using information from fault simulation of single stuck-at faults. A realistic fault model is used which considers the existence of the Byzantine Generals problem. Sets representing nodes possibly involved in a defect are partitioned based on logic and fault simulation of
Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for faultdiagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked and used as the bearing fault features. Next, MDC was used to classify the bearing fault features extracted by EMD, WT and MF-DFA in the time domain and assess the abilities of the three methods to extract fault features from bearing fault data. The results show that MF-DFA seems to outperform each of envelope analysis, statistical parameters, EMD and WT in feature extraction of bearing fault data and then the proposed method in this paper delivers satisfactory performances in distinguishing different types and severity of bearing faults. Furthermore, to further ascertain the nature causing the multifractality of bearing vibration data, the generalized Hurst exponents of the original bearing vibration data were compared with those of the shuffled and the surrogated data. Consequently, the long-range correlations for small and large fluctuations of data seem to be chiefly responsible for the multifractality of bearing vibration data.
Faultdiagnosis of rotating machinery is very important and critical to avoid serious accidents. However, the complex and non-stationary vibration signals with a large amount of noise make the fault detection to be challenging, especially at the early stage. Based on the inner product principle, fault detection using wavelet transforms is to match fault features most correlative to basis functions, and its effectiveness is determined by the construction and choice of wavelet basis function. In this paper, a new method based on adaptive multiwavelets via two-scale similarity transforms (TSTs) is proposed. Multiwavelets can offer multiple wavelet basis functions and so have the possibility of matching various fault features preferably. TSTs are simple and straightforward methods to design a series of new biorthogonal multiwavelets with some desirable properties. Using TSTs, a changeable and adaptive multiwavelet library is established so as to provide various ascendant multiple basis functions for inner product operation. By the rule of kurtosis maximization principle, optimal multiwavelets most similar to the fault features of a given signal are searched for. The applications to a rolling bearing of outer-race fault and a flue gas turbine unit of rub-impact fault show that the proposed method is an effective approach to detecting the impulse feature components hidden in vibration signals and performs well for rotating machinery faultdiagnosis.
An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach faultdiagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The faultdiagnosis results obtained through the analyses of SSME ground test data are presented and discussed.
The overall goal of this NSF research is to develop a dynamic data driven faultdiagnosis framework for wind turbine systems. An important component of the research is the development of wireless sensor nodes that will be deployed inside a wind turbine to collect vibration and acoustic signals. To enhance the research and education, an IREE program has been added
This paper aims to develop an complete system including signal processing, feature extraction, feature selection and classification approaches for faultdiagnosis of gear by using the wavelet transform, the entropy, the mutual information and the least-square support vector machine (LS-SVM). Firstly, the vibration signals are decomposed to several wavelet coefficients. The energy of every coefficient and the singularity values (SV)
Artificial neural networks have capacity to learn and store information about process faults via associative memory, and thus have an associative diagnostic ability with respect to faults that occur in a process. Knowledge of the faults to be learned by the network evolves from sets of data, namely values of steady-state process variables collected under normal operating condition and those collected under faulty conditions, together with information about the degree of the faults and their causes. The authors describe how to apply artificial neural networks to faultdiagnosis. A suitable two-stage multilayer neural network is proposed as the network to be used for diagnosis. The first stage of the network discriminates between the causes of faults when fed the noisy process measurements. Once the fault is identified, the second stage of the network estimates the degree of the fault. Thus, the diagnosis of incipient faults becomes possible.
Watanabe, K.; Matsuura, I.; Abe, M. Kubota, M. (Dept. of Instrument and Control Engineering, Hosei Univ. Tokyo 184 (JP))
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 faultdiagnosis 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.
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 faultdiagnosis 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.
Bottom up self-assembly of nano-crossbars from carbon nano-tubes and semiconductor nano-wires has shown the potential to overcome the limitations of lithographic fabrication of CMOS for further down-scaling. However, very high permanent and transient fault rates necessitates the incorporation of efficient fault tolerance techniques, capable of handling multiple faults. Self repair provides fault tolerance through fault detection, diagnosis and reconfiguration to
This paper presents new techniques for locating and diagnosing faults on electric power distribution feeders. The proposed fault location and diagnosis scheme is capable of accurately identifying the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database. The developed fault location and diagnosis system can also be applied to the investigation of temporary faults that may not result in a blown fuse. The proposed fault location algorithm is based on the steady-state analysis of the faulted distribution network. To deal with the uncertainties inherent in the system modeling and the phasor estimation, the fault location algorithm has been adapted to estimate fault regions based on probabilistic modeling and analysis. Since the distribution feeder is a radial network, multiple possibilities of fault locations could be computed with measurements available only at the substation. To identify the actual fault location, a faultdiagnosis algorithm has been developed to prune down and rank the possible fault locations by integrating the available pieces of evidence. Testing of the developed fault location and diagnosis system using field data has demonstrated its potential for practical use.
Zhu, J. [Advanced Control Systems, Inc., Norcross, GA (United States); Lubkeman, D.L.; Girgis, A.A. [Clemson Univ., SC (United States). Dept. of Electrical and Computer Engineering
Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on faultdiagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the faultdiagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The faultdiagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of faultdiagnosis and fault-tolerant control strategies. The faultdiagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the faultdiagnosis and fault-tolerant control of PMSM.
In this paper a novel method for de-noising nonstationary vibration signal and diagnosing diesel engine faults is presented. The method is based on the adaptive wavelet threshold (AWT) de-noising, ensemble empirical mode decomposition (EEMD) and correlation dimension (CD). A new adaptive wavelet packet (WP) thresholding function for vibration signal de-noising is used in this paper. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new AWT-EEMD-based method for faultdiagnosis of diesel engine. A study of correlation dimension in engine condition monitoring is reported also. Some important influencing factors relating directly to the computational precision of correlation dimension are discussed. Industrial engine normal and faultvibration signals measured from different operating conditions are analyzed using the above method.
Wang, Xia; Liu, Changwen; Bi, Fengrong; Bi, Xiaoyang; Shao, Kang
A robust feature extraction scheme for the rolling element bearing (REB) faultdiagnosis is proposed by combining the envelope extraction and the independent component analysis (ICA). In the present approach, the envelope extraction is not only utilized to obtain the impulsive component corresponding to the faults from the REB, but also to reduce the dimension of vibration sources included in the sensor-picked signals. Consequently, the difficulty for applying the ICA algorithm under the conditions that the sensor number is limited and the source number is unknown can be successfully eliminated. Then, the ICA algorithm is employed to separate the envelopes according to the independence of vibration sources. Finally, the vibration features related to the REB faults can be separated from disturbances and clearly exposed by the envelope spectrum. Simulations and experimental tests are conducted to validate the proposed method.
This paper introduces the design of an integrated framework for on-board faultdiagnosis and failure prognosis of a helicopter transmission component, and describes briefly its main modules. It suggests means to (1) validate statistically and pre-process sensor data (vibration), (2) integrate model-based diagnosis and prognosis, (3) extract useful features or condition indicators from data de-noised by blind deconvolution, and (4)
Romano Patrick; Marcos E. Orchard; Bin Zhang; Michael D. Koelemay; Gregory J. Kacprzynski; Aldo A. Ferri; George J. Vachtsevanos
An understanding of the vibration spectra is very useful for any gear fault detection scheme based upon vibration measurements. The vibration measured from planetary gears is complicated. Sternfeld noted the presence of sidebands about the gear mesh harmonics spaced at the planet passage frequency in spectra measured near the ring gear of a CH-47 helicopter. McFadden proposes a simple model of the vibration transmission that predicts high spectral amplitudes at multiples of the planet passage frequency, for planetary gears with evenly spaced planets. This model correctly predicts no strong signal at the meshing frequency when the number of teeth on the ring gear is not an integer multiple of the number of planets. This paper will describe a model for planetary gear vibration spectra developed from the ideas started in reference. This model predicts vibration to occur only at frequencies that are multiples of the planet repetition passage frequency and clustered around gear mesh harmonics. Vibration measurements will be shown from tri-axial accelerometers mounted on three different planetary gear systems and compared with the model. The model correctly predicts the frequencies with large components around the first several gear mesh harmonics in measurements for systems with uniformly and nonuniformly spaced planet gears. Measurements do not confirm some of the more detailed features predicted by the model. Discrepancies of the ideal model to the measurements are believed due to simplifications in the model and will be discussed. Fault detection will be discussed applying the understanding will be discussed.
A comprehensive procedure in predicting faults in gear transmission systems under normal operating conditions is presented. Experimental data were obtained from a spiral bevel gear fatigue test rig at NASA/Lewis. Time-synchronous-averaged vibration data were recorded throughout the test as the fault progressed from a small single pit to severe pitting over several teeth, and finally tooth fracture. A numerical procedure based on the Wigner-Ville distribution was used to examine the time-averaged vibration data. Results from the Wigner-Ville procedure are compared to results from a variety of signal analysis techniques that include time-domain analysis methods and frequency analysis methods. Using photographs of the gear tooth at various stages of damage, the limitations and accuracy of the various techniques are compared and discussed. Conclusions are drawn from the comparison of the different approaches as well as the applicability of the Wigner-Ville method in predicting gear faults.
Choy, F. K.; Huang, S.; Zakrajsek, J. J.; Handschuh, R. F.; Townsend, D. P.
This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in faultdiagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.
The successful implementation of faultdiagnosis and failure prognosis algorithms to safety critical systems requires the definitions and applications of mathematically rigorous modules. These modules, including data preprocessing, feature extraction, diagnostic and prognostic algorithms, performance metrics definition, and a fault progression model, form an integrated architecture for system health monitoring and management. In these modules, the fault progression model is
Bin Zhang; Chris Sconyers; Marcos Orchard; Romano Patrick; George Vachtsevanos
Physical defects cause behaviors unmodeled by even the best fault simulators, which complicates predictive diagnosis. This paper reports on a diagnosis procedure that uses modified composite signatures constructed from single stuck-at information combined with a lexicographic matching and ranking algorithm. The diagnosis procedure is used to perform high-quality bridging faultdiagnosis for more than 400,000 diagnostic experiments involving dropping or
The machinery faultdiagnosis is important for improving reliability and performance of systems. Many methods such as Time Synchronous Average (TSA), Fast Fourier Transform (FFT)-based spectrum analysis and short-time Fourier transform (STFT) have been applied in faultdiagnosis and condition monitoring of mechanical system. The above methods analyze the signal in frequency domain with low resolution, which is not suitable for non-stationary vibration signal. The Kolmogorov-Smirnov (KS) test is a simple and precise technique in vibration signal analysis for machinery faultdiagnosis. It has limited use and advantage to analyze the vibration signal with higher noise directly. In this paper, a new method for the fault degradation assessment of the water hydraulic motor is proposed based on Wavelet Packet Analysis (WPA) and KS test to analyze the impulsive energy of the vibration signal, which is used to detect the piston condition of water hydraulic motor. WPA is used to analyze the impulsive vibration signal from the casing of the water hydraulic motor to obtain the impulsive energy. The impulsive energy of the vibration signal can be obtained by the multi-decomposition based on Wavelet Packet Transform (WPT) and used as feature values to assess the fault degradation of the pistons. The kurtosis of the impulsive energy in the reconstructed signal from the Wavelet Packet coefficients is used to extract the feature values of the impulse energy by calculating the coefficients of the WPT multi-decomposition. The KS test is used to compare the kurtosis of the impulse energy of the vibration signal statistically under the different piston conditions. The results show the applicability and effectiveness of the proposed method to assess the fault degradation of the pistons in the water hydraulic motor.
A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Faultdiagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.
In general, we do not know which fault model can explain the cause of the faulty values at the primary outputs in a circuit under test before starting diagnosis. Moreover, under Built-In Self Test (BIST) environment, it is difficult to know which primary output has a faulty value on the application of a failing test pattern. In this paper, we propose an effective diagnosis method on multiple fault models, based on only pass/fail information on the applied test patterns. The proposed method deduces both the fault model and the fault location based on the number of detections for the single stuck-at fault at each line, by performing single stuck-at fault simulation with both passing and failing test patterns. To improve the ability of faultdiagnosis, our method uses the logic values of lines and the condition whether the stuck-at faults at the lines are detected or not by passing and failing test patterns. Experimental results show that our method can accurately identify the fault models (stuck-at fault model, AND/OR bridging fault model, dominance bridging fault model, or open fault model) for 90% faulty circuits and that the faulty sites are located within two candidate faults.
Takamatsu, Yuzo; Takahashi, Hiroshi; Higami, Yoshinobu; Aikyo, Takashi; Yamazaki, Koji
This paper addresses the design process of diagnosis and fault-tolerant control when a system should operate despite multiple failures in sensors or actuators. Graph-theory based analysis of systems structure is demonstrated to be a unique design methodology that can cope with the diagnosis design for systems of high complexity, and also analyze the cases of cascaded or multiple faults. The
Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent faultdiagnosis and maintenance schedules are essentially to both civil and military engines.
Faultdiagnosis is a vital aspect in the design of operational control systems for large-scale systems with stringent requirements on safety and reliability. In this paper, we develop graph representations for the failure propagation in large-scale systems. Using this model, we present efficient algorithms for failure source identification for single and multiple faults, for diagnosis of faulty alarms, and for
In this paper, the Wigner-Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time-frequency images after the images were normalized. By this way, the faultdiagnosis of valve train was transferred to the classification of time-frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time-frequency distributions before classification, the faultdiagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods.
A faultdiagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.
In this paper, a faultdiagnosis scheme for a class of time-varying faults using output probability density estimation is presented. The system studied is a nonlinear system with time delays. The measured output is viewed as a stochastic process and its probability density function (PDF) is modeled, which leads to a deterministic dynamical model including nonlinearities, uncertainties. The fault considered
We present a simulation based software environment conceived to allow an easy assessment of faultdiagnosis based fault tolerant control techniques. The new tool is primary intended for the development of advanced flight control applications with fault accommodation abilities, where the requirements for increased autonomy and safety play a premier role.
A Hilbert-Huang transform (HHT) is a time-frequency technique and has been widely applied to analyzing vibration signals in the field of faultdiagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in faultdiagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD.
Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for faultdiagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. PMID:22035775
The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.
Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and faultdiagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and faultdiagnosis systems, and their applications to new real-time hardware faultdiagnosis and monitoring systems for aircraft.
The sparse decomposition based on matching pursuit is an adaptive sparse expression of the signals. An adaptive matching pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and faultdiagnosis. First, a new dictionary model is established according to the characteristics and mechanism of rolling bearing faults. The new model incorporates the rotational speed of the bearing, the dimensions of the bearing and the bearing fault status, among other parameters. The model can simulate the impulse experienced by the bearing at different bearing fault levels. A simulation experiment suggests that a new impulse dictionary used in a matching pursuit algorithm combined with a genetic algorithm has a more accurate effect on bearing faultdiagnosis than using a traditional impulse dictionary. However, those two methods have some weak points, namely, poor stability, rapidity and controllability. Each key parameter in the dictionary model and its influence on the analysis results are systematically studied, and the impulse location is determined as the primary model parameter. The adaptive impulse dictionary is established by changing characteristic parameters progressively. The dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The matching pursuit algorithm of an adaptive impulse dictionary is adopted to analyze the simulated signals. The results indicate that the characteristic fault components could be accurately extracted from the noisy simulation fault signals by this algorithm, and the result exhibited a higher efficiency in addition to an improved stability, rapidity and controllability when compared with a matching pursuit approach that was based on a genetic algorithm. We experimentally analyze the early-stage fault signals and composite fault signals of the bearing. The results further demonstrate the effectiveness and superiority of the matching pursuit algorithm that uses the adaptive impulse dictionary. Finally, this algorithm is applied to the analysis of engineering data, and good results are achieved.
The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.
\\u000a In view of non-stationary and non-linearity of vibration signal from machine surface, a new method, which is called Local-Wave\\u000a Method (LWM), is presented to decompose it into number of Intrinsic Mode Weighs (IMW). Then improved RBF network model is\\u000a constructed and trained using IMW as inputs. Taking the diesel faultdiagnosis as an example, the method, which is checked\\u000a through
Current research toward real time faultdiagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.
Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet
This paper presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach.
Zhang, Y.; Ding, X.; Liu, Y. [Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States). Bradley Dept. of Electrical Engineering] [Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States). Bradley Dept. of Electrical Engineering; Griffin, P.J. [Doble Engineering Co., Watertown, MA (United States)] [Doble Engineering Co., Watertown, MA (United States)
This paper introduces the overall design of vehicle bus data acquisition and faultdiagnosis system on the basis of OBD, focusing on its lower computer system and the upper computer system design principles. This system is based on the widely used CAN bus technology, to extract the vehicle's status or fault information. The CAN bus adopts the SAE J1939 protocol
An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine faultdiagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.
Based on neural network theory, a new faultdiagnosis method for power electronic circuits is presented. By keeping the relations between faults and waveforms in a neural network, the neural network can be trained to detect faults. So automation of faultdiagnosis can be realized. In this paper, the faultdiagnosis of a three-phase SCR rectifier circuit will be taken
Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and faultdiagnosis applications. The potential applica...
Many studies have presented different approaches for the faultdiagnosis with fault models having ± 50% variation in the component values in analog electronic circuits. There is still a need of the approaches which provide the faultdiagnosis with the variation in the component value below ± 50%. A new single and multiple faultdiagnosis technique for soft faults in analog electronic circuit using fuzzy classifier has been proposed in this paper. This technique uses the simulation before test (SBT) approach by analyzing the frequency response of the analog circuit under faulty and fault free conditions. Three signature parameters peak gain, frequency and phase associated with peak gain, of the frequency response of the analog circuit are observed and extracted such that they give unique values for faulty and fault free configuration of the circuit. The single and double fault models with the component variations from ± 10% to ± 50% are considered. The fuzzy classifier along the classification of faults gives the estimated component value under faulty and faultfree conditions. The proposed method is validated using simulated data and the real time data for a benchmark analog circuit. The comparative analysis is also presented for both the validations. PMID:23849881
This paper presents a novel method for faultdiagnosis based on an improved wavelet package transform (IWPT), a distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, with investigating the feature of impact fault in vibration signals, a biorthogonal wavelet with impact property is constructed via lifting scheme, and the IWPT is carried out to extract salient frequency-band features from raw vibration signals. Then, the faulty features can be detected by envelope spectrum analysis of wavelet package coefficients of the most salient frequency band. Secondly, with the distance evaluation technique, the optimal features are selected from the statistical characteristics of raw signals and wavelet package coefficients, and the energy characteristics of decomposition frequency band. Finally, the optimal features are input into the SVMs ensemble with AdaBoost algorithm to identify the different abnormal cases. The proposed method is applied to the faultdiagnosis of rolling element bearings, and testing results show that the SVMs ensemble can reliably separate different fault conditions and identify the severity of incipient faults, which has a better classification performance compared to the single SVMs.
Time-frequency representations (TFR) have been intensively employed for analyzing vibration signals in the field of mechanical faultsdiagnosis. However, in many applications, TFR are simply utilized as a visual aid. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing an improved morphological pattern spectrum (IMPS) for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time-frequency analysis of vibration signals. Then, we present an improved morphological pattern spectrum (IMPS) scheme, which utilizes the first moment replace of the volume measure used in traditional morphological pattern spectrum (MPS) method. The promise of IMPS is illustrated by performing our procedure on vibration signals measured from an engine with five operating states. Experimental results have demonstrated the presented IMPS to be an effective approach for characterizing the TFR of vibration signals in engine faultdiagnosis.
Artificial neural networks have been used to develop software applied to fault identification and classification in transmission lines with satisfactory results. The input data to the neural network are the sampled values of voltage and current waveforms. The values proceed from the digital fault recorders, which monitor the transmission lines and make the data available in their analog channels. It
N. S. D. Brito; W. L. A. Neves; B. A. Souza; K. M. C. Dantas; A. V. Fontes; A. B. Fernandes; S. S. B. Silva
This paper explores the improved time-scale representation by considering the non-linear property for effectively identifying rotating machine faults in the time-scale domain. A new time-scale signature, called time-scale manifold (TSM), is proposed in this study through combining phase space reconstruction (PSR), continuous wavelet transform (CWT), and manifold learning. For the TSM generation, an optimal scale band is selected to eliminate the influence of unconcerned scale components, and the noise in the selected band is suppressed by manifold learning to highlight the inherent non-linear structure of faulty impacts. The TSM reserves the non-stationary information and reveals the non-linear structure of the fault pattern, with the merits of noise suppression and resolution improvement. The TSM ridge is further extracted by seeking the ridge with energy concentration lying on the TSM signature. It inherits the advantages of both the TSM and ridge analysis, and hence is beneficial to demodulation of the fault information. Through analyzing the instantaneous amplitude (IA) of the TSM ridge, in which the noise is nearly not contained, the fault characteristic frequency can be exactly identified. The whole process of the proposed faultdiagnosis scheme is automatic, and its effectiveness has been verified by means of typical faulty vibration/acoustic signals from a gearbox and bearings. A reliable performance of the new method is validated in comparison with traditional enveloping methods for rotating machine faultdiagnosis.
The objective of this work is to develop an algorithm for faultdiagnosis in a process of animal cell cultivation, for bioinsecticide production. Generally, these processes are batch processes. It is a fact that the diagnosis for a batch process involves a division of the process evolution (time horizon) into partial processes, which are defined as pseudocontinuous blocks. Therefore, a
This report surveys the field of diagnosis and introduces some of the key algorithms and heuristics currently in use. Faultdiagnosis is an important and a rapidly growing discipline. This is important in the design of self-repairable computers because the present diagnosis resolution of its fault-tolerant computer is limited to a functional unit or processor. Better resolution is necessary before failed units can become partially reuseable. The approach that holds the greatest promise is that of resident microdiagnostics; however, that presupposes a microprogrammable architecture for the computer being self-diagnosed. The presentation is tutorial and contains examples. An extensive bibliography of some 220 entries is included.
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.
Vibration signal analysis is the main technique in machine condition monitoring or faultdiagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for gearbox based on near-field acoustic holography (NAH) and spatial distribution features of sound field is presented in this paper. It focuses on applying distribution information of sound field to gearbox faultdiagnosis. A two-stage industrial helical gearbox is experimentally studied in a semi-anechoic chamber and a lab workshop, respectively. Firstly, multi-class faults (mild pitting, moderate pitting, severe pitting and tooth breakage) are simulated, respectively. Secondly, sound fields and corresponding acoustic images in different gearbox running conditions are obtained by fast Fourier transform (FFT) based NAH. Thirdly, by introducing texture analysis to faultdiagnosis, spatial distribution features are extracted from acoustic images for capturing fault patterns underlying the sound field. Finally, the features are fed into multi-class support vector machine for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with traditional ABD method. Even with strong noise interference, spatial distribution features of sound field can reliably reveal the fault patterns of gearbox, and thus the satisfactory accuracy can be obtained. The combination of histogram features and gray level gradient co-occurrence matrix features is suggested for good diagnosis accuracy and low time cost.
Results of preliminary research on the design of a knowledge based faultdiagnosis system for use with on-orbit spacecraft such as the Hubble Space Telescope are presented. A candidate data structure and associated search algorithm from which the knowledge based system can evolve is discussed. This algorithmic approach will then be examined in view of its inability to diagnose certain common faults. From that critique, a design for the corresponding knowledge based system will be given.
By building mathematical model for HAGC (hydraulic automation gauge control) system of strip rolling mill, treating faults as unknown inputs induced by model uncertainty, and analyzing fault direction, an unknown input faultdiagnosis observer group was designed. Fault detection and isolation were realized through making observer residuals robust to specific faults but sensitive to other faults. Sufficient existence conditions and
In this paper, the synchronous signal average of gear mesh vibration signals is modelled with the multiple modulated sinusoidal representations. The signal model parameters are optimised against the measured signal averages by using the batch learning of the least squares technique. With the optimal signal model, all components of a gear mesh vibration signal, including the amplitude modulations, the phase modulations and the impulse vibration component induced by gear tooth cracking, are identified and analysed with insight of the gear tooth crack development and propagation. In particular, the energy distribution of the impulse vibration signal, extracted from the optimal signal model, provides sufficient information for monitoring and diagnosing the evolution of the tooth cracking process, leading to the prognosis of gear tooth cracking. The new methodologies for gear mesh signal modelling and the diagnosis of the gear tooth fault development and propagation are validated with a set of rig test data, which has shown excellent performance.
Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and faultdiagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation. PMID:18255587
An algorithm of faultdiagnosis is proposed for vision module on the intelligent agent. The basis to generate the expectation is the fundamental matrix, which is an important concept of epipolar geometry. The proposed method of faultdiagnosis is mainly composed of two processes. The fundamental matrix is firstly computed and its uncertainty is characterized. This is can be finished off-line. Then a procedure is performed in real time to judge whether the relative orientation of the cameras has been changed. Compared with the conventional methods, our method does not need additional sensors and can be almost automatically executed. Experiments have been carried out with several practical applications and the results show that the method of faultdiagnosis is reasonable.
The use of human memory and knowledge structures to direct faultdiagnosis performance was investigated. The performances of 20 pilots with instrument flight ratings were studied in a faultdiagnosis task. The pilots were read a scenario which described flight conditions under which the symptoms which are indicative of a problem were detected. They were asked to think out loud as they requested and interpreted various pieces of information to diagnose the cause of the problem. Only 11 of the 20 pilots successfully diagnosed the problem. Pilot performance on this faultdiagnosis task was modeled in the use of domain specific knowledge organized in a frame system. Eighteen frames, with a common structure, were necessary to account for the data from all twenty subjects.
Smith, P. J.; Giffin, W. C.; Rockwell, T. H.; Thomas, M. E.
Even though a lot of research has gone into diagnosing misfire in IC engines, most approaches use torsional vibration of the crankshaft, and only a few use the rocking motion (roll) of the engine block. Additionally, misfire diagnosis normally requires an expert to interpret the analysis results from measured vibration signals. Artificial Neural Networks (ANNs) are potential tools for the automated misfire diagnosis of IC engines, as they can learn the patterns corresponding to various faults. This paper proposes an ANN-based automated diagnostic system which combines torsional vibration and rotation of the block for more robust misfire diagnosis. A critical issue with ANN applications is the network training, and it is improbable and/or uneconomical to expect to experience a sufficient number of different faults, or generate them in seeded tests, to obtain sufficient experimental results for the network training. Therefore, new simulation models, which can simulate combustion faults in engines, were developed. The simulation models are based on the thermodynamic and mechanical principles of IC engines and therefore the proposed misfire diagnostic system can in principle be adapted for any engine. During the building process of the models, based on a particular engine, some mechanical and physical parameters, for example the inertial properties of the engine parts and parameters of engine mounts, were first measured and calculated. A series of experiments were then carried out to capture the vibration signals for both normal condition and with a range of faults. The simulation models were updated and evaluated by the experimental results. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The automated diagnostic system comprises three stages: misfire detection, misfire localization and severity identification. Multi-layer Perceptron (MLP) and Probabilistic Neural Networks were applied in the different stages. The final results have shown that the diagnostic system can efficiently diagnose different misfire conditions, including location and severity.
Chen, J.; Randall, R. B.; Peeters, B.; Van der Auweraer, H.; Desmet, W.
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 faultdiagnosis 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.
For an induction machine, we suggest a theoretical development of the mechanical unbalance effect on the analytical expressions of radial vibration and stator current. Related spectra are described and characteristic defect frequencies are determined. Moreover, the stray flux expressions are developed for both axial and radial sensor coil positions and a substitute diagnosis technique is proposed. In addition, the load torque effect on the detection efficiency of these diagnosis media is discussed and a comparative investigation is performed. The decisive factor of comparison is the fault sensitivity. Experimental results show that spectral analysis of the axial stray flux can be an alternative solution to cover effectiveness limitation of the traditional stator current technique and to substitute the classical vibration practice. PMID:23938005
This paper presents a novel integrated hybrid approach for faultdiagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. PMID:24239987
As for non-destructive condition monitoring method, vibration signals are measured and analyzed to diagnose mechanical faults. A method of mechanical fault levels identification is proposed in this paper using vibration signals. Firstly, Hilbert-Huang transform is used to analyze the signal. Fault information is obtained. And energy ratio of intrinsic mode functions is taken as fault characteristics. Secondly, different levels of
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
The versatile capabilities and applications of several vibrationdiagnosis (VD) systems described, including the VD8815 for diagnosing rotational machinery, VD8855 for vibration data collection, and SP MANAGER for machine life forecasts. These systems enable early machine failure detection, the analysis of the failure cause, and the reduction of maintenance cost without help of experts.
Because of both increasing complexity and increasing geographic distribution, faults in measurement systems are becoming more troublesome to diagnose in all life-cycle phases: manufacturing, deployment, and operation. This paper considers which features are necessary in an automatic diagnosis system for large-scale measurement systems. The paper describes MonteJade, a diagnosis system designed with these essential features in mind. Examples are given
This paper describes a method based on Bayesian belief networks (BBNs) sensor fault detection, isolation, classification, and accommodation (SFDIA). For this purpose, a BBN uses three basic types of nodes to represent the information associated with each sensor: (1) sensor-reading nodes that represent the mechanisms by which the information is communicated to the BBN, (2) sensor-status nodes that convey the status of the corresponding sensors at any given time, and (3) process-variable nodes that are a conceptual representation of the actual values of the process variables, which are unknown.
Aradhye, H.B.; Heger, A.S. [Univ. of New Mexico, Albuquerque, NM (United States)
In this paper, we consider the problem of constructing optimal and near-optimal multiple faultdiagnosis (MFD) in bipartite systems with unreliable (imperfect) tests. It is known that exact computation of conditional probabilities for multiple faultdiagnosis is NP-hard. The novel feature of our diagnostic algorithms is the use of Lagrangian relaxation and subgradient optimization methods to provide: (1) near optimal solutions for the MFD problem, and (2) upper bounds for an optimal branch-and-bound algorithm. The proposed method is illustrated using several examples. Computational results indicate that: (1) our algorithm has superior computational performance to the existing algorithms (approximately three orders of magnitude improvement), (2) the near optimal algorithm generates the most likely candidates with a very high accuracy, and (3) our algorithm can find the most likely candidates in systems with as many as 1000 faults.
Shakeri, Mojdeh; Raghavan, Vijaya; Pattipati, Krishna R.; Patterson-Hine, Ann
An expert system for accident analysis and faultdiagnosis for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach...
Grids have the potential to revolutionize computing by providing ubiquitous, on demand access to computational services and resources. However, grid systems are extremely large, complex and prone to failures. A survey we've conducted reveals that faultdiagnosis is still a major problem for grid users. When a failure appears at the user screen, it becomes very difficult for the user
Alexandre Duarte; Francisco Vilar Brasileiro; Walfredo Cirne; Jose Alencar Filho
In this paper, a new robust fault detection and isolation (FDI) methodology for an unmanned aerial vehicle (UAV) is proposed. The faultdiagnosis scheme is constructed based on observer-based techniques according to fault models corresponding to each component (actuator, sensor, and struc- ture). The proposed faultdiagnosis method takes advantage of the structural perturbation of the UAV model due to
Nowadays, the Built-in test (BIT) technique is adopted widely in aircraft faultdiagnosis and maintenance. However, because of the complicated structure, mass data transmission, and especially propagations of coherent faults in aircraft, it is difficult to localize faults and to guarantee the accuracy and efficiency of BIT faultdiagnosis. To reduce the high BIT false alarm rate (FAR), the coherent
An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance. PMID:10641641
This paper proposes a feature extraction method based on information theory for faultdiagnosis 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.
Impulsive sound and vibration signals in machinery are often caused by the impacting of components and are commonly associated with faults. It has long been recognized that these signals can be gainfully used for fault detection. However, it tends to be difficult to make objective measurements of impulsive signals because of the high levels of background noise. This paper presents an enhancement scheme to aid the measurement and characterization of such impulsive sounds, called a two-stage Adaptive Line Enhancer (ALE), which exploits two adaptive filter structures in series. The resulting enhancer signals are analyzed in the time-frequency domain to obtain simultaneous spectral and temporal information. In order to apply the two-stage ALE successfully, the filter parameters and adaptive algorithms should be chosen carefully. Conditions for the choice of these parameters are presented and suggestions are made for suitable adaptive algorithms. Finally, the techniques developed are applied to the diagnosis of faults within an internal combustion engine and to data from an industrial gearbox.
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 faultdiagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-faultdiagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-faultdiagnosis 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-faultdiagnosis. 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
The application of a new method for faultdiagnosis in an automotive diesel engine is presented. Two common types of fault are investigated: (i) sensor fault, caused by a bias in the inlet manifold pressure sensor and (ii) process fault, caused by small air leaks in the inlet manifold plenum chamber. Such faults may lead to increased emission levels which,
This paper describes a hybrid approach to synthesizing solutions for diagnosis and set covering problems from the area of power system operations. The approach combines expert systems written in a rule-based language (OPS5) with algorithmic programs written in C and Lisp. An environment called DPSK has been developed to allow these programs to be run in parallel in a network of computers. Speeds sufficient for real-time applications can thereby be obtained.
The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive faultdiagnosis framework that tackles the above challenges. This framework investigates faultdiagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework. PMID:24806649
Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A faultdiagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors.
In order to improve precision of faultdiagnosis which based on artificial immune system, a kind of faultdiagnosis algorithm based on immune danger theory was presented. The algorithm can make judgment according to whether existing danger signals and reduce false rate. The algorithm also can adjust databases online. The algorithm was applied to automobile axle driving faultdiagnosis. the
In operation of mechanical equipment, faultdiagnosis plays an important role. In this paper, a novel faultdiagnosis method based on pulse coupled neural network (PCNN) and probability neural network (PNN) is presented. The shape information of shaft orbit provides an important basis for faultdiagnosis. However, the feature extraction and classification of shaft orbit is difficult to realize automation.
This paper introduces multiclass support vector machines (SVM) and a back-propagation neural network (BPNN) for faultdiagnosis of a field air defense gun. These intelligent methods preclude human error in faultdiagnosis, and they make it possible to diagnose a new failure precisely and rapidly. Our experimental results show that both SVM and BPNN provide excellent faultdiagnosis accuracy when
In this paper, a CMAC (cerebellar model articulation controller) neural network diagnosis system of turbine-generator is proposed. This novel faultdiagnosis system contains an input layer, quantization layer, binary coding layer, and fired up memory addresses coding unit. Firstly, we construct the configuration of diagnosis system depending on the fault patterns. Secondly, the known fault patterns were used to train
Chin-Pao Hung; Mang-Hui Wang; Chin-Hsing Cheng; Wen-Lang Lin
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.
This paper addresses the problem of the detailed quality end-test of vacuum cleaner motors at the end of the manufacturing cycle. For the prototyping purposes a test rig has been constructed and is presented in short. The diagnostic system built hereto takes advantage of vibration, sound and commutation analysis as well as parity relation checks. The paper focuses on the sound analysis module and provides two main contributions. First, an analysis of sound sources is performed and a set of appropriate features is suggested. Second, efficient signal processing algorithms are developed in order to detect and localise bearing faults, defects in fan impeller, improper brush-commutator contacts and rubbing of rotating surfaces. A thorough laboratory study shows that the underlying diagnostic modules provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.
This paper introduces a novel cognitive faultdiagnosis system (FDS) for distributed sensor networks that takes advantage of spatial and temporal relationships among sensors. The proposed FDS relies on a suitable functional graph representation of the network and a two-layer hierarchical architecture designed to promptly detect and isolate faults. The lower processing layer exploits a novel change detection test (CDT) based on hidden Markov models (HMMs) configured to detect variations in the relationships between couples of sensors. HMMs work in the parameter space of linear time-invariant dynamic systems, approximating, over time, the relationship between two sensors; changes in the approximating model are detected by inspecting the HMM likelihood. Information provided by the CDT layer is then passed to the cognitive one, which, by exploiting the graph representation of the network, aggregates information to discriminate among faults, changes in the environment, and false positives induced by the model bias of the HMMs. PMID:24808562
Alippi, Cesare; Ntalampiras, Stavros; Roveri, Manuel
This paper presents a novel faultdiagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and faultdiagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.
Vibration signal analysis is the most widely used technique in condition monitoring or faultdiagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for rolling element bearing faultdiagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing faultdiagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.
Supervised learning has been developed to collect condition monitoring (CM) data for faultdiagnosis and prognosis. However, labeling the condition monitoring data is expensive due to the use of field knowledge while unlabeled CM data contain significant information of normal conditions or faults, which cannot be explored by supervised learning. Manifold regularization (MR) based semi-supervised learning (SSL) is first introduced to fault detection by utilizing both labeled and unlabeled CM data, and then a new single-conditions labeled mode based on MR is proposed for SSL learning. This approach, leveraged by effectively exploiting the embedded intrinsic geometric manifolds, outperforms supervised learning in both single-conditions labeled and all-conditions labeled modes within the application of two real-life fault detection datasets. The experimental results also suggest that most effective classifier in practical application could be trained by the SSL approach and fault type representation with medium load condition. The improved predictive performance implies that the manifold assumption of MR has its inherent fundamentals. Finally, the manifold fundamental of single-conditions labeled mode is analyzed with dimensionality reduction.
The structural layers and methods of multi-sensor information fusion technology are analysed, and its application in faultdiagnosis of hydraulic system is discussed. Aiming at hydraulic system, a model of hydraulic faultdiagnosis system based on multi-sensor information fusion technology is presented. Choosing and implementing the method of information fusion reasonably, the model can fuse and calculate various fault characteristic parameters in hydraulic system effectively and provide more valuable result for faultdiagnosis of hydraulic system.
Zhang, L. Q.; Yang, G. L.; Zhang, L. G.; Zhang, S. Y.
This paper proposes a four-step open-circuit faultdiagnosis and fault-tolerant scheme for isolated phase-shifted full-bridge (PSFB) dc–dc converters to improve the reliability. The fault diagnostic method utilizes the primary voltage of the transformer as the diagnostic criterion, which can be obtained easily by adding an auxiliary winding. When an open-circuit fault occurs in any switch of the PSFB converter, the
Principal component analysis (PCA) is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for faultdiagnosis, it is not best suited for faultdiagnosis. Discriminant partial least squares (DPLS) has been shown to improve faultdiagnosis for
Published research on path delay fault (PDF) testing has largely focused on the PDF classification and test-vector generation problems. Little attention has been paid to the diagnosis of delay faults and in defining realistic metrics for delay-fault coverage, We present a statistical diagnosis framework to detect which parts of the circuit are likely to have caused a chip failure for
This papers aims to design a new approach in order to increase the performance of the decision making in model-based faultdiagnosis when signature vectors of various faults are identical or closed. The proposed approach consists on taking into account the knowledge issued from the reliability analysis and the model-based faultdiagnosis. The decision making, formalised as a bayesian network,
Philippe WEBER; Didier THEILLIOL; Christophe AUBRUN; Alexandre EVSUKOFF
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
In this paper, an effective fault location algorithm and intelligent faultdiagnosis scheme are proposed. The proposed scheme first identifies fault locations using an iterative estimation of load and fault current at each line section. Then an actual location is identified, applying the current pattern matching rules. If necessary, comparison of the interrupted load with the actual load follows and
Sensor faultdiagnosis of maglev train is studied based on Kalman filtering theory. Usually, a single Kalman filter of a control system can only detect faults, but can not locate fault parts. Therefore, Kalman filter group is introduced in. Further more, a single sensor fault will cause the system matrix of a close loop feedback control system to change which
A novel intelligent faultdiagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. PMID:23793021
Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi
A novel intelligent faultdiagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.
Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi
Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.
The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in faultdiagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for faultdiagnosis of planetary gearboxes. In order to satisfy the mono-component requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.
Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system. PMID:23464251
We conducted friction experiments on rock samples at high slip rates where frictional melting occurs. We measured vibrations caused by high-speed slipping of faults (i.e. high frequency wave radiation from the fault) in addition to the mechanical properties of rock friction. The purpose is to understand the friction process that occurs inside fault zones from the faultvibration data. The experiments were preformed at a normal stress of 3 MPa and at a slip rate of 1.5 m/s using a high-speed rotary-shear apparatus. For each run, we prepared a pair of solid cylindrical specimens of 25 mm in diameter made from India gabbro, which are the same as used by Hirose and Shimamoto . For the measurements of faultvibration, we used a triaxial, amplifier built-in accelerometer (SA11ZSC-TI, Fuji Ceramics Co., Ltd; sensitivity, 1 mV/m/s2; dynamic range, ± 4000 m/s2) attached to the sample holder of the apparatus. The amplitude response of the accelerometer is mostly flat within 1dB from 100 Hz to 5 kHz. Vibrations due to fault slip were recorded with the normal and shear stress data on the fault plane and axial shortening data of the specimens due to wear, using the digital recorder (LX-120, TEAC Co., Ltd) with 24-bit amplitude resolution and a 20 kHz sampling rate. We calculated the Fourier amplitude spectra of the acceleration data at an interval of 0.1s with a time window length of 0.1s. We examined the evolution of the Fourier amplitude of the vibration and its gradient in frequency by comparing the mechanical data of rock friction. Based on our experimental results, we recognized four stages in view of the friction process inside the fault zone during frictional melting, its associated mechanical behavior, and the frequency / amplitude characteristics of the faultvibration as shown in the following. Stage 1: Formation of gouge particles and melt patches and subsequent growth of the patches to a continuous molten layer on the fault. In this stage, friction coefficient increases from an initial value of 0.5 to 0.7 at the second peak. The Fourier amplitude of the vibration decreases as a function of frequency with a slope of -2 in log-log scale. Stage 2: Thickening of the molten layer. The friction coefficient decreases rapidly from the second peak toward the steady-state value. The Fourier amplitude of vibration decreases by an order of magnitude lower than in Stage 1 in all frequency range. The slope is still -2 in log-log scale. Stage 3: Axial shortening of the specimens starts to occur due to the escape of molten materials from the fault. The friction coefficient gradually increases compared to Stage 2. The vibration amplitude decreases in low frequency range (lower than 500 Hz) and the slope becomes -1 in log-log scale. Stage 4: A balance is achieved between the production rate and escape rate of molten materials. The friction coefficient becomes constant and achieves steady state. The frequency/amplitude characteristics of the vibration are the same as Stage 3. From the analyses of the vibration data generated during frictional melting, important insights are obtained on physical process inside the fault zone in addition to its associated mechanical behavior.
The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the faultdiagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.
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.
Objective For a reliable objective diagnosis of vascular injuries in hand-arm vibration syndrome (HAVS), the standardized cold provocation\\u000a tests—finger skin temperature measurement during hand(s) immersion in cold water (FST test) and finger systolic blood pressure\\u000a measurement during local cold exposure (FSBP test)—are widely used. In recent years there is a growing controversy regarding\\u000a the diagnostic value of these tests. The aim
Abstract: Precise failure analysis requires accurate faultdiagnosis. A previously proposed method for diagnosing bridging faults using single stuck-at dictionaries was applied only to small circuits, produced large and imprecise diagnoses, and did not take into account the Byzantine Generals Problem for bridging faults. We analyze the original technique and improve it by introducing the concepts of match restriction, match
Brian Chess; David B. Lavo; F. Joel Ferguson; Tracy Larrabee
An expert system for internal combustion engine faultdiagnosis using Wigner–Ville distribution for feature extraction and probability neural network for fault classification is described in this paper. Most of the conventional techniques for fault signal analysis in a mechanical system are based chiefly on the difference of signal amplitude in the time and frequency domains. Unfortunately, in some conditions the
The ability to detect and isolate process fault for product quality control in assembly processes plays an essential role in the success of a manufacturing enterprise in today’s globally competitive marketplace. However, the complexity of assembly processes makes it fairly challenging to diagnose process faults. One novel fixture faultdiagnosis methodology has been developed in this study. The relationship between
Since most of the induction motors are operated by the inverter, an unexpected fault of the inverter can cause serious troubles such as downtime of equipment, heavy loss, and etc. Therefore, the studies on the robust drive system for induction motors to protect the system under the fault modes are gaining more interests. This paper investigates the faultdiagnosis for
Jang-Hwan Park; Dong-Hwa Kim; Sung-Suk Kim; Dae-Jong Lee; Myung-Geun Chun
Vibration analysis is one of the most used tech- niques for predictive maintenance in high-speed rotating ma- chinery. Using the information contained in the vibration signals, a system for alarm detection and diagnosis of failures in mechanical components of power wind mills is devised. As previous failure data collection is unfeasible in real life scenarios, the method to be employed
David Martinez-Rego; Oscar Fontenla-Romero; Amparo Alonso-Betanzos
The quality consistence and machine utilization of a flexible manufacturing system (FMS) strongly depend on the statistical process control (SPC) and the correct faultdiagnosis of equipment. An FMS can be modelled by the colored timed Petri net (CTPN). However, most CTPN models of FMS lack the activities of SPC and faultdiagnosis, and they lead to incomplete FMS CTPN
Condition monitoring leading to faultdiagnosis and prediction of electrical machines and drives has recently become of importance. The topic has attracted researchers to work in during the past few years because of its great influence on the operational continuation of many industrial processes. Correct diagnosis and early detection of incipient faults result in fast unscheduled maintenance and short down
Processes combining heat and material flows involve non-linear relationships that complicate fault detection and diagnosis (FDD) procedures. This paper proposes two model-based methods for detecting and diagnosing faults in process models as well as in measurements. The models involved in both methods consist of stationary mass and energy conservation equations. The ?2 detection test and the generalized likelihood ratio diagnosis
This paper is based the theory of Petri net, and made a major study of the Petri net in fire control system faultdiagnosis. In regard to the characteristies of failure for fire control system, made the Petri net model is extended to the field of faultdiagnosis model. It is analyzed transmission characteisties and administ hierachical structure of the
An expert system for accident analysis and faultdiagnosis for the Loss Of Fluid Test (LOFT) reactor, a small scale pressurized water reactor, was developed for a personal computer. The knowledge of the system is presented using a production rule approach with a backward chaining inference engine. The data base of the system includes simulated dependent state variables of the LOFT reactor model. Another system is designed to assist the operator in choosing the appropriate cooling mode and to diagnose the fault in the selected cooling system. The response tree, which is used to provide the link between a list of very specific accident sequences and a set of generic emergency procedures which help the operator in monitoring system status, and to differentiate between different accident sequences and select the correct procedures, is used to build the system knowledge base. Both systems are written in TURBO PROLOG language and can be run on an IBM PC compatible with 640k RAM, 40 Mbyte hard disk and color graphics.
Experimental results obtained when a previously described faultdiagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.
This paper describes faultdiagnosis of an air-conditioning system for improving reliability and guaranteeing the thermal\\u000a comfort and energy saving. To achieve this goal, we proposed a technique which is model based faultdiagnosis technique. Here,\\u000a a lumped parameter model of an air-conditioning system is considered and then characteristics of twelve faults are investigated\\u000a in an air-conditioning system provided in
Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant`s training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.
Since machinery faultvibration signals are usually multicomponent modulation signals, how to decompose complex signals into a set of mono-components whose instantaneous frequency (IF) has physical sense has become a key issue. Local mean decomposition (LMD) is a new kind of time-frequency analysis approach which can decompose a signal adaptively into a set of product function (PF) components. In this paper, a modulation feature extraction method-based LMD is proposed. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. The computed IF and IA are displayed together in the form of time-frequency representation (TFR). Modulation features can be extracted from the spectrum analysis of the IA and IF. In order to make the IF have physical meaning, the phase-unwrapping algorithm and IF processing method of extrema are presented in detail along with a simulation FM signal example. Besides, the dependence of the LMD method on the signal-to-noise ratio (SNR) is also investigated by analyzing synthetic signals which are added with Gaussian noise. As a result, the recommended critical SNRs for PF decomposition and IF extraction are given according to the practical application. Successful faultdiagnosis on a rolling bearing and gear of locomotive bogies shows that LMD has better identification capacity for modulation signal processing and is very suitable for failure detection in rotating machinery.
Feature extraction plays an important role in the clustering analysis. In this paper an integrated Autoregressive (AR)/Autoregressive Conditional Heteroscedasticity (ARCH) model is proposed to characterize the vibration signal and the model coefficients are adopted as feature vectors to realize clustering diagnosis of rolling element bearings. The main characteristic is that the AR item and ARCH item are interrelated with each other so that it can depict the excess kurtosis and volatility clustering information in the vibration signal more accurately in comparison with two-stage AR/ARCH model. To testify the correctness, four kinds of bearing signals are adopted for parametric modeling by using the integrated and two-stage AR/ARCH model. The variance analysis of the model coefficients shows that the integrated AR/ARCH model can get more concentrated distribution. Taking these coefficients as feature vectors, K means based clustering is utilized to realize the automatic classification of bearing fault status. The results show that the proposed method can get more accurate results in comparison with two-stage model and discrete wavelet decomposition.
This study presents a novel procedure based on ensemble empirical mode decomposition (EEMD) and optimized support vector machine (SVM) for multi-faultdiagnosis of rolling element bearings. The vibration signal is adaptively decomposed into a number of intrinsic mode functions (IMFs) by EEMD. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are IMFs, are extracted. EEMD energy entropy is used to specify whether the bearing has faults or not. If the bearing has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method was tested on a system with an electric motor which has two rolling bearings with 8 normal working conditions and 48 fault working conditions. Five groups of experiments were done to evaluate the effectiveness of the proposed method. The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures.
The performance of manufacturing systems or equipment is, to a great extent, dependent upon the condition of their components.\\u000a Closely monitoring the condition of the critical components and carrying out timely system diagnosis whenever a fault symptom\\u000a is detected would help to reduce system downtime and improve overall productivity. Fault tree analysis (FTA) is a powerful\\u000a tool for reliability studies
A fault diagnostics and fault tolerant control system for controller of brushless direct current motor is designed. The neural network state observer is trained by real nonlinear control system. From the residual difference between outputs of actual system and neural network observer, the fault of control system is detected and determined. The simulation results and study on fault diagnostics are
The detection and diagnosis of SSME faults in an early stage is important in order to allow enough time for fault preventive or corrective measurements. Since most of the faults in a complex system like SSME develop rapidly, early detection and diagnosis of faults is critical for the survival of space vehicles. An expert system has been designed for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations. This paper describes an innovative machine learning approach which is employed for the automatic training of this expert system.
This paper presents a model based on neural network optimized by the ant colony optimization algorithm (ACOA) for fault section diagnosis in distribution systems of electric power systems, and the simulation results show that it can effectively improve the fault-tolerance ability of fault section diagnosis. It had better fault-tolerance ability in contrast with the BP-NN model and the DGA-NN model.
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and having high generalization ability. It is strong to solve the problem with small sample, nonlinear and high dimension. The fundamental theory of DGA (Dissolved Gas Analysis, DGA) and fault characteristic of transformer is firstly researched in this paper, and then the disadvantages of traditional method of transformer faultdiagnosis are analyzed, finally, a new faultdiagnosis method using multi-class support vector machines (M-SVMs) based on DGA theory for transformer is put forward. Then the faultdiagnosis model based on M-SVMs for transformer is established. At the same time, the faultdiagnosis system based on M-SVMs for transformer is developed. The system can realize the acquisition of the dissolving gas in the transformer oil and data timely and low cost transmission by GPRS (General Packet Radio Service, GPRS). And it can identify out the transformer running state according to the acquisition data. The test results show that the method proposed has an excellent performance on correct ratio. And it can overcome the disadvantage of the traditional three-ratio method which lacks of fault coding and no fault types in the existent coding. Combining the wireless communication technology with the monitoring technology, the designed and developed system can greatly improve the real-time and continuity for the transformer' condition monitoring and faultdiagnosis.
Unbalance, fatigue crack and rotor-stator rub are the three common and important faults in a rotor-bearing system. They are originally interconnected each other, and their vibration behaviors do often show strong nonlinear and transient characteristic, especially when more than one of them coexist in the system. This article is aimed to study the vibration response of the rotor system in presence of multiple rotor faults such as unbalance, crack, and rotor-stator rub, using local mean decomposition-based time-frequency representation. Equations of motion of the multi-faulted Jeffcott rotor, including unbalance, crack, and rub, are presented. By solving the motion equations, steady-state vibration response is obtained in presence of multiple rotor faults. As a comparison, Hilbert-Huang transformation, based on empirical mode decomposition, is also applied to analyze the multi-faults data. By the study some diagnostic recommendations are derived.
Purpose – To provide an overview of the similarity-based modeling (SBM) technology and review its application to condition monitoring of rotating equipment using features calculated from vibration sensor signals. Design\\/methodology\\/approach – Concentrates on the practical capabilities and underlying technology of SBM. Examines the effectiveness of it as an approach to detect and diagnose faults in an electric motor-driven shaft during
This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based\\u000a approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in\\u000a the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To\\u000a diagnose the
Faultdiagnosis in power distribution systems is critical to expedite the restoration of service and improve the reliability. With power grids becoming smarter, more and more data beyond utility outage database are available for fault cause identification. This paper introduces basic methodologies to integrate and analyze data from different sources. Geographic information system (GIS) provides a framework to integrate these
The classification accuracy and efficiency of multiclass SVMs are largely dependent on the SVM combination strategy in analog circuits faultdiagnosis. An optimized SVM extension strategy is presented in this paper, which uses minimum spanning tree (MST) algorithm to simplify the SVM structure and decrease the classification errors. By taking the separability measure of fault classes as edge weight of
In this paper, a 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
In this paper, an approach to faultdiagnosis in a nonlinear stochastic dynamic system is proposed using the interacting multiple particle filtering (IMPF) algorithm. The fault diagnostic approach is formulated as a hybrid multiple- model estimation scheme. The proposed diagnostic approach provides an integrated framework to estimate the system's current operational or faulty mode, as well as the unmeasured state
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to
A transformer is one of the most important units in power networks; thus, faultdiagnosis of transformers is quite significant. In this paper, the frequency-response analysis, deemed as a suitable diagnostic method for electrical and\\/or mechanical faults of a transformer, is employed to make a decision over a defective phase. To deal with wideband frequency responses of each phase, a
This paper presents the most up-to-date methods for the task of designing a system to accurately classify abnormal events, or faults, in a complex tribological mechanism, using el- emental analysis of lubrication oil as an indicator of engine con- dition. The discussion combines perspectives from numerous faultdiagnosis applications, both online and offline, to focus upon the task of offline
This paper presents a new approach for faultdiagnosis of hydraulic servo-valves with the BP neural network based on genetic algorithm. The paper uses a known set of faults as the output to the valve-behavior model. An appropriate neural network is established to be the best solution to the problem. Adoption of this approach brings about advantages of shortening training
In order to improve the efficiency and to supply more sufficient information support, an intelligent faultdiagnosis system based on desktop virtual environment is proposed. In the first place, basic concepts and principles of virtual reality and intelligent faultdiagnosis technology are presented in this paper. Then, several essential implementation issues of the system, including the system architecture, the 3D visualization of the faultdiagnosis environment and the user interface and so on, are also been discussed. Lastly, intelligent faultdiagnosis technologies are elaborated, such as the rule base and the strategy of the reasoning and control in the expert system, etc. Practical applications and experiments demonstrate that the proposed approach is effective and robust.
A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the faultdiagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI.
Zhao Jinsong [College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029 (China)], E-mail: email@example.com; Huang Jianchao [College of Information Science and Technology, Beijing Institute of Technology, Beijing 10086 (China); Sun Wei [College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029 (China)
Fault detection and diagnosis plays a pivotal role in the health-monitoring techniques for liquid- propellant rocket engines. This paper firstly gives a brief summary on the techniques of fault detection and diagnosis utilized in liquid-propellant rocket engines. Then, the applications of fault detection and diagnosis algorithms studied and developed to the Long March Main Engine System(LMME) are introduced. For fault detection, an analytical model-based detection algorithm, a time-series-analysis algorithm and a startup- transient detection algorithm based on nonlinear identification developed and evaluated through ground-test data of the LMME are given. For faultdiagnosis, neural-network approaches, nonlinear-static-models based methods, and knowledge-based intelligent approaches are presented. Keywords: Fault detection; Faultdiagnosis; Health monitoring; Neural networks; Fuzzy logic; Expert system; Long March main engines Contact author and full address: Dr. Jianjun Wu Department of Astronautical Engineering School of Aerospace and Material Engineering National University of Defense Technology Changsha, Hunan 410073 P.R.China Tel:86-731-4556611(O), 4573175(O), 2219923(H) Fax:86-731-4512301 E-mail:firstname.lastname@example.org
The problem of vibration based fault detection, identification (localization) and estimation in a scale aircraft skeleton structure is considered via a stochastic functional model based method (FMBM). The method is based on the novel class of stochastic Functionally Pooled models, which are capable of accurately representing the structure in a faulty state for the state's continuum of fault magnitudes, as
By using a modified signed directed graph (SDG) together with the distributed artificial neutral networks and a knowledge-based system, a method of incipient multi-faultdiagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient faultdiagnosis possible for large-scale systems, (2) perform the faultdiagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-faultdiagnosis of such a large-scale system in a real-time manner.
Chung, H.Y.; Bien, Z.; Park, J.H.; Seon, P.H. (Korea Advanced Inst. of Science and Technology, Taejon (Korea, Republic of). Dept. of Electrical Engineering)
When faults occur in the gear, energy distribution of gear vibration signals measured in time–frequency plane would be different from the distribution under the normal state. Therefore, it is possible to detect a fault by comparing the energy distribution of gear vibration signals with and without fault conditions. Hilbert–Huang transform can offer a complete and accurate energy–frequency–time distribution. On the
Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based faultdiagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for the initial set-up, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2, 373 and 597 kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on an new motor, significantly reducing the system development time.
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.
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.
This paper proposes a new connectionist (or neural network) expert system for on-line faultdiagnosis of a power substation. The Connectionist Expert Diagnosis System has similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate the faults with their protective relays and breakers as well as the bus voltages and feeder currents. Through an elaborately designed structure, alarm signals are processed by different connectionist models. The output of the connectionist models is then integrated to provide the final conclusion with a confidence level. The proposed approach has been practically verified by testing on a typical Taiwan Power (Taipower) secondary substation. The test results show that rapid and exactly correct diagnosis is obtained even for the fault conditions involving multiple faults or failure operation of protective relay and circuit breaker. Moreover, the system can be transplanted into various substations with little additional implementation effort.
Yang, H.T.; Chang, W.Y.; Huang, C.L. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering
Submarine cable faults are not uncommon events in the Internet today. However, their impacts on end-to-end path quality have received almost no attention. In this paper, we report path-quality measurement results for a recent SEA-ME-WE 4 cable fault in 2010. Our measurement methodology captures the path-quality degradation due to the cable fault, in terms of delay, asymmetric packet losses, and correlation between loss and delay. We further leverage traceroute data to infer the root causes of the performance degradation.
Chan, Edmond W. W.; Luo, Xiapu; Fok, Waiting W. T.; Li, Weichao; Chang, Rocky K. C.
Based on the urgent needs of some domestic manufacturers of vehicle motor, this paper studies the generating mechanism of types of faults of wiper DC motors. A new method that extracting the speed signal from the cepstrum of vibration signal is proposed. It is compared that the advantages and disadvantages of the two time-frequency analysis -- short-time Fourier analysis and
This paper describes a new comparison-based model for distributed faultdiagnosis in multicomputer systems with a weak reliable broadcast capability. The classical problems of diagnosability and diagnosis are both considered under this broadcast comparison model. A characterization of diagnosable systems is given, which leads to a polynomial-time diagnosability algorithm. A polynomial-time diagnosis algorithm for t-diagnosable systems is also given. A
Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.
Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.
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.
We consider the problem of detecting network faults. Our focus is on detection schemes that send probes both proactively and non-adaptively. Such schemes are particularly relevant to all-optical networks, due to these networks' operational characteristics and strict performance requirements. This faultdiagnosis problem motivates a new technical framework that we introduce: group testing with graph-based constraints. Using this framework, we
Nicholas J. A. Harvey; Mihai Patrascu; Yonggang Wen; Sergey Yekhanin; Vincent W. S. Chan
This paper presents a new approach to multiple faultdiagnosis for sheet metal fixtures using designated component analysis (DCA). DCA first defines a set of patterns based on product\\/process information, then finds the significance of these patterns from the mea- surement data and maps them to a particular set of faults. Existing diagnostics methods has been mainly developed for rigid-body-based
The proposed common module thermal control system for the Space Station is designed to integrate thermal distribution and thermal control functions in order to transport heat and provide environmental temperature control through the common module. When the thermal system is operating in an off-normal state, due to component faults, an intelligent controller is called upon to diagnose the fault type, identify the fault location and determine the appropriate control action required to isolate the faulty component. A methodology is introduced for faultdiagnosis based upon a combination of signal redundancy techniques and fuzzy logic. An expert system utilizes parity space representation and analytic redundancy to derive fault symptoms, the aggregate of which is assessed by a multivalued rule based system. A subscale laboratory model of the thermal control system designed is used as the testbed for the study.
Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the faultdiagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis. PMID:22346592
The back propagation (BP)-based artificial neural nets (ANN) can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and Momentum coefficient) algorithm is proposed to reduce the training time and avoid being trapped into local minima, where
Yan-jing SUN; Shen ZHANG; Chang-xin MIAO; Jing-meng LI
The power electronics inverter can be considered as the weakest link in an electric drive system, hence the focus of this research work is on the detection of fault conditions of the inverter. A machine learning framework is developed to systematically se...
Faultdiagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.
Paliwal, Deepak; Choudhur, Achintya; Govandhan, T.
A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, faultdiagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.
Schutte, Paul C.; Abbott, Kathy H.; Palmer, Michael T.; Ricks, Wendell R.
This paper describes a technique for achieving on-line faultdiagnosis in continuous systems that are modeled using Petri nets. The effect of place markings and transition markings are considered and based on the computed error between the initial marking and subsequent markings evolved in time, the faults are categorized assuming that the markings are both observable and unobservable. An algorithm has been suitably proposed for achieving detection of faults for a typical continuous three tank system along with suitable results. PMID:20466365
\\u000a The hydraulic servo-valve is the key component of the electro-hydraulic system. But it is difficult to diagnose faults in\\u000a a hydraulic servo-valve. In this paper, a Genetic Algorithm-based Artificial Neural Network model for faultdiagnosis in hydraulic\\u000a servo–valves is proposed. We use a known set of servo-valve faults as the outputs to the valve-behavior model. Adoption of\\u000a this approach brings
Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and non-stationary signals. It may decompose a complicated signal into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The EMD method has attracted considerable attention and been widely applied to faultdiagnosis of rotating machinery recently. However, it cannot reveal the signal characteristic information accurately because of the problem of mode mixing. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new EEMD-based method for faultdiagnosis of rotating machinery. First, a simulation signal is used to test the performance of the method based on EEMD. Then, the proposed method is applied to rub-impact faultdiagnosis of a power generator and early rub-impact faultdiagnosis of a heavy oil catalytic cracking machine set. Finally, by comparing its application results with those of the EMD method, the superiority of the proposed method based on EEMD is demonstrated in extracting fault characteristic information of rotating machinery.
The focus of this research is the investigation of data structures and associated search algorithms for automated faultdiagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based FaultDiagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.
This paper presents an application of artificial neural networks (ANNs) and expert system (ES) for offline faultdiagnosis in power systems using the information of the operated relays and tripped circuit breakers after they reached their final status. The hybrid system candidates the faulted section(s) even in the case of multiple faults. The developed system also has the ability to
Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for faultdiagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better faultdiagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.
Time-frequency analysis has been found to be effective in monitoring the transient or time-varying characteristics of machinery vibration signals, and therefore its use in machine condition monitoring is increasing. This paper proposes the application of time-frequency methods, which can provide more information about a signal in time and in frequency and gives a better representation of the signal than the
The aim of this paper is to propose diagnosis methods based on fractional order models and to validate their efficiency to detect faults occurring in thermal systems. Indeed, it is first shown that fractional operator allows to derive in a straightforward way fractional models for thermal phenomena. In order to apply classical diagnosis methods, such models could be approximated by integer order models, but at the expense of much higher involved parameters and reduced precision. Thus, two diagnosis methods initially developed for integer order models are here extended to handle fractional order models. The first one is the generalized dynamic parity space method and the second one is the Luenberger diagnosis observer. Proposed methods are then applied to a single-input multi-output thermal testing bench and demonstrate the methods efficiency for detecting faults affecting thermal systems.
This paper presents a new method to enhance the detection and diagnosis of rolling element-bearing faults based on discrete wavelet packet analysis (DWPA). The extraction of attenuated resonant vibrations due to impacts from localized faults in rolling el...
Partial rub and looseness are common faults in rotating machinery because of the clearance between the rotor and the stator.\\u000a These problems cause malfunctions in rotating machinery and create strange vibrations coming from impact and friction. However,\\u000a non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency\\u000a information is needed for identifying
The steam turbine generator faults not only damage the generator itself, but also cause outages and loss of profits, for this reason, many researchers work on the faultdiagnosis. But misdiagnosing may also lead to serious losses. In order to improve the diagnosis reliability and reduce the loss caused by misdiagnosis, in this paper, cost integrated multiclass SVM with reject
The early diagnosis of rotor winding inter-turn short circuit fault is one of the most difficult problems in the operation of large synchronous generator, and a diagnosis method based on excitation current harmonics is described. Through analyzing air-gap electromagnetism characteristic on the fault, it is got that armature reaction magnetic field nonsynchronously rotates with rotor when harmonics' times and the
Bearing failure is one of the most common reasons of machine breakdowns and accidents. Therefore, the faultdiagnosis of rolling element bearings is of great significance to the safe and efficient operation of machines owing to its fault indication and accident prevention capability in engineering applications. Based on the orthogonal projection theory, a novel method is proposed to extract the fault characteristic frequency for the incipient faultdiagnosis of rolling element bearings in this paper. With the capability of exposing the oscillation frequency of the signal energy, the proposed method is a generalized form of the squared envelope analysis and named as spectral auto-correlation analysis (SACA). Meanwhile, the SACA is a simplified form of the cyclostationary analysis as well and can be iteratively carried out in applications. Simulations and experiments are used to evaluate the efficiency of the proposed method. Comparing the results of SACA, the traditional envelope analysis and the squared envelope analysis, it is found that the result of SACA is more legible due to the more prominent harmonic amplitudes of the fault characteristic frequency and that the SACA with the proper iteration will further enhance the fault features.
In this paper, embedded system was used to work for the online faultdiagnosis of hybrid electric vehicles (HEV). This system took 32-bit embedded one as a hardware platform, customized a WinCE6.0 operation system and used Embedded Visual C++ (EVC) as the tool to design the embedded application. Through this online diagnosis device, the failure phenomenon, failure causes and failure
Changqing Song; Jun Li; Dawei Qu; Dongqing Zhou; Luyan Fan
A new generation instrument was developed for condition monitoring of rotating machinery. By detecting four channels of acoustic emission (AE) signal and four channels of vibration signal, the instrument can monitor the vibration and rubbing conditions of rotating machinery on-line and locate the rubbing fault approximately. It contributes to a correct diagnosis for fault reasons by analyzing the vibration and AE signals.
Shao, Yongbo; Zhang, Aiping; Ye, Rongxue; Cui, Naidong
This paper presents a new general framework for multisensor fusion based on a distributed detection. Parallel processing and distributed multisensor fusion, as rapidly emerging and promising technologies, provides powerful tools for solving this difficult problem, The distribution and parallelism of proposing and confirming of hypothesis in condition and diagnostic is prosed. A combination serial and parallel reconfiguration of n sensors for decision fusion is analyzed. It shows the result for a real-time parallel distributed complex machine condition monitor and fault diagnostic system.
Diagnosis of incipient faults for electronic systems, especially for analog circuits, is very important, yet very difficult.\\u000a The methods reported in the literature are only effective on hard faults, i.e., short-circuit or open-circuit of the components.\\u000a For a soft fault, the fault can only be diagnosed under the occurrence of large variation of component parameters. In this\\u000a paper, a novel
In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks. PMID:24807956
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, faultdiagnosis, 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 faultdiagnosis.
Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette
In this paper, a reduced-order observer is applied to faultdiagnosis for reaction flywheels in satellite attitude control system. The benefit of this approach is able to estimate the fault signal of reaction flywheel in the presence of unknown inputs. Through ingenious state transformation, the original system is decomposed into two subsystems, one of which is driven by known inputs only. Thus, after a reduced-order observer being designed, the subsystem's accurate state estimation can be obtained without the effect of unknown inputs, consequently, the unknown inputs can be estimated. In this case the fault signals of reaction flywheels may come down to a part of unknown inputs, then the faults can be detected according to the estimated unknown inputs.
A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for faultdiagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM.
Yan-wei CHANG; Yao-cai WANG; Tao LIU; Zhi-jie WANG
Research is reported in the program to refine the current notion of system reliability by identifying and investigating attributes of a system which are important to reliability considerations, and to develop techniques which facilitate analysis of system reliability. Reliability analysis, and on-line faultdiagnosis are discussed.
This paper describes a fuzzy-based method of fault detection and diagnosis in a PWM inverter feeding an induction motor. The proposed fuzzy approach is a sensor-based technique using the mains current measurement to detect intermittent loss of firing pulses in the inverter switches. A localization domain made with seven patterns is built with the stator Concordia current vector. One is
F. Zidani; D. Diallo; M. E. H. Benbouzid; R. Nait-Said
With the development of information and computational technology, the safety simulation technique is becoming more and more useful in the chemical process hazard assessment, hazard identification, and safety control system design and operating personnel training etc.The faultdiagnosis of the gravity water tank is studied by using dynamic simulation of HYSYS (Hyprotech System for Engineers). The simulation results presents the
The state-of-the-art advancement in wind turbine condition monitoring and faultdiagnosis for the recent several years is reviewed. Since the existing surveys on wind turbine condition monitoring cover the literatures up to 2006, this review aims to report the most recent advances in the past three years, with primary focus on gearbox and bearing, rotor and blades, generator and power
A new method of faultdiagnosis based on network-induced delay is presented in this paper to solve the problem of optimal control in networked control systems. The probability distribution of the network-induced delay is discussed. A stochastic optimal controller is designed, which is shown to render corresponding networked control systems mean square exponentially stable.
In this paper, a faultdiagnosis method is proposed for nonlinear networked control systems (NCSs) with random delays. First, a two-layer quasi T-S fuzzy model based on probability is presented for the NCSs. Stochastic and nonlinear features of the NCSs are incorporated in the model. Then, based on this model the fuzzy observer and the residual generator are designed to
A novel faultdiagnosis method of condenser based on kernel principle component analysis (KPCA) and multi-class support vector machines (MSVMs) is proposed in this paper. KPCA is applied to MSVMs for feature extraction. It firstly maps data from the original input space into high dimensional feature space via nonlinear kernel function and then extract optimal feature vector as the inputs
We present three architectures, drawn from intelligent control approaches, that represent our first investigations into the design of a fast-acting faultdiagnosis system for electrical power distribution networks. We present experiments on artificial neural network and fuzzy logic based approaches, then make proposals for a hybrid combination of the two that could have promising potential. All three techniques appear to
Konstantinos Stergiopoulos; Anthony G. Pipe; H. Nouri
A correctly functioning enterprise-software system exhibits long-term, stable correlations between many of its monitoring metrics. Some of these correlations no longer hold when there is an error in the system, potentially enabling error detection and faultdiagnosis. However, existing approaches are inefficient, requiring a large number of metrics to be monitored and ignoring the relative discriminative properties of different metric
Miao Jiang; Mohammad Ahmad Munawar; Thomas Reidemeister; Paul A. S. Ward
Recently, research has picked up a fervent pace in the area of faultdiagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as
Model-based artificial intelligence approaches to diagnosis require encoding a reasonable facsimile of the problem domain. The model is encoded as a classical engineering simulation of the domain, a spacecraft electrical power system (EPS). Portions of the reasoning system thus become comparators between the expected behavior and the EPS. The diagnostic problem is partitioned into six discrete steps including: fault detection,
A user-friendly, interactive software package is described that can be used for faultdiagnosis in dynamic systems. The methodology is based on the representation of system evolution in time as probability of transitions between sets of magnitude intervals in the state\\/parameter space. The software is developed in C++ for Windows NT platform. The display capabilities of the software and its
Over the past few years, many researchers have been attracted by the challenges of electrical machines' faultdiagnosis and condition monitoring, which provide early warnings that could help schedule necessary maintenance to avoid catastrophic consequence. With advancements in the use of rare-earth magnets, Brushless Permanent Magnet Machines are widely used in industry recently, which has led to the development of
Modeling uncertainty is an inevitable consequence of the complexity of jet engine systems, and accurate dynamic models can never be fully obtained. The authors concentrate on the derivation of suitable mathematical models of a jet engine, to enable robust faultdiagnosis designs to be achieved. The modeling uncertainty can be described as an additional term in the dynamic structure. Based
A comprehensive gas turbine faultdiagnosis system has been designed using a full nonlinear simulator developed in Turbotec company for the V94.2 industrial gas turbine manufactured by Siemens AG. The methods used for detection and isolation of faulty components are gas path analysis (GPA) and extended Kalman filter (EKF). In this paper, the main health parameter degradations namely efficiency and
Amin Salar; SeyedMehrdad Hosseini; Ali Khaki Sedigh; Behnam Rezaei Zangmolk
In this paper, an online faultdiagnosis for a complex dynamical systems integrating adaptive neuro-fuzzy inference system (ANFIS) and using independent component analysis (ICA) for feature extracting is presented. In this approach, using ICA provide salient features selected from raw measured data sets. Subsequently, the most superior extracted features are fed into multiple ANFIS in order to identify different abnormal
This paper proposes a new neural networks diagnostic system for on-line faultdiagnosis of power systems using information of relays and circuit breakers. This system has a similar profile to an expert system, but can be constructed much more easily from a few sets of training samples. These samples are used for training of subnets of three neural network modules.
This paper describes the formalization and use of Latent Nesting Method (LNM) using Coloured Petri Nets (CPNs) for faultdiagnosis and recovery in hybrid and com- plex systems. The method presented here will expand the initial proposed using Hybrid Petri Nets (HPNs) for adding the continuous dynamic. This method is illustrated with a comprehensive example of a lubrication and cooling
Leonardo Rodriguez U; Emilio Garcia M; Francisco Morant A; Antonio Correcher S
This annotated bibliography developed in connection with an ongoing investigation of the use of computer simulations for faultdiagnosis training cites 61 published works taken predominantly from the disciplines of engineering, psychology, and education. A review of the existing literature included computer searches of the past ten years of…
This research addresses the need for fault detection and diagnosis (FDD) in residential-style, air conditioner, and heat pump systems in an attempt to make these systems more trouble free and energy efficient over their entire lifetime. This work is one o...
Using the biological immunology principle unifies the neural network and the immunity algorithm to form the immunity neural network, which is applied to electro-hydraulic servo valve breakdown diagnosis. The result indicated that, the immunity neural network can identify many kinds of failures pattern recognition accurately by the smaller network scale, and has the high efficiency, good fault-tolerant performance and formidable
Lian-Dong Fu; Kui-Sheng Chen; Shu-Guang Fu; Long-Yuan Liu; Jin Zhu
Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.
Pennacchi, P.; Ricci, Roberto; Chatterton, S.; Borghesani, P.
The design and evaluation are presented for the knowledge-based assistance of a human operator who must diagnose a novel fault in a dynamic, physical system. A computer aid based on a qualitative model of the system was built to help the operators overcome some of their cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction that are believed to be more suitable for deep reasoning. Four aiding approaches, each of which provided unique information to the operator, were evaluated. The aiding features were designed to help the human's casual reasoning about the system in predicting normal system behavior (N aiding), integrating observations into actual system behavior (O aiding), finding discrepancies between the two (O-N aiding), or finding discrepancies between observed behavior and hypothetical behavior (O-HN aiding). Human diagnostic performance was found to improve by almost a factor of two with O aiding and O-N aiding.
The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro-fuzzy inference system (ANFIS) for online faultdiagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time. PMID:20667537
Lau, C K; Heng, Y S; Hussain, M A; Mohamad Nor, M I
Wavelet analysis has been widely applied to mechanical faultdiagnosis. Aiming at the problems of current wavelet basis, such as low time-frequency sampling, asymmetry and poor shift-invariance, this paper develops a new family of dense framelets with two generators and some desirable properties. To perform the corresponding framelet transform, three-channel iterated filterbank should be used, where the first and the third channel is decimated while the second channel is undecimated. This arrangement is very helpful for extracting the fault feature of the mid and low frequency band signal components and obtaining some symmetric framelets. To obtain framelets with high symmetry and a specified number of vanishing moments, B-spline and maximally flat linear FIR filter are, respectively, used to design filterbank. Three symmetric framelets and one framelets with symmetric low-pass filter and high-pass filter are constructed. Compared with the higher density framelets and orthonormal wavelets, the proposed framelets have better shift-invariance and denoising performance. Finally, the proposed framelets are applied to faultdiagnosis of two gearboxes. The results show that the proposed framelets can be effectively applied to mechanical faultdiagnosis and is superior to other commonly-used framelets/wavelets.
Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean network model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean network model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean network model for the oxidative stress response. This model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI3k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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
Recently, the conventional controller of machine-tool has been increasingly replaced by the PC-based open architecture controller,\\u000a which is independent of the CNC vendor and on which it is possible to implement user-defined application programs. This paper\\u000a proposes CNC-implemented faultdiagnosis and web-based remote services for machine-tool with open architecture CNC. The faults\\u000a of CNC machine-tool are defined as the operational
Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).
Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.
Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.
Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, statistical analysis and artificial intelligence techniques. However, they
Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.
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.
A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for faultdiagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are faultdiagnosis and failure prognosis. With the goal of designing an efficient and reliable faultdiagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.
In this paper, we propose an approach for achieving detection and identification of faults, and provide fault tolerant control for systems that are modeled using timed hybrid Petri nets. For this purpose, an observer based technique is adopted which is useful in detection of faults, such as sensor faults, actuator faults, signal conditioning faults, etc. The concepts of estimation, reachability and diagnosability have been considered for analyzing faulty behaviors, and based on the detected faults, different schemes are proposed for achieving fault tolerant control using optimization techniques. These concepts are applied to a typical three tank system and numerical results are obtained. PMID:21507399
Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faultsdiagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis.
Chou, H.P. (National Tsing-Hua Univ., Hsinchu (Taiwan, Province of China)); Prock, J.; Bonfert, J.P. (Institute of Safety Technology, Garching (Germany))
Embedded bus monitoring and faultdiagnosis system, which was based on protocol SAE J1939 was designed in this paper. And this system took the 32-bit embedded one as a hardware platform, customized a WinCE6.0 operation system and used EVC as the tool to design the embedded application. The functions of CAN communication, protocol defamations etc were realized. Good human-computer interaction
Sun Wei; Li Jun; Gao Ying; Qu Dawei; Yang Chenghong
Current spacecraft health monitoring and faultdiagnosis practices that involve around-the-clock limit-checking and trend analysis on large amount of telemetry data, do not scale well for future multi-platform space missions due to the presence of larger amount of telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team. The need
To tackle the flaws in transformer faultdiagnosis such as long computing time, weak generalized ability and fuzzy knowledge acquisition difficulty, a self-adaptive neuro-fuzzy inference system (ANFIS) is proposed based on emotional learning in this paper. The method can automatically adapt itself to the change of input information characteristics, and compensate for the flaws of the imperfectness of the 3-ratio-code.
An automatic scheme for faultdiagnosis and location of stator-winding interturns in permanent-magnet brushless dc motors is presented. System performances under healthy and faulty operation are obtained via a discrete-time model. Waveform of the electromagnetic torque is monitored and processed using discrete Fourier transform and short-time Fourier transform to derive proper diagnostic indices. Two adaptive neuro-fuzzy inference systems (ANFIS) are
This paper proposes an expert system for faultdiagnosis of a power system using a new inference method. Expertise is, in this paper, represented by logical implications and converted into a Boolean function. Unlike conventional rule-based expert systems, the expertise is converted into Prime Implicants (PIs) which are logically complete and sound. Therefore, off-line inference is possible by off-line identification
Using feedforward neural networks (FNNs), a faultdiagnosis and severity assessment (FDIA) scheme for a screw compressor has been established. This FDIA method consists of non-linear model identification of the compressor and pattern classification of parameters of identified models corresponding to the various faulty conditions. First, a non-linear input/output model is identified in the form FNNs. Then an FNN classifies the FNN model into one of the possible faults or the baseline conditions. If the model is classified as a faulty one, another FNN is used to assess the severity of the fault. A fully automatic structure and weight learning algorithm for FNNs is utilised to identify an FNN non-linear model from operating data of the compressor. To establish training data for the classifiers, measurements of motor current and shaft speed were made under baseline conditions and faulty conditions such as various extents of gaterotor wear and increased rolling friction. Experimental results show that the scheme is capable of not only diagnosing the faults but also assessing the magnitude of faults.
The induction motors, which have simple structures and design, are the essential elements of the industry. Their long-lasting utilization in critical processes possibly causes unavoidable mechanical and electrical defects that can deteriorate the production. The early diagnosis of the defects in induction motors is crucial in order to avoid interruption of manufacturing. In this work, the mechanical and the electrical faults which can be observed frequently on the induction motors are classified by means of analysis of the acoustic data of squirrel cage induction motors recorded by using several microphones simultaneously since the true nature of propagation of sound around the running motor provides specific clues about the types of the faults. In order to reveal the traces of the faults, multiple microphones are placed in a hemispherical shape around the motor. Correlation and wavelet-based analyses are applied for extracting necessary features from the recorded data. The features obtained from same types of motors with different kind of faults are used for the classification using the Self-Organizing Maps method. As it is described in this paper, highly motivating results are obtained both on the separation of healthy motor and faulty one and on the classification of fault types.
After analysing the flaws of conventional faultdiagnosis methods, data mining technology is introduced to faultdiagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.
Supervisory Control and Data Acquisition (SCADA) systems are fundamental tools for quick faultdiagnosis and efficient restoration\\u000a of power systems. When multiple faults, or malfunctions of protection devices occur in the system, the SCADA system issues\\u000a many alarm signals rapidly and relays these to the control center. The original cause and location of the fault can be difficult\\u000a to determine
Ching Lai Hor; Peter Crossley; Simon Watson; Dean Millar
The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called "baseline." However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second hand or have been purchased from unknown suppliers, meaning that the baseline is unknown. In this paper, a novel vibration-based fault diagnostic method is developed to identify the vital components of a diesel engine that have abnormal clearance. The advantage of this method is that it does not require the comparison of current operating parameters to those collected as the baseline. First, the nominal baseline is obtained via theoretical modeling rather than being actually captured from the sensory signals in a healthy condition. The abnormal clearance is then determined by inspecting the timing of impacts created by the components that had abnormal clearance during operation. To detect the timing of these impacts from vibration signals accurately, soft-re-sampling and empirical mode decomposition (EMD) techniques are employed. These techniques have integrated with our proposed ranged angle (RA) analysis to form a new ranged angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of the RA-EMD in detecting the impacts and their times of occurrence, their induced vibrations are collected from a series of generators under normal and faulty engine conditions. The results show that this method is capable of extracting the impacts induced by vibrations and is able to determine their times of occurrence accurately even when the impacts have been overwhelmed by other unrelated vibration signals. With the help of the RA-EMD, clearance-related faults, such as incorrect open and closed valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence, proper remedies can be applied to defective diesel engines to ensure that valuable fuel is not wasted due to the incorrect timing of combustion as well as unexpected fatal breakdown, which may cause loss of production or even human casualties, can be minimized.
Li, Yujun; Tse, Peter W.; Yang, Xin; Yang, Jianguo
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
This paper present an improved 2D bearing model for investigation of the vibrations of a ball-bearing during run-up. The presented numerical model assumes deformable outer race, which is modelled with finite elements, centrifugal load effects and radial clearance. The contact force for the balls is described by a nonlinear Hertzian contact deformation. Various surface defects due to local deformations are introduced into the developed model. The detailed geometry of the local defects is modelled as an impressed ellipsoid on the races and as a flattened sphere for the rolling balls. The obtained equations of motion were solved numerically with a modified Newmark time-integration method for the increasing rotational frequency of the shaft. The simulated vibrational response of the bearing with different local faults was used to test the suitability of the continuous wavelet transformation for the bearing fault identification and classification.
Morphological analysis is a signal processing method that extracts the local morphological features of a signal by intersecting it with a structuring element (SE). When a bearing suffers from a localized fault, an impulse-type cyclic signal is generated. The amplitude and the cyclic time interval of impacts could reflect the health status of the inspected bearing and the cause of defects, respectively. In this paper, an enhanced morphological analysis called ‘morphogram’ is presented for extracting the cyclic impacts caused by a certain bearing fault. Based on the theory of morphology, the morphogram is realized by simple mathematical operators, including Minkowski addition and subtraction. The morphogram is able to detect all possible fault intervals. The most likely fault-interval-based construction index (CI) is maximized to establish the optimal range of the flat SE for the extraction of bearing fault cyclic features so that the type and cause of bearing faults can be easily determined in a time domain. The morphogram has been validated by simulated bearing fault signals, real bearing faulty signals collected from a laboratorial rotary machine and an industrial bearing fault signal. The results show that the morphogram is able to detect all possible bearing fault intervals. Based on the most likely bearing fault interval shown on the morphogram, the CI is effective in determining the optimal parameters of the flat SE for the extraction of bearing fault cyclic features for bearing faultdiagnosis.
This paper presents the formal verification of a new protocol for online distributed diagnosis for the SPIDER family of architectures. An instance of the Scalable Processor-Independent Design for Electromagnetic Resilience (SPIDER) architecture consists of a collection of processing elements communicating over a Reliable Optical Bus (ROBUS). The ROBUS is a specialized fault-tolerant device that guarantees Interactive Consistency, Distributed Diagnosis (Group Membership), and Synchronization in the presence of a bounded number of physical faults. Formal verification of the original SPIDER diagnosis protocol provided a detailed understanding that led to the discovery of a significantly more efficient protocol. The original protocol was adapted from the formally verified protocol used in the MAFT architecture. It required O(N) message exchanges per defendant to correctly diagnose failures in a system with N nodes. The new protocol achieves the same diagnostic fidelity, but only requires O(1) exchanges per defendant. This paper presents this new diagnosis protocol and a formal proof of its correctness using PVS.
The Kurtogram is based on the kurtosis of temporal signals that are filtered by the short-time Fourier transform (STFT), and has proved useful in the diagnosis of bearing faults. To extract transient impulsive signals more effectively, wavelet packet transform is regarded as an alternative method to STFT for signal decomposition. Although kurtosis based on temporal signals is effective under some conditions, its performance is low in the presence of a low signal-to-noise ratio and non-Gaussian noise. This paper proposes an enhanced Kurtogram, the major innovation of which is kurtosis values calculated based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths. The power spectrum of the envelope of the signals defines the sparse representation of the signals and kurtosis measures the protrusion of the sparse representation. This enhanced Kurtogram helps to determine the location of resonant frequency bands for further demodulation with envelope analysis. The frequency signatures of the envelope signal can then be used to determine the type of fault that has affected a bearing by identifying its characteristic frequency. In many cases, discrete frequency noise always exists and may mask the weak bearing faults. It is usually preferable to remove such discrete frequency noise by using autoregressive filtering before the enhanced Kurtogram is performed. At last, we used a number of simulated bearing fault signals and three real bearing fault signals obtained from an experimental motor to validate the efficiency of these proposed modifications. The results show that both the proposed method and the enhanced Kurtogram are effective in the detection of various bearing faults.
A new approach to faultdiagnosis of gear crack based on ensemble empirical mode decomposition (EEMD) and Hilbert-Huang transform (HHT) technique is presented. Firstly, the time-domain vibration signal of the gearbox with gear crack fault is measured. Then the original vibration signal is separated into intrinsic oscillation modes, using the ensemble empirical mode decomposition. Secondly, Hilbert transform tracks the modulation
The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension. Currently, nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings, such as manifold learning. However, these methods are all based on manual intervention, which have some shortages in stability, and suppressing the disturbance noise. To extract features automatically, a manifold learning method with self-organization mapping is introduced for the first time. Under the non-uniform sample distribution reconstructed by the phase space, the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation. Finally, the signal is reconstructed by the kernel regression. Several typical states include the Lorenz system, engine fault with piston pin defect, and bearing fault with outer-race defect are analyzed. Compared with the LTSA and continuous wavelet transform, the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified. A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.
Chen, Xiaoguang; Liang, Lin; Xu, Guanghua; Liu, Dan
This paper proposes a weak signal detection strategy for rolling element bearing faultdiagnosis by investigating a new mechanism to realize stochastic resonance (SR) based on the Woods-Saxon (WS) potential. The WS potential has the distinct structure with smooth potential bottom and steep potential wall, which guarantees a stable particle motion within the potential and avoids the unexpected noises for the SR system. In the Woods-Saxon SR (WSSR) model, the output signal-to-noise ratio (SNR) can be optimized just by tuning the WS potential's parameters, which delivers the most significant merit that the limitation of small parameter requirement of the classical bistable SR can be overcome, and thus a wide range of driving frequencies can be detected via the SR model. Furthermore, the proposed WSSR model is also insensitive to the noise, and can detect the weak signals with different noise levels. Additionally, the WS potential can be designed accurately due to its parameter independence, which implies that the proposed method can be matched to different input signals adaptively. With these properties, the proposed weak signal detection strategy is indicated to be beneficial to rolling element bearing faultdiagnosis. Both the simulated and the practical bearing fault signals verify the effectiveness and efficiency of the proposed WSSR method in comparison with the traditional bistable SR method.
The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight faults has been developed. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of faults, maladjustments and malfunctions, has led us to take two approaches to faultdiagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes the reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the diagnosis of each.
Ali, M.; Scharnhorst, D. A.; Ai, C. S.; Ferber, H. J.
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 faultdiagnosis 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)
In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of 1's and 0's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time faultdiagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMS-RT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) Hidden Markov Model based diagnosis.
Kirubarajan, Thiagalingam; Malepati, Venkatesh N.; Deb, Somnath; Ying, Jie
The objective of this research is to investigate the feasibility of utilizing the hybrid method of ensemble empirical mode decomposition (EEMD) and pure empirical mode decomposition (EMD) to efficiently decompose the complicated vibration signals of rotating machinery into a finite number of intrinsic mode functions (IMFs), so that the fault characteristics of the misaligned shaft can be examined in the time-frequency Hilbert spectrum as well as the marginal Hilbert spectrum. The intrawave frequency modulation (FM) phenomenon, which indicates the nonlinear vibration behavior of a misaligned shaft, can be observed in the time-frequency Hilbert spectrum through the Hilbert-Huang transform (HHT) technique. The fault characteristic of shaft misalignment is also featured in terms of the amplitude modulation (AM) phenomenon in the information-containing IMF components that are extracted by the significance test. Through performing the envelope analysis on the information-containing IMF, the marginal Hilbert spectrum of the envelope signal of this IMF component exhibits that the level of shaft misalignment is presented by the level of AM in the IMF. A test bed of a rotor-bearing system is performed experimentally to illustrate both the parallel and angular shaft misalignment conditions as well as the healthy condition. The analysis results show that the proposed approach is capable of diagnosing the misaligned fault of the shaft in rotating machinery and providing a more meaningful physical insight compared with the conventional methods.
This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effect of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. We used sensitivity and criterion based on Signal Detection Theory to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.
Austin Ragsdale; Roger Lew; Brian P. Dyre; Ronald L. Boring
The paper has extracted the energy spectrum entropy of wavelet packet as the eigen vector of fault patterns, through analyzing the vibration signal in the decomposition of wavelet packet when Low-Voltage (LV) Circuit Breaker broke down. Based on the concept of Clustering Center, a Nai?ve Bayesian classifier has been constructed. By using the weight of probability measure, the correlations between
In this work a new method is proposed for the diagnosis of faults in electric power transmission systems based on neural modularity. This method performs the diagnosis through the assignation of a generic neural module for each type of element conforming the transmission system, whether it be line, bus or transformer. A total of three generic neural modules are designed,
Agustin Flores; Eduardo Quiles; Emilio García; Francisco Morant
This paper presents an application example using the lating nestling method for the faultdiagnosis based in the use of coloured Petri nets, to a lubrication and cooling system in the wind turbinepsilas gearbox with a critical subsystem as far as failure probability. It demonstrate the synthesis capacity of the method for any model of diagnosis and isolation, giving as
Leonardo Rodríguez Urrego; Emilio García Moreno; Francisco José Morant Anglada; Antonio Correcher Salvador; Eduardo Quiles Cucarella
A hard competitive growing neural network (HC-GNN) with shrinkage learning is put forward to detect and diagnose small bearing faults. Structure determination based on supervised learning is an important issue in pattern classification. For that reason, the proposed approach introduces new hidden units whenever necessary and adjusts their shapes to minimize the risk of misclassification. This leads to smaller networks compared to classical radial basis functions or probabilistic neural networks and therefore enables the use of large data sets with satisfactory classification accuracy. This technique is based on the following concepts: (1) growing architecture, (2) dynamic adaptive learning, (3), convergence by means of several criteria, (4) embedded weighted feature selection, and (5) optimized network structure. HC-GNN consists of two main stages and runs in an iterative way. The first stage learns weighted selected parameters to well-known classes while the second stage associates the testing parameters of unknown samples to the learned classes. This approach is applied on a machinery system with different small bearing faults at various speeds and loads. The challenge is to detect and diagnose these faults regardless of the motor's shaft speed. Obtained results are analyzed, explained and compared with various techniques that have been widely investigated in diagnosis area.
The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher workloads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) is developed to assist pilots in monitoring, diagnosing and recovering from in-flight faults. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with airplane status information during the diagnostic process. The simulator is based on the Lockheed Advanced Concept System (ACS), a future generation airplane, and on the Boeing 737. A distinction between two types of faults, maladjustments and malfunctions, has led to two approaches to faultdiagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to access abnormal situations during airplane flight. Malfunctions and maladjustments are handled separately, diagnosed using domain knowledge.
Ali, Moonis; Scharnhorst, D. A.; Ai, C. S.; Feber, H. J.
Time-frequency feature is beneficial to representation of non-stationary signals for effective machinery faultdiagnosis. The time-frequency distribution (TFD) is a major tool to reveal the synthetic time-frequency pattern. However, the TFD will also face noise corruption and dimensionality reduction issues in engineering applications. This paper proposes a novel nonlinear time-frequency feature based on a time-frequency manifold (TFM) technique. The new TFM feature is generated by mainly addressing manifold learning on the TFDs in a reconstructed phase space. It combines the non-stationary information and the nonlinear information of analyzed signals, and hence exhibits valuable properties. Specifically, the new feature is a quantitative low-dimensional representation, and reveals the intrinsic time-frequency pattern related to machinery health, which can effectively overcome the effects of noise and condition variance issues in sampling signals. The effectiveness and the merits of the proposed TFM feature are confirmed by case study on gear wear diagnosis, bearing defect identification and defect severity evaluation. Results show the value and potential of the new feature in machinery fault pattern representation and classification.
A two-level hierarchical approach for process faultdiagnosis 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.
A two-level hierarchical approach for process faultdiagnosis 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)
This study was designed to confirm the results of Finkenberg et al. (J Hand Surg 1993;18A: 4-7), who found a high sensitivity (100%) and specificity (95%) of the intrasound vibration method in diagnosing occult scaphoid fractures. These occult scaphoid fractures are not visible on x-ray films, but clinically the patients are suspected of having a scaphoid fracture. A vibratory apparatus is placed over the anatomical snuff-box and a vibration of 100 mW is emitted; a painful sensation is produced if the scaphoid is fractured. Thirty-seven consecutive patients with a clinically suspected scaphoid fracture were evaluated. In 6 patients, a scaphoid fracture was radiographically identified; in the remaining 31 patients, a 3-phase bone scan was obtained. Eleven wrists showed increased uptake over the scaphoid and were considered to have an occult scaphoid fracture. In this group, bone scintigraphy was used as the reference standard. The vibration test was painful in 1 of 6 patients with a proven scaphoid fracture and in 3 of the 11 patients with a positive bone scan. In contrast to the results of Finkenberg et al, the intrasound vibration method shows a sensitivity of 24%, a specificity of 85%, a positive predictive value of 40%, and a negative predictive value of 65%. We conclude that the accuracy of intrasound vibration is low and that it is not useful in the diagnosis of scaphoid fractures. PMID:9556260
The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery.
In view of the crucial deficiency of the traditional diagnosis approaches for photoelectric tracking devices and the output of more sufficient diagnosis information, in this paper, an virtual faultdiagnosis system based on open graphic library(OpenGL) is proposed. Firstly, some interrelated key principles and technology of virtual reality, visualization and intelligent faultdiagnosis technology are put forward. Then, the demand analysis and architecture of the system are elaborated. Next, details of interrelated essential implementation issues are also discussed, including the the 3D modeling of the related diagnosis equipments, key development process and design via OpenGL. Practical applications and experiments illuminate that the proposed approach is feasible and effective.
Hou, Mingliang; Li, Cunhua; Zhang, Yong; Su, Liyun
An effective approach for faultdiagnosis of aeroengine based on integration of wavelet analysis and neural networks is presented. The wavelet transform can accurately localizes the characteristics of a signal in time-frequency domains and in a view of the inter relationship of wavelet transform between exponent theory, the whole and local exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function for fault pattern recognition. The faultdiagnosis model of aero-engine is established and the improved Levenberg-Marquardt training algorithm is used to fulfill the network structure and parameter identification. By choosing enough samples to train the faultdiagnosis network and the information representing the faults input into the neural network, the fault pattern can be determined. The robustness of wavelet neural network for faultdiagnosis is discussed. The practical faultdiagnosis for aeroengine vibration approves to be accurate and comprehensive.
The Hilbert-Huang transform and its marginal spectrum are applied to bearing faultdiagnosis of ball bearing. The principle of Empirical mode decomposition (EMD), Hilbert-Huang transformation (HHT) and marginal spectrum is introduced. Firstly, the vibration signals of bearing fault are separated into several intrinsic mode functions (IMFs) using EMD method. Secondly, the marginal spectrum of each IMF is calculated. In the
Infrared thermography has been used routinely in industrial applications for quite a long time. For example, the condition of electric power lines, district heating networks, electric circuits and components, heat exchangers, pipes and its insulations, cooling towers, and various machines and motors is monitored using infrared imaging techniques. Also the usage of this technology in predictive maintenance has proved successful, mainly because of effective computers and tailored softwares available. However, the usage of thermal sensing technique in fluid power systems and components (or other automation systems in fact) is not as common. One apparent reason is that a fluid power circuit is not (and nor is a hydraulic component) an easy object of making thermal image analyses. Especially the high flow speed, fast pressure changes and fast movements make the diagnosis complex and difficult. Also the number of people whose knowledge is good both in thermography and fluid power systems is not significant. In this paper a preliminary study of how thermography could be used in the condition monitoring, faultdiagnosis and predictive maintenance of fluid power components and systems is presented. The shortages and limitations of thermal imaging in the condition monitoring of fluid power are also discussed. Among many other cases the following is discussed: (1) pressure valves (leakage, wrong settings), (2) check valves (leakage); (3) cylinders (leakage and other damages); (4) directional valves and valve assemblies; (5) pumps and motors (leakage in piston or control plate, bearings). The biggest advantage of using thermography in the predictive maintenance and faultdiagnosis of fluid power components and systems could be achieved in the process industry and perhaps in the commissioning of fluid power systems in the industry. In the industry the predictive maintenance of fluid power with the aid of an infrared camera could be done as part of a condition monitoring of other systems, for instance bearings.
In this paper an improved bearing model is developed in order to investigate the vibrations of a ball bearing during run-up. The numerical bearing model was developed with the assumptions that the inner race has only 2 DOF and that the outer race is deformable in the radial direction, and is modelled with finite elements. The centrifugal load effect and the radial clearance are taken into account. The contact force for the balls is described by a nonlinear Hertzian contact deformation. Various surface defects due to local deformations are introduced into the developed model. The detailed geometry of the local defects is modelled as an impressed ellipsoid on the races and as a flattened sphere for the rolling balls. With the developed bearing model the transmission path of the bearing housing can be taken into account, since the outer ring can be coupled with the FE model of the housing. The obtained equations of motion were solved numerically with a modified Newmark time-integration method for the increasing rotational frequency of the shaft. The simulated vibrational response of the bearing with different local faults was used to test the suitability of the envelope analysis technique and the continuous wavelet transformation was used for the bearing-fault identification and classification.
The integrity of the electric motors in work and passenger vehicles can best be maintained by monitoring its condition frequently on-board the vehicle. In this paper, a signal processing based faultdiagnosis scheme for on-board diagnosis of rotor asymmetry at start-up and idle mode is presented. Regular rotor asymmetry tests are done when the motor is running at certain speed
Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281
Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus
Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.
Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus
Interturn faultdiagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results. PMID:24808044
A faultdiagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing faultdiagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed faultdiagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.
A faultdiagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing faultdiagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed faultdiagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197
The characteristic signal of a rolling bearing with a defect acts as a series of periodic impulses. These features are usually immersed in heavy noise and then difficult to extract. It is feasible to make the features distinct through wavelet denoising. Scalar wavelet thresholding has been used to extract features. However, scalar wavelet might not extract the feature available due to its limitation in some important properties, and conventional term-by-term thresholding does not consider the effect of neighboring coefficients. Since multiwavelets have been formulated recently and they might offer good properties in signal processing, a novel denoising method — multiwavelet denoising with improved neighboring coefficients (neighboring coefficients dependent on level, DLNeighCoeff for short) — is proposed in this article. The method proposed is applied to a simulated signal and faultdiagnosis of locomotive rolling bearings, obtaining performance superior to conventional methods.
Raynaud's phenomenon often represents an early symptom of collagen diseases as well as of occlusive vascular disease; it can as well be due to vibration trauma following development of mechanical lesion of the acral capillary system. A rare case of professionally acquired vibration trauma in a miner who developed vasospastic ischaemia after 20 years subterranean work is reported. Non-invasive blood-flow measurements such as thermic relaxation time, transcutaneous O2-pressure, and laser-doppler perfusion rate, represent suitable means of diagnosis and observation of the course of the disease. Combination therapy including both primary inhibition of platelet aggregation as well as improvement of rheological properties has proved to be superior to other treatment plans. PMID:2501078
This paper presents a new faultdiagnosis technology for event-driven controlled systems such as Pro- grammable Logic Control (PLC). The controlled plant is modeled by means of the Timed Markov Model, which regards the time interval between successive two events as a random variable. In order to estimate the probability density functions of the randomized time intervals, the maximum entropy
The paper analyzes the merits and drawbacks of the genetic algorithm and BP neural network, combines with the improved genetic algorithm and BP neural network to obtain a new algorithm. The new algorithm is used in the faultdiagnosis of electro-hydraulic servo valve and justified its validity, accuracy and rapidity by experiment. The BP algorithm, the conventional GA-BP algorithm and
Low frequent vibrations may cause from disturbing up to damaging effects. There is no precise distinction between structure-borne sound and vibrations. However - depending on the frequency range - measurements and predictions require different techniques. In a wide frequency range, the generation, transmission and propagation of vibrations can be investigated similar to structure-borne sound (see Chap. 9).
This paper presents a signal processing method - amplitude recovery method (abbreviated to ARM) - that can be used as the signal pre-processing for fast Fourier transform (FFT) in order to analyze the spectrum of the other-order harmonics rather than the fundamental frequency in stator currents and diagnose subtle faults in induction motors. In this situation, the ARM functions as a filter that can filter out the component of the fundamental frequency from three phases of stator currents of the induction motor. The filtering result of the ARM can be provided to FFT to do further spectrum analysis. In this way, the amplitudes of other-order frequencies can be extracted and analyzed independently. If the FFT is used without the ARM pre-processing and the components of other-order frequencies, compared to the fundamental frequency, are fainter, the amplitudes of other-order frequencies are not able easily to extract out from stator currents. The reason is when the FFT is used direct to analyze the original signal, all the frequencies in the spectrum analysis of original stator current signal have the same weight. The ARM is capable of separating the other-order part in stator currents from the fundamental-order part. Compared to the existent digital filters, the ARM has the benefits, including its stop-band narrow enough just to stop the fundamental frequency, its simple operations of algebra and trigonometry without any integration, and its deduction direct from mathematics equations without any artificial adjustment. The ARM can be also used by itself as a coarse-grained diagnosis of faults in induction motors when they are working. These features can be applied to monitor and diagnose the subtle faults in induction motors to guard them from some damages when they are in operation. The diagnosis application of ARM combined with FFT is also displayed in this paper with the experimented induction motor. The test results verify the rationality and feasibility of the ARM. It should be clarified that the ARM must be applied in three phases of currents in electrical machines. For a single phase of alternating current or direct current, it can do nothing. However, since three-phase electrical machines have a dominant position in the application field in modern economic society and it is natural and convenient to acquire three phases of stator currents during the three-phase electrical machines are tested, it is necessary and meaningful to develop the ARM to diagnose and guard them.
The numerical modelling and process simulation for the faultdiagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control. PMID:11954726
The advent of molecularly targeted therapies requires effective identification of the various cell types of non-small cell lung carcinomas (NSCLC). Currently, cell type diagnosis is performed using small biopsies or cytology specimens that are often insufficient for molecular testing after morphologic analysis. Thus, the ability to rapidly recognize different cancer cell types, with minimal tissue consumption, would accelerate diagnosis and preserve tissue samples for subsequent molecular testing in targeted therapy. We report a label-free molecular vibrational imaging framework enabling three-dimensional (3-D) image acquisition and quantitative analysis of cellular structures for identification of NSCLC cell types. This diagnostic imaging system employs superpixel-based 3-D nuclear segmentation for extracting such disease-related features as nuclear shape, volume, and cell-cell distance. These features are used to characterize cancer cell types using machine learning. Using fresh unstained tissue samples derived from cell lines grown in a mouse model, the platform showed greater than 97% accuracy for diagnosis of NSCLC cell types within a few minutes. As an adjunct to subsequent histology tests, our novel system would allow fast delineation of cancer cell types with minimum tissue consumption, potentially facilitating on-the-spot diagnosis, while preserving specimens for additional tests. Furthermore, 3-D measurements of cellular structure permit evaluation closer to the native state of cells, creating an alternative to traditional 2-D histology specimen evaluation, potentially increasing accuracy in diagnosing cell type of lung carcinomas.
Gao, Liang; Hammoudi, Ahmad A.; Li, Fuhai; Thrall, Michael J.; Cagle, Philip T.; Chen, Yuanxin; Yang, Jian; Xia, Xiaofeng; Fan, Yubo; Massoud, Yehia; Wang, Zhiyong; Wong, Stephen T. C.
This dissertation presents a reliable technique for monitoring the condition of rotating machinery by applying instantaneous angular speed (IAS) analysis. A new analysis of the effects of changes in the orientation of the line of action and the pressure angle of the resultant force acting on gear tooth profile of spur gear under different levels of tooth damage is utilized. The analysis and experimental work discussed in this dissertation provide a clear understating of the effects of damage on the IAS by analyzing the digital signals output of rotary incremental optical encoder. A comprehensive literature review of state of the knowledge in condition monitoring and fault diagnostics of rotating machinery, including gearbox system is presented. Progress and new developments over the past 30 years in failure detection techniques of rotating machinery including engines, bearings and gearboxes are thoroughly reviewed. This work is limited to the analysis of a gear train system with gear tooth surface faults utilizing angular motion analysis technique. Angular motion data were acquired using an incremental optical encoder. Results are compared to a vibration-based technique. The vibration data were acquired using an accelerometer. The signals were obtained and analyzed in the phase domains using signal averaging to determine the existence and position of faults on the gear train system. Forces between the mating teeth surfaces are analyzed and simulated to validate the influence of the presence of damage on the pressure angle and the IAS. National Instruments hardware is used and NI LabVIEW software code is developed for real-time, online condition monitoring systems and fault detection techniques. The sensitivity of optical encoders to gear fault detection techniques is experimentally investigated by applying IAS analysis under different gear damage levels and different operating conditions. A reliable methodology is developed for selecting appropriate testing/operating conditions of a rotating system to generate an alarm system for damage detection.
Man's reactions to vibration are emphasized rather than his reactions to the vibrational characteristics of vehicles. Vibrational effects studies include: performance effects reflected in tracking proficiency, reaction time, visual impairment, and other measures related to man's ability to control a system; physiological reactions; biodynamic responses; subjective reactions; and human tolerance limits. Technological refinements in shaker systems and improved experimental designs are used to validate the data.
There have been many studies of thermographic diagnosis of vibration disease, but few of them seem to have discussed tie-tamping machines as a cause. This study focuses on thermographic diagnosis of vibration disease in tie-tamper operators of the Japanese National Railways. In the diagnosis the subject's both hands were immersed in water at 10 degrees C for 3 minutes before being examined. Variables such as season, age, type of vibration tool used and total operating time were considered. These were selected as outside variables and thermographic results as dependent variables, in Quantification Method II. Season and confirmation of vibration disease were found to have a relationship to thermographic scaling, but no such relationship was found for age, type of vibration tool used, or total operating time. A cross-analysis of variables confirmed the relationship with season, and revealed that there were fewer confirmed cases of vibration disease in spring and summer than in fall and winter. It was finally concluded that thermographic analysis is more reliable in colder weather. PMID:6090740
This paper deals with gear condition monitoring based on vibration analysis techniques. The detection and diagnostic capability of some of the most effective techniques are discussed and compared on the basis of experimental results, concerning a gear pair affected by a fatigue crack. In particular, the results of new approaches based on time-frequency and cyclostationarity analysis are compared against those
Nowadays a novel permanent magnetic fault tolerant synchronous motor which can realize electric, magnetic, heat, and physic insulation between each phase has brought broad attention in aeronautics and astronautics field. The key technique to achieve fault tolerant control is that system could detect custom electric fault on line. Because the signal of few turns when encountering short circuits is feeble,
In previous study Petri net models were developed for detecting fault location in power system. In the current work, we proposed two models to diagnose the fault: the neural Petri net (NPN) and the fuzzy neural Petri net (FNPN). These models based on underlying Petri net. When the faults occur in power system, it is inevitable that a great amount
A one-step tau-fault diagnosable system is a system in which all faults may be identified from the test results, provided that the number of faults does not exceed tau. In this paper we present two algorithms that may be used for the one-step diagnosabili...
Due to the complexity of the self-propelled fire control system and the difficulty of test entrance's selection, the support vector machine combined with text categorization method (TC-SVM) is applied as the first step of faultdiagnosis. The text pretreatment of text representation and feature extraction are introduced first. Then the error-correcting code is used to reduce the multiclass classifier to
Twenty pilots with instrument flight ratings were asked to perform a fault-diagnosis task for which they had relevant domain knowledge. The pilots were asked to think out loud as they requested and interpreted information. Performances were then modeled as the activation and use of a frame system. Cognitive biases, memory distortions and losses, and failures to correctly diagnose the problem were studied in the context of this frame system model.
Smith, Philip J.; Giffin, Walter C.; Rockwell, Thomas H.; Thomas, Mark
Nonstationary signal analysis is one of the main topics in the field of machinery faultdiagnosis. Time-frequency analysis can identify the signal frequency components, reveals their time variant features, and is an effective tool to extract machinery health information contained in nonstationary signals. Various time-frequency analysis methods have been proposed and applied to machinery faultdiagnosis. These include linear and bilinear time-frequency representations (e.g., wavelet transform, Cohen and affine class distributions), adaptive parametric time-frequency analysis (based on atomic decomposition and time-frequency auto-regressive moving average models), adaptive non-parametric time-frequency analysis (e.g., Hilbert-Huang transform, local mean decomposition, and energy separation), and time varying higher order spectra. This paper presents a systematic review of over 20 major such methods reported in more than 100 representative articles published since 1990. Their fundamental principles, advantages and disadvantages, and applications to faultdiagnosis of machinery have been examined. Some examples have also been provided to illustrate their performance.
Online monitoring of rotary machines, like induction motors, can effectively diagnosis electrical and mechanical faults. The origin of most recurrent faults in rotary machines is in the components: bearings, stator, rotor and others. Different methodologies based on current and vibration monitoring have been proposed using FFT and wavelet analysis for preventive monitoring of induction motors resulting in countless techniques for
E. Cabal-Yepez; Roque A. Osornio-Rios; René de Jesús Romero-Troncoso; J. R. Razo-Hernandez; R. Lopez-Garcia
Searching for solutions to manufacture industries, which every day deal with problems of faults in their process, that generate economics and humans main losses, an algorithm to construct a Petri Nets based model and diagnoser to isolate and fault detection of discrete events systems is presented. This algorithm is developed in a real process of liquids packaging, where we can
Miguel Angel Trigos Martinez; Emilio García Moreno
This paper presents research in model-based fault diagnostics for the power electronics inverter-based induction motor drives. A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals wer...
A connectionist network is introduced for faultdiagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.
Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.
In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of I's and O's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time faultdiagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMSAT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) HidderMarkov Model based diagnosis.
Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie
A new combined method based on wavelet transformation, fuzzy logic and neuro-networks is proposed for faultdiagnosis of a triplex. The failure characteristics of the fluid- and dynamic-end can be divided into wavelet transform in different scales at the same time (in: Jun Zhu et al. (Eds.), Proceedings of an International Conference on Condition Monitoring. National Defense Industry Press, Beijing, 1997, pp. 271-275). Therefore, the characteristic variables can be constructed making use of the coefficients of Edgeworth asymptotic spectrum expansion formula and fuzzified to train the neuro-network to identify the faults of fluid- and dynamic-end of triplex pump in fuzzy domain. Tests indicate that the information of wavelet transformation in scale 2 is related to the meshing state of the gear and the information in scales 4 and 5 is related to the running state of fluid-end. Good agreement between analytical and experimental results has been obtained.
The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN. The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system’s validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault.
Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the faultdiagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the faultdiagnosis and the condition monitoring of machine tools.
This paper studies an absolute positioning sensor for a high-speed maglev train and its faultdiagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619
This paper studies an absolute positioning sensor for a high-speed maglev train and its faultdiagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project.
In this paper, we present a comprehensive methodology for a formal, but intuitive, cause-effect dependency modeling using multi-signal directed graphs that correspond closely to hierarchical system schematics and develop diagnostic strategies to isolate faults in the shortest possible time without making the unrealistic single fault assumption. A key feature of our methodology is that our models lend naturally to real-world
This book contains over 30 selections. Some of the titles are: A New Method in the FaultDiagnosis of Turbomachine and Its Application; Vibration Control of a Cylindrical Off-Shore Structure; Design Evaluation of Flow-Induced Vibrations for a Large Shell and Tube Type Nuclear Heat Exchanger; Simulation of Fluid-Structure Interaction Between a Drywell Penetration and a High Energy Line Break in a BWR.
The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.
? Abstract - This paper investigates the recent advances on digital signal processing techniques for induction machines diagnosis. Since non-invasive sensors offer a relatively simple and cost effective faultdiagnosis, more emphasis is given to stator current analysis rather than vibration or acoustic analysis in induction machines. Here, further interest has been paid on modern signal processing techniques with a
A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time–frequency approach, wavelet transform (WT) has inherent problems of large computational time
This paper presents sensitivity of Frequency Response Analysis (FRA) method applied to detection of interturn faults. Frequency response has been carried out on a special Reduced Scale Traction Transformer (RSTT). The RSTT has the wound iron core and eight coils arranged in the \\
Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished
Chemical process plant safety, production specifications, environmental regulations, operational constraints, and plant economics are some of the main reasons driving an upward interest in research and development of more robust methods for process monitoring and control. Principal component analysis (PCA) has long been used in fault detection by extracting relevant information from multivariate chemical data. The recent success of wavelets
Manish Misra; H. Henry Yue; S. Joe Qin; Cheng Ling
This paper investigates the ability of a multilayer neural network to diagnose actuator faults in a Fisher-Rosemount 667 process control valve. A software package that comes with the valve is used to obtain experimental figures of merit related to the position response of the valve given a step command. The particular values of the dead time, peak time, percent overshoot,
This paper proposes a new interacting multiple model (IMM) filter for actuator fault detection. Since each individual filter of the IMM filter uses the combined information of the estimation values from all the operating filters, it can effectively estimate system parameter variations, thereby it can diagnose the actuator damage with an unknown magnitude. In this study, to diagnose the actuator
Models such as statecharts and fault trees become increasingly more available in electronic form as they progressively find more useful applications in the development of safety critical systems. As these models typically reduce in their utility after system certification, however, useful knowledge about the behaviour of the system remains unused in the operational phase of the system lifecycle. In this
The garbage crusher is one of the important parts in recoverable coal production line. To diagnose its faults during the working process, back propagation algorithm is used. However, it has some shortcomings, such as low precision solution, slow searching speed and easy convergence to the local minimum points. To overcome this problem, a novel method which integrates back propagation neural
As the health status of aeroplane structural components has direct impact on the flight, it is important to diagnose the health status of structural components timely. In this paper, acoustic emission technology is used to monitor the health status of the aeroplane structural component. The acoustic emission health information from the aeroplane structural component is analyzed and disposed. The fault
Condition monitoring is used for increasing machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring scheme is capable of providing warning and predicting the faults at early stages. The monitoring system obtains information about the machine in the form of primary data and through the use
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
The k-nearest neighbour (k-NN) rule is applied to diagnose the conditions of induction motors. The features are extracted from the time vibration signals while the optimal features are selected by a genetic algorithm based on a distance criterion. A weight value is assigned to each feature to help select the best quality features. To improve the classification performance of the
In this paper, from the Angle to predict 9ÿ take hydro- generating operation condition parameters (head, power) as input sample, take vibration, shaft waggling and pulse pressure, bearings temperature and so on parameter as output sample, create neural network prediction model. Train the established models, through comparing a different designs scheme, chose one smaller error model. Predict through the trained
Xinfeng Ge; Luoping Pan; Zhongxin Gao; Shu Tang; Dongdong Chu
The gearbox faultdiagnosis was developed for some decades. The current diagnosis techniques were mostly based on analyzing the shell vibration signals especially close to the bearing seat of gearbox. In order to utilize the spatial distribution information of fault signal, the near field acoustic holography (NAH) is employed for the condition monitoring and faultdiagnosis of the gearbox in this presentation. The distribution images of sound pressure on the surface of gearbox are reconstructed by NAH, and the feature extraction and pattern recognition can be made by image processing techniques. A gearbox is studied in a semi-anechoic chamber to verify the faultdiagnosis technique based on NAH. The pitting and partial broken tooth faults of gears are artificially made on one gear as the fault statuses, and the differences of acoustic images among normal and fault working states under the idling condition are analyzed. It can be found that the acoustic images of gearbox in the three different situations change regularly, and the main sound sources can be recognized from the acoustic images which also contain rich diagnosis information. After feature extraction of the acoustic images, the pattern reorganization technique is employed for diagnosis. The results indicate that this diagnosis procedure based on acoustic images is available and feasible for the gearbox faultdiagnosis.
Initial results obtained from an investigation using pattern recognition techniques for identifying fault modes in the Deep Space Network (DSN) 70 m antenna control loops are described. The overall background to the problem is described, the motivation and potential benefits of this approach are outlined. In particular, an experiment is described in which fault modes were introduced into a state-space simulation of the antenna control loops. By training a multilayer feed-forward neural network on the simulated sensor output, classification rates of over 95 percent were achieved with a false alarm rate of zero on unseen tests data. It concludes that although the neural classifier has certain practical limitations at present, it also has considerable potential for problems of this nature.
\\u000a Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While\\u000a many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been\\u000a a dearth of work exploring the details of how that might be accomplished.\\u000a \\u000a \\u000a In this paper, we describe how a component-oriented approach to software
The problem of constructing optimal and near-optimal test sequences to diagnose permanent faults in electronic and electromechanical systems is considered. The test sequencing problem is formulated as an optimal binary AND\\/OR decision tree construction problem, whose solution is known to be NP-complete. The approach used is based on integrated concepts from information theory and heuristic AND\\/OR graph search methods to
The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results. PMID:22163938
This paper proposes a new induction motor broken bar fault extent diagnostic approach under varying load conditions based on wavelet coefficients of stator current in a specific frequency band. In this paper, winding function approach (WFA) is used to develop a mathematical model to provide indication references for parameters under different load levels and different fault cases. It is shown that rise of number of broken bars and load levels increases amplitude of the particular side band components of the stator currents in faulty case. Stator current, rotor speed and torque are used to demonstrate the relationship between these parameters and broken rotor bar severity. An induction motor with 1, 2 and 3 broken bars and the motor with 3 broken bars in experiment at no-load, 50% and 100% load are investigated. A novel criterion is then developed to assess rotor fault severity based on the stator current and the rotor speed. Simulations and experimental results confirm the validity of the proposed approach.
The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.
Vibration-based condition monitoring (VCM) requires vibration measurement on each bearing pedestal using a number of vibration transducers and then signals processing for all the measured vibration data to identify fault(s), if any, in a rotating machine. Such a large vibration data set makes the diagnosis process complex generally for a large rotating machine supported through a number of bearing pedestals. Hence a new method is used to construct a single composite spectrum using all the measured vibration data set. This composite spectrum is expected to represent the dynamics of the complete machine assembly and can make faultdiagnosis process relatively easier and more straightforward. The paper presents the concept of the proposed composite spectrum which was applied to a laboratory test rig with different simulated faults; healthy and three faulty cases named misalignment, crack shaft, and shaft rub. A comparison between the composite spectrum with and without the coherence has been investigated for the simulated faults in the rig. It has been observed that the coherent composite spectrum provides much better diagnosis compared to the non-coherent composite spectrum.
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.
Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph. PMID:22999985
Saidi, L; Fnaiech, F; Henao, H; Capolino, G-A; Cirrincione, G
The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion
Karim Salahshoor; Mojtaba Kordestani; Majid S. Khoshro
Damage prediction in mechanical and structural systems is establishing a prominent role in modern engineering. Vibration based damage methods give ample flexibility to understand the extent of expected damages in the system. Measurement of vibration characteristics like natural frequencies and mode shapes, Fourier responses and transient responses can help in comprehending the present status of a system either by comparing with their baseline equivalents or by formulating residual functions and minimizing them. The minimization of residues is carried out using non-conventional optimization techniques like genetic algorithms. Genetic algorithms being a meta-heuristic method obtain global minimum values with implicitly defined constraints and objective. In all the residual functions considered in this paper, it is assumed that only the stiffness parameters are reduced individually in each element due to the damage. The amount of reduction in each element is an unknown parameter. The approach is attempted with a structural member like beam. Experimental analysis is carried out to test the natural frequencies and mode shapes of the damaged beams from finite element model considered. A cantilever beam with central slot of desired depth is selected and impact hammer analysis is performed to know the variation in modes when compared to undamaged counter part. Results are presented in the form of table and graphs.
This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well.
Fiorenzo Filippetti; Giovanni Franceschini; Carla Tassoni; Peter Vas
We present a BIST-based approach able to detect and accurately diagnose any single and most multiple faulty programmable logic blocks (PLBs) in field programmable gate arrays (FPGAs). For any faulty PLB, we also identify its internal faulty modules or modes of operation. This accurate diagnosis provides the basis for both failure analysis used for yield improvement and for any repair
? Abstract -- Recent advances in microelectronic circuit have enabled the application of process diagnostics to a variety of systems to improve performances and the reliability. Failure detection and isolation strategies monitor a system for degradations and if detected, classify the failure source. One of the most important methods for failure detection and diagnosis is the analysis of the variations
P. Dobra; M. Trusca; I. V. Sita; R. A. Munteanu; M. Munteanu
Testing circuits is a stage of the produc- tion process that is becoming more and more important when a new product is developed. Test and diagnosis techniques for digital circuits have been successfully de- veloped and automated. But, this is not yet the case for analog circuits. Even though there are plenty of methods proposed for diagnosing analog electronic circuits,
Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.
Advanced safety-critical control applications such as fly-by- wire and steer-by-wire are being realized as distributed systems comprising many embedded processors, sensors, and actuators interconnected via a communication medium. They have severe cost constraints but demand a high level of safety and performance. Recently, the authors in paper (Kandasamy et al., 2005) have developed a diagnosis approach for single rate steer-by-wire
The increasing complexity of process plants and their reliability have necessitated the development of more powerful methods for detecting and diagnosing process abnormalities. Among the underlying strategies, analytical redundancy and knowledge-based system techniques offer viable solutions. In this work, the authors consider the adaptive inclusion of analytical redundancy models (state and parameter estimation modules) in the diagnostic reasoning loop of a knowledge-based system. This helps overcome the difficulties associated with each category. The design method is a new layered knowledge base that houses compiled/qualitative knowledge in the high levels and process-general estimation knowledge in the low levels of a hierarchical knowledge structure. The compiled knowledge is used to narrow the diagnostic search space and provide an effective way of employing estimation modules. The estimation-based methods that resort to fundamental analysis provide the rationale for a qualitatively-guided reasoning process. The overall structure of the fault detection and isolation system based on the combined strategy is discussed focusing on the model-based redundancy methods which create the low levels of the hierarchical knowledge base. The system has been implemented using the condensate-feedwater subsystem of a coal-fired power plant. Due to the highly nonlinear and mixed-mode nature of the power plant dynamics, the modified extended Kalman filter is used in designing local detection filters.
Fathi, Z.; Ramirez, W.F. (Univ. of Colorado, Boulder (United States)); Korbicz, J. (Higher Coll. of Engineering, Gora (Poland))
As an adaptive time-frequency-energy representation analysis method, empirical mode decomposition (EMD) has the attractive feature of robustness in the presence of nonlinear and non-stationary data. It is evident that an appropriate definition of baseline (or called mean curve) of data plays a crucial role in EMD scheme. By defining several baselines, an adaptive data-driven analysis approach called generalized empirical mode decomposition (GEMD) is proposed in this paper. In the GEMD method, different baselines are firstly defined and separately subtracted from the original data, and then different pre-generated intrinsic mode functions (pre-GIMFs) are obtained. The GIMF component is defined as the optimal pre-GIMF among the obtained ones with the smallest rate of frequency bandwidth to center frequency. Next, the GIMF is subtracted from the original data and a residue is obtained, which is further regarded as the original data to repeat the sifting process until a constant or monotonic residue is derived. Since the GIMF in each frequency-band is the best among different pre-GIMFs derived from EMD and other EMD like methods, the GEMD results are best as well. Besides, a demodulating method called empirical envelope demodulation (EED) is introduced and employed to analyze the GIMFs in time-frequency domain. Furthermore, GEMD and EED are contrasted with the original Hilbert-Huang Transform (HHT) by analyzing simulation and rolling bearing vibration signals. The analysis results indicate that the proposed method consisting of GEMD and EED is superior to the original HHT at least in restraining the boundary effect, gaining a better frequency resolution and more accurate components and time frequency distribution.
In this paper, a vibration testing and health monitoring system based on an impulse response excited by laser ablation is proposed to detect bolted joint loosening. A high power Nd: YAG pulse laser is used to generate an ideal impulse on a structural surface which offers the potential to measure high frequency vibration responses on the structure. A health monitoring apparatus is developed with this vibration testing system and a damage detecting algorithm. The joint loosening can be estimated by detecting fluctuations of the high frequency response with the health monitoring system. Additionally, a finite element model of bolted joints is proposed by using three-dimensional elements with a pretension force applied and with contact between components taken into account to support the bolt loosening detection method. Frequency responses obtained from the finite element analysis and the experiments using the laser excitation are in good agreement. The bolt loosening can be detected and identified by introducing a damage index by statistical evaluations of the frequency response data using the Recognition-Taguchi method. The effectiveness of the present approach is verified by simulations and experimental results, which are able to detect and identify loose bolt positions in a six-bolt joint cantilever.
The calculations of stress distribution and mode frequencies of a turbine-generator mechanical system obtained by using the finite element method (FEM) are more accurate than those obtained by using the lumped parameter program. However, FEM is not capable of analyzing the disturbances coming from the generator. In this article, a modified dynamic program is used to simulate the shaft vibrations
Chun-Tang Chao; Tsung-Lin Fan Chiang; Chwen Chyn; Chi-Jo Wang
Among the topics discussed are: turbomachinery tip rubs and interactive casting resonances; the transverse vibrational characteristics of an externally damaged pipe and performance of vibration monitoring for the prevention of gas turbine airfoil failures. Consideration is also given to: velocity response analysis of a spherical roller bearing; vibration monitoring of large pumps via a remote satellite stations; dynamic edge strain prediction in stiffened honeycomb panels; and fault-diagnosis for turbo-machines by means of vibration monitoring. Additional topics discussed include: early detection and diagnosis of faults rolling element bearings; spectral analysis of damped vibration by means of a modified version of the Prony method and guidelines for forced vibration in machine tools for use in protective maintenance and analysis.
The dynamic response of a cracked Jeffcott rotor passing through the critical speed with constant acceleration is investigated analytically and numerically. The nonlinear equations of motion are derived and include a simple hinge model for small cracks and Mayes' modified funciton for deep cracks. The equations of motion are integrated in the rotating coordinate system. The angle between the crack centerline and the shaft vibaiton (whirl) vector is used to determine the clsoing and opening of the crack, allowing one to study the dynamic response with and without the rotor weight dominance. Vibration phase response is used as one of possible tools for detection the existence of cracks. The results of parametric studies of the effect of crack depth, unbalance eccentricity orientation with respect to crack, and the rotor acceleration on the rotor's response are presented.
Sawicki, Jerzy T.; Wu, Xi; Baaklini, George Y.; Gyekenyesi, Andrew L.
An improved accelerometer is introduced. It comprises a transducer responsive to vibration in machinery which produces an electrical signal related to the magnitude and frequency of the vibration; and a decoding circuit responsive to the transducer signal which produces a first fault signal to produce a second fault signal in which ground shift effects are nullified.
There are several applications where the motor is operating in continuous non-stationary operating conditions. Actuators in the aerospace and transportation industries are examples of this kind of operation. Diagnostics of faults in such applications is, however, challenging. A novel method using windowed Fourier ridges is proposed in this paper for the detection of rotor faults in BLDC motors operating under
Satish Rajagopalan; Thomas G. Habetler; Ronald G. Harley; José M. Aller; José A. Restrepo
For rolling bearing fault detection, it is expected that a desired time–frequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. As the best available time–frequency method so far, the wavelet transform still cannot fulfill the rolling bearing fault detection task very well since it has some inevitable deficiencies. The recent
This paper describes the use of the extended Park's vector approach (EPVA) for diagnosing the occurrence of stator winding faults in operating three-phase synchronous and asynchronous motors. The major theoretical principles related with the EPVA are presented and it is shown how stator winding faults can be effectively diagnosed by the use of this noninvasive approach. Experimental results, obtained in
The fault report is downloaded from the aircraft with ACARS for the line maintenance. This is widely attended currently. But the false alert often occurs in the fault report and drop the maintenance efficiency Aimed at the problem, the gray clustering filtering algorithms is set up based on gray cluster and filter theory .The algorithms can identify the false alert
The main contribution in the hereby presented paper is to investigate the fault detection capability of a motor current signature analysis by expanding its scope to include the gearbox, and not only the induction motor. Detecting bearing faults outside the induction motor through the stator current analysis represents an interesting alternative to traditional vibration analysis. Bearing faults cause changes in the stator current spectrum that can be used for faultdiagnosis purposes. A time-domain simulation of the drivetrain model is developed. The drivetrain system consists of a loaded single stage gearbox driven by a line-fed induction motor. Three typical bearing faults in the gearbox are addressed, i.e. defects in the outer raceway, the inner raceway, and the rolling element. The interaction with the fault is modelled by means of kinematical and mechanical relations. The fault region is modelled in order to achieve gradual loss and gain of contact. A bearing fault generates an additional torque component that varies at the specific bearing defect frequency. The presented dynamic electromagnetic dq-model of an induction motor is adjusted for diagnostic purpose and considers such torque variations. The bearing fault is detected as a phase modulation of the stator current sine wave at the expected bearing defect frequency.
Cibulka, Jaroslav; Ebbesen, Morten K.; Robbersmyr, Kjell G.
The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191
Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W
The authors consider the development of a knowledge base branch related to rotor electrical faults in squirrel cage machines, to be implemented in an expert system (ES), utilizing instantaneous values as input data. The knowledge base is organized in two levels: in the first level diagnostic indexes for the orientation of the ES inference engine toward the appropriate branch of
F. Filippetti; M. Martelli; G. Franceschini; C. Tassoni
Time-frequency analysis has been found to be effective in monitoring the transient or time- varying characteristics of machinery vibration signals, and therefore its use in machine condition monitoring is increasing. This paper proposes the application of time-frequency methods, which can provide more information about a signal in time and in frequency and gives a better representation of the signal than
The dc-dc converter is a critical component in a hybrid electric vehicle since it supplies power to an electronic control unit, as well as chassis electric components such as power windows, wipers, etc. In this paper, a low-cost diagnostic method for MOSFET faults in a zero- voltage-switching dc-dc converter is proposed. The proposed method utilizes the dc-link current patterns as
Sung Young Kim; Kwanghee Nam; Hong-Seok Song; Ho-Gi Kim
Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.
Recently, research has picked up a fervent pace in the area of faultdiagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However, with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus, other signals such as speed, torque, noise, vibration, etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. It is indeed evident that this area is vast in scope. Going by the present trend, human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems; to name a few. However, this cannot be achieved without detailed fault analysis and subsequent recognition of the fault pattern. Keeping this in mind, simulation studies of the broken bar and eccentricity related faults using MCSA have been taken up. Also, a common theoretical basis for the different types (static, dynamic and mixed) of eccentricity related faults which give different signatures for different pole and rotor bar combinations has been developed. This will be of great importance both from faultdiagnosis as well as sensorless drive applications' viewpoint. Finally, the insight gained from the analysis of eccentricity related faults leads to a novel detection technique of stator inter-turn faults by analyzing the frequency content of the transient line to line voltage, after the motor is switched off.
The objective of this on-going research is to develop a design methodology to increase the availability for offshore wind farms, by means of an intelligent maintenance system capable of responding to faults by reconfiguring the system or subsystems, without increasing service visits, complexity, or costs. The idea is to make use of the existing functional redundancies within the system and sub-systems to keep the wind turbine operational, even at a reduced capacity if necessary. Re-configuration is intended to be a built-in capability to be used as a repair strategy, based on these existing functionalities provided by the components. The possible solutions can range from using information from adjacent wind turbines, such as wind speed and direction, to setting up different operational modes, for instance re-wiring, re-connecting, changing parameters or control strategy. The methodology described in this paper is based on qualitative physics and consists of a faultdiagnosis system based on a model-based reasoner (MBR), and on a functional redundancy designer (FRD). Both design tools make use of a function-behaviour-state (FBS) model. A design methodology based on the re-configuration concept to achieve self-maintained wind turbines is an interesting and promising approach to reduce stoppage rate, failure events, maintenance visits, and to maintain energy output possibly at reduced rate until the next scheduled maintenance.
Echavarria, E.; Tomiyama, T.; van Bussel, G. J. W.
This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum,\\u000a in analysis of vibration signals and faultdiagnosis of roller bearings. The empirical mode decomposition (EMD), Hilbert-Huang\\u000a transform (HHT) and marginal spectrum are introduced. First, the vibration signals are separated into several intrinsic mode\\u000a functions (IMFs) by using EMD. Then the marginal
Diagnostic fault simulation can generate enormous amounts of data. The techniques used to manage this data can have signi cant e ect on the outcome of the faultdiagnosis procedure. We rst demonstrate that if information is removed from a fault dictionary, its ability to diagnose unmodeled faults may be severely curtailed even if dictionary quality metrics remain una ected;
In order to support dispatcher to deal with the fault of the power grid more fast and correctly, this paper developed an intelligent on-line fault handling expert system which consists of two modules of faultdiagnosis and fault restoration. The system makes full use of a variety of alarm, fault feature information to carry on real-time faultdiagnosis, and provides
Issues related to automating the process of fault management (faultdiagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing faultdiagnosis computer programs is outlined. Some existing work in the area of graph-based faultdiagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described.
Boyd, Mark A.; Iverson, David L.; Patterson-Hine, F. Ann
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 faultdiagnosis 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
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 faultdiagnosis 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.
Various features extracted from raw signals usually contain a large amount of redundant information which may impede the practical applications of machine condition monitoring and faultdiagnosis. Hence, as a solution, dimensionality reduction is vital for machine condition monitoring. This paper presents a new technique for dimensionality reduction called the discriminant diffusion maps analysis (DDMA), which is implemented by integrating a discriminant kernel scheme into the framework of the diffusion maps. The effectiveness and robustness of DDMA are verified in three different experiments, including a pneumatic pressure regulator experiment, a rolling element bearing test, and an artificial noisy nonlinear test system, with empirical comparisons with both the linear and nonlinear methods of dimensionality reduction, such as principle components analysis (PCA), independent components analysis (ICA), linear discriminant analysis (LDA), kernel PCA, self-organizing maps (SOM), ISOMAP, diffusion maps (DM), Laplacian eigenmaps (LE), locally linear embedding (LLE) analysis, Hessian-based LLE analysis, and local tangent space alignment analysis (LTSA). Results show that DDMA is capable of effectively representing the high-dimensional data in a lower dimensional space while retaining most useful information. In addition, the low-dimensional features generated by DDMA are much better than those generated by most of other state-of-the-art techniques in different situations.
Huang, Yixiang; Zha, Xuan F.; Lee, Jay; Liu, Chengliang
Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. Complex systems often use fault trees to analyze the faults. Faultdiagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space
There remains a need for objective and cost-effective approaches capable of diagnosing early-stage disease in point-of-care clinical settings. Given an increasingly ageing population resulting in a rising prevalence of chronic diseases, the need for screening to facilitate the personalising of therapies to prevent or slow down pathology development will increase. Such a tool needs to be robust but simple enough to be implemented into clinical practice. There is interest in extracting biomarkers from biofluids (e.g., plasma or serum); techniques based on vibrational spectroscopy provide an option. Sample preparation is minimal, techniques involved are relatively low-cost, and data frameworks are available. This review explores the evidence supporting the applicability of vibrational spectroscopy to generate spectral biomarkers of disease in biofluids. We extend the inter-disciplinary nature of this approach to hypothesise a microfluidic platform that could allow such measurements. With an appropriate lightsource, such engineering could revolutionize screening in the 21(st) century. PMID:24648213
Mitchell, Alana L; Gajjar, Ketan B; Theophilou, Georgios; Martin, Francis L; Martin-Hirsch, Pierre L
Considering accelerometer failure of single electromagnet suspension system of maglev train, a sensor-faultdiagnosis system based on unscented Kalman filter is designed. According to the results of fault detection and diagnosis on line, a improved PID regulator designed off line with tracking differentiators is adopted to tolerate the accelerometer failure. The simulation indicates that the faultdiagnosis system designed can
Multiple faultdiagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.
In the UK the use of the Stockholm Classification has been recommended by the Health and Safety Executive and by the Working Party of the Faculty of Occupational Medicine. The Stockholm Workshop 1994 did not recommend any changes to the existing classification but considered the variety of screening and diagnostic tests suitable for the staging of HAVS. Thirty one males claiming to be suffering from HAVS were interviewed and examined by each of the authors independently. The examination of each patient included detailed occupational and medical histories, standard physical examination with the additional tests of the rewarm time and aesthesiometry. Thermal neutral zone test (TNZ), vibrotactile thresholds and grip strength were also performed by McGeoch. All patients were classified by the Taylor/Pelmear and Stockholm Classifications. Both authors agreed that all the patients were suffering from HAVS. Agreement to within one stage was high for both the vascular and neurological elements. The additional neurological tests used by McGeoch appeared to result the raising of the neurological staging. The results indicate that independent interview plus objective tests performed by experienced physicians allow for reliable diagnosis and staging of claimants. Standardisation of tests is urgently required. PMID:9150985
ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) Syste...
This paper discusses multiple faultdiagnosis for SSM\\/PMAD using the Knowledge Management Design System as applied to the SSM\\/PMAD domain ( KNOMAD-SSM\\/PMAD). KNOMAD-SSM\\/PMAD provides a powerful facility for knowledge representation and re* soning which has been used to build the second generation of FRAMES (Fault Recovery and Management Expert System). FRAMES now handles the diagnosis of multiple faults as well
Multivalued influence-matrix (MVIM) method potential utility as theoretical basis of proposed automated monitoring systems detecting faults in helicopter gearboxes. Applied to recognize patterns in vibration measurements. Fault-recognition system required to operate continuously while helicopter airborne, analyzing measurements of vibrations for signs of trouble to provide real-time warning of any dangerous or potentially dangerous fault like cracked case or fractured gear tooth. System also required not to give false alarms to prevent unnecessary emergency landings.
A fault finder for locating faults along a high voltage electrical transmission line. Real time monitoring of background noise and improved filtering of input signals is used to identify the occurrence of a fault. A fault is detected at both a master and remote unit spaced along the line. A master clock synchronizes operation of a similar clock at the remote unit. Both units include modulator and demodulator circuits for transmission of clock signals and data. All data is received at the master unit for processing to determine an accurate fault distance calculation.
Bunch, Richard H. (1614 NW. 106th St., Vancouver, WA 98665) [1614 NW. 106th St., Vancouver, WA 98665
Successful failure analysis requires accurate faultdiagnosis. This paper presents a method for diagnosing bridging faults that improves on previous methods. The new method uses single stuck-at fault signatures, produces accurate and precise diagnoses, and takes into account imperfect fault modeling; it accomplishes this by introducing the concepts of match restriction, match requirement, and match ranking
David B. Lavo; Brian Chess; Tracy Larrabee; F. Joel Ferguson
This paper uses results of field studies from multiple domains to explore the cognitive activities involved in dynamic fault management. Faultdiagnosis has a different character in dynamic fault management situations as compared to troubleshooting a broken device that has been removed from service. In fault management there is some underlying process (an engineered or physiological process that will be
net Abstract: With consideration of some practical difficulties of fault knowledge sharing and application in current aviation maintenance, the expression modeling and integration application of deep and shallow aircraft fault knowledge are studied. According to widely used knowledge management technology such as case-based reasoning and diagnostics expert system, ontology-based fault case knowledge model and model based faultdiagnosis knowledge model
Oil debris sensors are designed for monitoring machine component conditions by detecting oil debris in the circulating oil lines. However, these sensors are not only sensitive to metallic particles, but are susceptible to machinery vibration as well. The vibration-induced signal has thus far been treated as interference and is accordingly removed to better reveal the particle signature. As the vibration signal also contains important information on machine health, which can be used to detect not only the machine component faults but also machine structural malfunctions, we propose a joint integral and wavelet transform approach to separate the vibration and particle signals to make the oil debris sensor multi-functional. The recovered vibration signal is then used to detect faults that cannot be revealed by examining oil debris content. Our experimental results have shown that the separated vibration signal is, in general, consistent with the vibration velocity and hence can be used as an auxiliary vibration monitoring tool.
This paper proposes a fault-tolerant matrix converter with reconfigurable structure and modified switch control schemes, along with a faultdiagnosis 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
Based on the complicated relationships between the symptoms and the defects of hydro-generator units, An approach to diagnosing the faults in hydro-generator units via a neural networks combined with Genetic algorithm (GA) and nonlinear principal analysis neural network (NLPCA NN) is presented in this paper. At first, GA optimizes both the structure and the connection of the NLPCA NN. The
Based on the complicated relationships between the symptoms and the defects of hydro-generator units, An approach to diagnosing the faults in hydro-generator units via a neural networks combined with genetic algorithm (GA) and nonlinear principal analysis neural network (NLPCA NN) is presented in this paper. At first, GA optimizes both the structure and the connection of the NLPCA NN. The
The objective of this on-going research is to develop a design methodology to increase the availability for offshore wind farms, by means of an intelligent maintenance system capable of responding to faults by reconfiguring the system or subsystems, without increasing service visits, complexity, or costs. The idea is to make use of the existing functional redundancies within the system and
Space and Earth observation programs demand stringent guarantees ensuring smooth and reliable operations of space vehicles and satellites. Due to unforeseen circumstances and naturally occurring faults, it is desired that a fault-diagnosis system be capable of detecting, isolating, identifying, or classifying faults in the system. Unfortunately, none of the existing fault-diagnosis methodologies alone can meet all the requirements of an
Guidelines of the methods and applications used in vibration technology at the MSFC are presented. The purpose of the guidelines is to provide a practical tool for coordination and understanding between industry and government groups concerned with vibration of systems and equipments. Topics covered include measuring, reducing, analyzing, and methods for obtaining simulated environments and formulating vibration specifications. Methods for vibration and shock testing, theoretical aspects of data processing, vibration response analysis, and techniques of designing for vibration are also presented.
Fault simulation is critical in test development and faultdiagnosis for mixed-signal systems. In this paper we present a novel concurrent fault simulation method for analog\\/digital mixed-signal circuits. A prototype mixed-signal fault simulation system is developed based on two existing digital and analog fault simulators. The key issue of fault list propagation between digital and analog fault simulators is addressed.
A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.
Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery faultdiagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches.
A training program is described which provides, during faultdiagnosis, additional information about the relationship between the remaining faults and the available indicators. An interactive computer program developed for this purpose and the first results of experimental training are described. (Author)
We consider the problem of sequencing tests to isolate multiple faults in redundant (fault-tolerant) systems with minimum expected testing cost (time). It can be shown that single faults and minimal faults, i.e., minimum number of failures with a failure signature different from the union of failure signatures of individual failures, together with their failure signatures, constitute the necessary information for faultdiagnosis in redundant systems. In this paper, we develop an algorithm to find all the minimal faults and their failure signatures. Then, we extend the Sure diagnostic strategies  of our previous work to diagnose multiple faults in redundant systems. The proposed algorithms and strategies are illustrated using several examples.
Shakeri, M.; Pattipati, Krishna R.; Raghavan, V.; Patterson-Hine, Ann; Iverson, David L.
Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.
Faultdiagnosis typically assumes a sufficiently large fault signature and enough time for a reliable decision to be reached. However, for a class of safety critical faults on commercial aircraft engines, prompt detection is paramount within a millisecond range to allow accommodation to avert undesired engine behavior. At the same time, false positives must be avoided to prevent inappropriate control
Faultdiagnosis of large scale wind turbine systems has received much attention in the recent years. Effective fault prediction would allow for scheduled maintenance and for avoiding catastrophic failures. Thus the availability of wind turbines can be enhanced and the cost for maintenance can be reduced. In this paper, we consider the sensor and actuator fault detection issue for large
Faultdiagnosis of large scale wind turbine systems has received much attention in the recent years. Effective fault prediction would allow for scheduled maintenance and for avoiding catastrophic failures. Thus the availability of wind turbines can be enhanced and the cost for maintenance can be reduced. In this paper, we consider the sensor and actuator fault detection issue for large
This article emphasizes simulation-based sampling techniques for estimating fault coverage that use small fault samples. Although random testing is considered to be the primary area of application of the technique it is also suitable for estimating the fault coverage of nonrandom tests based on specific fault models. Especially for fault coverages exceeding 95%, it is shown that a precise estimate
This site, by Andrew Davidhazy at the Rochester Institute of Technology, describes how to make interesting and artistic photographs of a vibrating string. Davidhazy explains how the string is vibrated, how the string is lit, and even the exposure time and the effect it has on the resulting image. Four images of the vibrating string are included.
A major problem in the qualification of integrated circuit cells and in the development of adequate tests for the circuits is to lack of information on the nature and density of fault models. Some of this information is being obtained from the test structures. In particular, the Pinhole Array Capacitor is providing values for the resistance of gate oxide shorts, and the Addressable Inverter Matrix is providing values for parameter distributions such as noise margins. Another CMOS fault mode, that of the open-gated transistor, is examined and the state of the transistors assessed. Preliminary results are described for a number of open-gated structures such as transistors, inverters, and NAND gates. Resistor faults are applied to various CMOS gates and the time responses are noted. The critical value for the resistive short to upset the gate response was determined.
Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.
Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.
Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.
Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Clancy, Daniel (Technical Monitor)
Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.
Oza, Nikunj; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Koga, Dennis (Technical Monitor)
Fault localization is a process of isolating faults responsible for the observable malfunctioning of the managed system. Until recently, fault localization efforts concentrated mostly on diag- nosing faults related to the availability of network resources in the lowest layers of the protocol stack. Modern enterprise envi- ronments require that faultdiagnosis be performed in integrated fashion in multiple layers of
Using first-principles density-functional theory we study the surface structure and stacking-fault energy of the Ru(0001) surface. In particular, we will present the relaxed structure of the Ru(0001) surface with and without stacking faults. The fault energy and relaxation are compared with bulk Ru values. Calculations are performed using our recently developed scalable band-distributed parallel molecular dynamics code. Selected vibrational modes are also reported.
In this paper a new deconvolution method is presented for the detection of gear and bearing faults from vibration data. The proposed maximum correlated Kurtosis deconvolution method takes advantage of the periodic nature of the faults as well as the impulse-like vibration behaviour associated with most types of faults. The results are compared to the standard minimum entropy deconvolution method on both simulated and experimental data. The experimental data is from a gearbox with gear chip fault, and the results are compared between healthy and faulty vibrations. The results indicate that the proposed maximum correlated Kurtosis deconvolution method performs considerably better than the traditional minimum entropy deconvolution method, and often performs several times better at fault detection. In addition to this improved performance, deconvolution of separate fault periods is possible; allowing for concurrent fault detection. Finally, an online implementation is proposed and shown to perform well and be computationally achievable on a personal computer.
Vibrations in rotating machinery are produced by a combination of periodic and random processes due to the machine's rotation cycle and interaction with the real world. The combination of such components can give rise to signals which have periodically time-varying ensemble statistics and are best considered as cyclostationary. In this paper, second-order cyclic statistical methods are described and several applications of these to machine vibration analysis are introduced. The second-order techniques are compared with traditional (purely stationary) spectral analysis and synchronous averaging (first-order cyclic analysis). This comparison is made for data collected from a rotating machine subjected to bearing faults and the applications are demonstrated.
Vibration-based damage detection and identification in a laboratory cable-stayed bridge model is addressed under inherent, environmental, and experimental uncertainties. The problem is challenging as conventional stochastic methods face difficulties due to uncertainty underestimation. A novel method is formulated based on identified Random Coefficient Pooled ARX (RCP-ARX) representations of the dynamics and statistical hypothesis testing. The method benefits from the ability of RCP models in properly capturing uncertainty. Its effectiveness is demonstrated via a high number of experiments under a variety of damage scenarios.
Michaelides, P. G.; Apostolellis, P. G.; Fassois, S. D.
Vibration signals are often used for faultdiagnosis in mechanical systems because they are containing dynamic information of mechanical elements. Vibration signals from a gearbox are usually noisy and the signal-to-noise ratio (SNR) is so low that feature extraction of signal components is very difficult, especially in practical situations. One of the solutions to this problem is applying signal time-averaging techniques in time domain for signal denoising, but using this method is only possible when gearbox input shaft rotation is constant or synchronous. In this paper, a new noise canceling method, based on time-averaging method for asynchronous input, is developed, and then complex Morlet wavelet is implemented for feature extraction and diagnosis of different kind of local gear damages. The complex Morlet wavelet, used in this work, is adaptive because the parameters are not fixed. The proposed method is implemented on a simulated signal and real test rig of Yahama motorcycle gearbox. Both simulation and experimental results have proved that the method is very promising in analysis of the signal and faultdiagnosis of gearbox.
Jafarizadeh, M. A.; Hassannejad, R.; Ettefagh, M. M.; Chitsaz, S.
By homing in on the distribution patterns of electrons around an atom, a team of scientists team with Berkeley Lab's Molecular Foundry showed how certain vibrations from benzene thiol cause electrical charge to "slosh" onto a gold surface (left), while others do not (right). The vibrations that cause this "sloshing" behavior yield a stronger SERS signal.
Viewgraphs on vibration isolation are presented. Techniques to control and isolate centrifuge disturbances were identified. Topics covered include: disturbance sources in the microgravity environment; microgravity assessment criteria; life sciences centrifuge; flight support equipment for launch; active vibration isolation system; active balancing system; and fuzzy logic control.