Enhanced Fault Detection Research Articles

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

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL1) values.

Diagnosability improvement of dynamic clustering through automatic learning of discrete event models

This paper deals with the problem of improving data-based diagnosis of continuous systems taking advantage of the system control information represented as discrete event dynamics. The approach starts from dynamic clustering results and, combining the information about operational modes, automatically generates a discrete event system that improves clustering results interpretability for decision-making purposes and enhances fault detection capabilities by the inclusion of event related dynamics. The generated timed discrete event system is adaptive thanks to the dynamic nature of the clusterer from which it was learned, namely DyClee. The timed discrete event system brings valuable temporal information to distinguish behaviors that are non-diagnosable based solely on the clustering itself.

Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes

Kernel principal component analysis (KPCA) based fault detection method, whose statistical model only utilizes normal operating data and ignores available prior fault information, may not provide the best fault detection performance for nonlinear process monitoring. In order to exploit available prior fault data to enhance fault detection performance, a fault discriminant enhanced KPCA (FDKPCA) method is proposed, which simultaneously monitors two types of data features, nonlinear kernel principal components (KPCs) and fault discriminant components (FDCs). More specifically, based on the normal operating data, KPCs are extracted by usual KPCA modeling, while with the normal operating data and prior fault data, FDCs are obtained by the kernel local-nonlocal preserving discriminant analysis (KLNPDA). Monitoring statistics are constructed for both the KPCA and KLNPDA sub-models. Moreover, Bayesian inference is employed to transform the corresponding monitoring statistics into fault probabilities, and the overall probability-based monitoring statistics are constructed by weighting the results of the two sub-models, which provides more effective on-line fault detection capability. Two case studies involving a simulated nonlinear system and a continuous stirred tank reactor demonstrate the superior fault detection performance of the proposed KLNPDA scheme over the traditional KPCA method.

Coverage‐based regression test case selection, minimization and prioritization: a case study on an industrial system

SummaryThis paper presents a case study of coverage‐based regression testing techniques on a real world industrial system with real regression faults. The study evaluates four common prioritization techniques, a test selection technique, a test suite minimization technique and a hybrid approach that combines selection and minimization. The study also examines the effects of using various coverage criteria on the effectiveness of the studied approaches. The results show that prioritization techniques that are based on additional coverage with finer grained coverage criteria perform significantly better in fault detection rates. The study also reveals that using modification information in prioritization techniques does not significantly enhance fault detection rates. The results show that test selection does not provide significant savings in execution cost (<2%), which might be attributed to the nature of the changes made to the system. Test suite minimization using finer grained coverage criteria could provide significant savings in execution cost (79.5%) while maintaining a fault detection capability level above 70%, thus representing a possible trade‐off. The hybrid technique did not provide a significant improvement over traditional minimization techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Fault Detection and Isolation using an Adaptive Unscented Kalman Filter

Abstract An enhanced Fault Detection and Isolation (FDI) technique based on 't' statistical test is presented here employing a residual based R-Adaptive Unscented Kalman Filter (AUKF). Although the use of AUKF is common for estimation problem, employing AUKF for FDI duty is quite rare. Adaptive Unscented Kalman filtering framework is chosen here due to its derivative free calculations in the algorithm and reportedly better accuracy. Monitoring the 't' statistic of the innovation sequence, measurement faults are detected and corresponding alarm signals are generated here. The presented 't' statistic based FDI technique comprising of consistency checking and abruptness checking is straight forward, demands less computational burden and minimizes false alarms. The superiority of the proposed AUKF based FDI method compared to non adaptive, nonlinear filtering based FDI techniques is demonstrated here by an extensive simulation study executed on a nonlinear LEO satellite planar model. The use of AUKF has provided faster convergence speed and is also found to be advantageous for the situations where the measurement noise statistics are unknown.

Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on differents metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in Matlab with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.

Seismic data conditioning and neural network-based attribute selection for enhanced fault detection

In this study of the Dorood oil field, offshore Iran, 3D seismic data were utilized to identify a complex fault pattern in the highly faulted and fractured Fahliyan Formation. To enhance data quality and improve attribute accuracy and detection power, a steering cube was first computed based on a sliding 3D Fourier analysis technique, using the concept of directivity. The steering cube, which contains dip and azimuth information for each trace, was utilized for calculation of dip-steered filters and attributes. We applied the dip-steered median filter to remove random noise and to enhance laterally continuous seismic events by filtering along the structural dip. Several fault identification attributes, such as dip, curvature, coherency and similarity, and a meta-attribute of a ridge enhancement filter, were extracted from dip-steered, noise-attenuated data. A supervised, fully connected multi-layer perceptron neural network was constructed to select and combine the most sensitive fault attributes. The neural network, trained at identified fault and non-fault locations, was applied to the whole seismic volume to generate a cube of fault probability. Interpretations of major faults and fractures were integrated with geological, reservoir engineering and production data to highlight the role of these heterogeneities on dynamic reservoir properties. Faults and fractures in the Fahliyan reservoir were identified in general to have the effect of decreasing reservoir permeability. While most of the faults recognized have locally a sealing capacity effect, when the fault throws are large enough to bring into contact the Manifa and Yamama reservoirs, they act as a conduit for fluid communication and, wherever a well crosses a major fault, early water breakthrough is observable.

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

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

Fault Detection Enhancement in Rolling Element Bearings Using the Minimum Entropy Deconvolution

Minimum Entropy Deconvolution (MED) has been recently introduced to the machine condition mon- itoring field to enhance fault detection in rolling element bearings and gears. MED proved to be an excellent aid to the extraction of these impulses and diagnosing their origin, i.e. the defective component of the bearing. In this paper, MED is revisited and re-introduced with further insights into its application to fault detection and diagnosis in rolling element bearings. The MED parameter selection as well as its combination with pre-whitening is discussed. Two main cases are presented to illustrate the benefits of the MED technique. The first one was taken from a fan bladed test rig. The second case was taken from a wind turbine with an inner race fault. The usage of the MED technique has shown a strong enhancement for both fault detection and diagnosis. The paper contributes to the knowledge of fault detection of rolling element bearings through providing an insight into the usage of MED in rolling element bearings diag- nostic. This provides a guide for the user to select optimum parameters for the MED filter and illustrates these on new interesting cases both from a lab environment and an actual case.

Combined oscillation and I DDQ testing of a CMOS amplifier circuit

A simple test methodology is presented, combining oscillation and quiescent power supply current (I DDQ) testing for detecting bridging and open faults in a CMOS amplifier circuit formed during fabrication. The testing is performed at room temperature (300 K) and also at liquid-nitrogen temperature (77 K) to enhance fault detection. An on-chip built-in current sensor has been integrated to monitor I DDQ of the circuit under test. A simple fault-injection technique has been used for simulating manufacturing defects. The amplifier was designed for operation at ±2.5 V in a standard 1.5 μm n-well CMOS process. It is shown that all faults can be detected through a combined oscillation and I DDQ testing method. Theoretical results obtained from SPICE simulations are in close agreement with the corresponding experimental results on fabricated devices.

Modeling and experimental verification of high impedance arcing fault in medium voltage networks

A high impedance arcing fault due to a leaning tree in medium voltage (MV) networks is modeled and experimentally verified. The fault is represented in two parts; an arc model and a high resistance. The arc is generated by a leaning tree towards the network conductor and the tree resistance limits the fault current. The arcing element is dynamically simulated using thermal equations. The arc model parameters and resistance values are determined using the experimental results. The fault behavior is simulated by the ATP/EMTP program, in which the arc model is realized using the universal arc representation. The experimental results have validated the system transient model. Discrete wavelet transform is used to extract the fault features and therefore localize the fault events. It is found that arc reignitions enhance fault detection when discrete wavelet transform is utilized

Principal‐component analysis of multiscale data for process monitoring and fault diagnosis

AbstractAn approach is presented to multivariate statistical process control (MSPC) for process monitoring and fault diagnosis based on principal‐component analysis (PCA) models of multiscale data. Process measurements, representing the cumulative effects of many underlying process phenomena, are decomposed by applying multiresolution analysis (MRA) by wavelet transformations. The decomposed process measurements are rearranged according to their scales, and PCA is applied to these multiscale data to capture process variable correlations occurring at different scales. Choosing an orthonormal mother wavelet allows each principal component to be a function of the process variables at only one scale level. The proposed method is discussed in the context of other multiscale approaches, and illustrated in detail using simulated data from a continuous stirred tank reactor (CSTR) system. A major contribution of the paper is to extend fault isolation methods based on contribution plots to multiscale approaches. In particular, once a fault is detected, the contributions of the variations at each scale to the fault are computed. These scale contributions can be very helpful in isolating faults that occur mainly at a single scale. For those scales having large contributions to the fault, one can further compute the variable contributions to those scales, thereby making fault diagnosis much easier. A comparison study is done through Monte Carlo simulation. The proposed method can enhance fault detection and isolation (FDI) performance when the frequency content of a fault effect is confined to a narrow‐frequency band. However, when the fault frequency content is not localized, the multiscale approaches perform very comparably to the standard single‐scale approaches, and offer no real advantage. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2891–2903, 2004

Induction motor fault detection using vibration and stator current methods

Induction motors are widely used in industry as prime electromechanical energy conversion devices. Consequently, the condition monitoring and fault diagnosis of induction motors have received significant attention recently and become an integrated part of various maintenance strategies (for example preventive, condition-based and reliability-based maintenance). This paper presents a comparison of results of induction motor broken rotor bar fault detection using vibration and stator current methods. A broken rotor bar fault was induced into in a variable speed three-phase induction motor. Both the vibration and stator current signatures were acquired under different speed and load conditions. The fault detection sensitivities of vibration and stator current methods are evaluated. This paper also addresses the relationship between current and vibration signatures under normal and faulty motor conditions using correlation and frequency response methods. This relationship is desirable in order to determine the fault signature transmission mechanism and to exclude the irrelevant vibration sources so as to enhance fault detection accuracy. The relationship, studied during steady-state operation and start-up, enabled the identification of the vibrations from other sources.

Extensions to the C programming language for enhanced fault detection

AbstractThe acceptance of the C programming language by academia and industry is partially responsible for the ‘software crisis’. The simple, trusting semantics of C mask many common faults, such as range violations, which would be detected and reported at run‐time by programs coded in a robust language such as Ada.Ada is a registered trademark of the U.S. Government (Ada Joint Program Office) This needlessly complicates the debugging of C programs. Although the assert macro lets programmers add run‐time consistency checks to their programs, the number of instantiations of this macro needed to make a C program robust makes it highly unlikely that any programmer could correctly perform the task. We make some unobtrusive extensions to the C language which support the efficient detection of faults at run‐time without reducing the readability of the source code. Examples of the extensions are automatic checking of error codes returned by library routines, constrained subtypes and detection of references to uninitialized and/or non‐existent array elements.

A Method of Fault Signature Extraction for Improved Diagnosis

Efficient extraction of fault signatures from sensory data is a major concern in fault diagnosis. This paper introduces a self-tuning method of fault signature extraction that enhances fault detection, minimizes false alarms, improves diagnosability, and reduces fault signature variability. The proposed method uses a Flagging Unit to convert the processed measurements to binary vectors, and utilizes nonparametric pattern classification techniques to estimate the fault signatures. The performance of the Flagging Unit, which relies on its adaptation algorithms to optimize its performance based upon a sample batch of measurement-fault vectors, is demonstrated in simulation.

Federated square root filter for decentralized parallel processors

An efficient, federated Kalman filter is developed for use in distributed multisensor systems. The design accommodates sensor-dedicated local filters, some of which use data from a common reference subsystem. The local filters run in parallel, and provide sensor data compression via prefiltering. The master filter runs at a selectable reduced rate, fusing local filter outputs via efficient square root algorithms. Common local process noise correlations are handled by use of a conservative matrix upper bound. The federated filter yields estimates that are globally optimal or conservatively suboptimal, depending upon the master filter processing rate. This design achieves a major improvement in throughput (speed), is well suited to real-time system implementation, and enhances fault detection, isolation, and recovery capability. >