Showing papers on "Fault detection and isolation published in 2021"
TL;DR: An adaptive event-triggered scheme for S-MJSs that is more effective than conventional event- triggered strategy for decreasing network transmission information is developed and a new adaptive law is designed that can dynamically adjust the event-Triggered threshold is designed.
Abstract: This paper examines the adaptive event-triggered fault detection problem of semi-Markovian jump systems (S-MJSs) with output quantization. First, we develop an adaptive event-triggered scheme for S-MJSs that is more effective than conventional event-triggered strategy for decreasing network transmission information. Meanwhile, we design a new adaptive law that can dynamically adjust the event-triggered threshold. Second, we consider output signal quantization and transmission delay in the proposed fault detection scheme. Moreover, we establish novel sufficient conditions for the stochastic stability in the proposed fault detection scheme with an $H_{\infty }$ performance with the help of linear matrix inequalities (LMIs). Finally, we provide simulation results to demonstrate the usefulness of the developed theoretical results.
183 citations
TL;DR: Based on Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured and the simulation study on a quarter-car suspension system verifies that the design can detect faults without error alarms.
Abstract: Based on the interval type-2 fuzzy (IT2F) approach, this paper investigates the fault detection filter design problem for a class of nonhomogeneous higher-level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. The simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.
136 citations
TL;DR: Fault detection of networked dynamical systems (NDSs) has attracted ever-increasing attention since it can maintain high-quality products as well as operational safety as mentioned in this paper. And considering the utilisation...
Abstract: Fault detection of networked dynamical systems (NDSs) has attracted ever-increasing attention since it can maintain high-quality products as well as operational safety. Considering the utilisation ...
131 citations
TL;DR: A new method is proposed for fault detection of wind turbine, in which the convolutional neural network cascades to the long and short term memory network (LSTM) based on attention mechanism, which verifies the effectiveness of the proposed method.
130 citations
TL;DR: A new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for K PI-related process monitoring and performs general singular value decomposition on the calculable loadings based on the kernel matrix.
Abstract: Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method.
114 citations
TL;DR: A systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted and the main trends, challenges and prospects are presented.
Abstract: The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.
112 citations
TL;DR: Novel approaches are proposed to derive the parity vectors that construct optimized residual generators for linear and nonlinear systems and can significantly improve the sensitivity to small faults, and thus, the fault detection rate is improved compared with the traditional nonoptimized approach.
Abstract: In the conventional approaches to the design of fault diagnosis systems, little effort is usually paid to the selection of the parity vectors. As a result, the systems’ performance can be significantly affected. In this article, novel approaches are proposed to derive the parity vectors that construct optimized residual generators for linear and nonlinear systems. Based on the analysis on the parity space dimension, a novel parameterization of all parity relation-based residual generators is proposed. An iterative procedure that guarantees minimal regression error is then employed in the search for the optimal parameters. Considering that the traditional parity relation-based approaches are only suitable for linear systems, in this work, the proposed approach is also generalized to deal with strong nonlinearities, with the aid of data-driven Hammerstein function estimation. Furthermore, optimized residual generation algorithms are summarized for offline design and online implementation, the performance of which is evaluated thoroughly with a three-tank system, a numerical nonlinear example, as well as a case study on an industrial hot rolling mill process. Results show that residuals generated by the proposed approaches can significantly improve the sensitivity to small faults, and thus, the fault detection rate is improved compared with the traditional nonoptimized approach.
109 citations
TL;DR: A novel deep transfer learning model is constructed based on an adversarial learning strategy, which can effectively separate multiple unlabeled new fault types from labeled known ones and suggest that it is promising to address fault diagnosis transfer tasks in which the multiple new faults occur in the target domain.
Abstract: Recently, deep transfer learning based intelligent fault diagnosis has been widely investigated, and the tasks that source and target domains share the same fault categories have been well addressed. However, due to complexity and uncertainty of mechanical equipment, unknown new faults may occur unexpectedly. This problem has received less attention in the current research, which seriously limited the application of deep transfer learning. In this article, a two-stage transfer adversarial network is proposed for multiple new faults detection of rotating machinery. First, a novel deep transfer learning model is constructed based on an adversarial learning strategy, which can effectively separate multiple unlabeled new fault types from labeled known ones. Second, an unsupervised convolutional autoencoders model with silhouette coefficient is built to recognize the number of new fault types. Extensive experiments on a gearbox dataset validate the practicability of the proposed scheme. The results suggest that it is promising to address fault diagnosis transfer tasks in which the multiple new faults occur in the target domain, which greatly expand the application of deep transfer learning.
106 citations
TL;DR: A general machine-learning-based architecture for sensor validation built upon a series of neural-network estimators and a classifier is proposed, which aims at detecting anomalies in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable digital twins.
Abstract: Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins. However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable digital twins. More specifically, we propose a general machine-learning-based architecture for sensor validation built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behaviour and replace the isolated faulty sensor within the system), whereas the classifier is used for detection and isolation tasks. A comprehensive statistical analysis on three different real-world data-sets is conducted and the performance of the proposed architecture validated under hard and soft synthetically-generated faults.
106 citations
TL;DR: An emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this article for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication.
Abstract: Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by insignificant information and string noise present in the infrared thermal image. To address this issue, an emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this article for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication. The dimensionality of the extracted features was reduced using principal component analysis (PCA) and thereafter the selected features were ranked in the order of most relevant features using the Mahalanobis distance (MD) method to achieve the optimal feature set. Finally these selected features have been passed to the complex decision tree (CDT), linear discriminant analysis (LDA) and support vector machine (SVM) for fault classification and performance evaluation. The classification results reveal that the SVM outperformed CDT and LDA. The proposed strategy can be used for self-adaptive recognition of bearing faults in IM which helps to avoid the unplanned and unwanted system shutdowns due to the bearing failure.
104 citations
TL;DR: In this paper, a fuzzy asynchronous fault detection filter (FAFDF) was proposed for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme.
Abstract: This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.
TL;DR: In this paper, the authors proposed a new feature engineering model which combines Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT) and statistical features of raw signals.
TL;DR: An event-triggering communication scheme is proposed to enhance the efficiency of network resource utilization while counteracting the impact of aperiodic DoS attacks on the USV control system performance and simulation results verify the effectiveness of the proposed co-design method.
Abstract: This paper addresses the co-design problem of a fault detection filter and controller for a networked-based unmanned surface vehicle (USV) system subject to communication delays, external disturbance, faults, and aperiodic denial-of-service (DoS) jamming attacks. First, an event-triggering communication scheme is proposed to enhance the efficiency of network resource utilization while counteracting the impact of aperiodic DoS attacks on the USV control system performance. Second, an event-based switched USV control system is presented to account for the simultaneous presence of communication delays, disturbance, faults, and DoS jamming attacks. Third, by using the piecewise Lyapunov functional (PLF) approach, criteria for exponential stability analysis and co-design of a desired observer-based fault detection filter and an event-triggered controller are derived and expressed in terms of linear matrix inequalities (LMIs). Finally, the simulation results verify the effectiveness of the proposed co-design method. The results show that this method not only ensures the safe and stable operation of the USV but also reduces the amount of data transmissions.
TL;DR: The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection, and this method also has practical application value for engineering rotating machinery.
Abstract: In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.
TL;DR: The state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines is reviewed, including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach.
Abstract: In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or on high frequency reflections from impedance discontinuities. Of these high frequency test methods, we review phasor, travelling wave and reflectometry methods. The reflectometry methods summarized include time domain reflectometry (TDR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR), orthogonal multi-tone reflectometry (OMTDR), noise domain reflectometry (NDR), chaos time domain reflectometry (CTDR), binary time domain reflectometry (BTDR), frequency domain reflectometry (FDR), multicarrier reflectometry (MCR), and time-frequency domain reflectometry (TFDR). All of these reflectometry methods result in complex data sets (reflectometry signatures) that are the result of reflections in the time/frequency/spatial domains. Automated analysis techniques are needed to detect, locate, and diagnose the fault including genetic algorithm (GA), neural networks (NN), particle swarm optimization, teaching–learning-based optimization, backtracking search optimization, inverse scattering, and iterative approaches. We summarize several of these methods including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach. We also discuss the issue of soft faults (small impedance changes) and methods to augment their signatures, and the challenges of branched networks. We also suggest directions for future research and development.
TL;DR: A data-driven methodology for fault detection and diagnosis by integrating the principal component analysis (PCA) with the Bayesian network (BN) and a combination of vine copula and Bayes’ theorem suggests that the proposed framework provides superior performance.
TL;DR: This work proposes a distributed sensor-fault detection and diagnosis system based on machine learning algorithms where the fault detection block is implemented in the sensor in order to achieve output immediately after data collection and shows the efficiency of the proposed fuzzy learning-based model over classic neuro-fuzzy and non- fuzzy learning approaches.
TL;DR: In this article, condition-based maintenance and fault diagnosis of rotating machinery (RM) has a vital role in the modern industrial world, however, the remaining useful life (RUL) of machiner...
Abstract: Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machiner...
TL;DR: A review of the machine learning algorithm applications in fault detection in induction motors and the future prospects and challenges for an efficient machine learning based fault detection systems are presented.
Abstract: Fault detection prior to their occurrence or complete shut-down in induction motor is essential for the industries. The fault detection based on condition monitoring techniques and application of machine learning have tremendous potential. The power of machine learning can be harnessed and optimally used for fault detection. The faults especially in induction motor needs to be addressed at a proper time for avoiding losses. Machine learning algorithm applications in the domain of fault detection provides a reliable and effective solution for preventive maintenance. This paper presents a review of the machine learning algorithm applications in fault detection in induction motors. This paper also presents the future prospects and challenges for an efficient machine learning based fault detection systems.
TL;DR: In this paper, the authors present a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multiantel network configurations.
Abstract: High voltage direct current (HVDC) transmission is an economical option for transmitting a large amount of power over long distances. Initially, HVDC was developed using thyristor-based current source converters (CSC). With the development of semiconductor devices, a voltage source converter (VSC)-based HVDC system was introduced, and has been widely applied to integrate large-scale renewables and network interconnection. However, the VSC-based HVDC system is vulnerable to DC faults and its protection becomes ever more important with the fast growth in number of installations. In this paper, detailed characteristics of DC faults in the VSC-HVDC system are presented. The DC fault current has a large peak and steady values within a few milliseconds and thus high-speed fault detection and isolation methods are required in an HVDC grid. Therefore, development of the protection scheme for a multi-terminal VSC-based HVDC system is challenging. Various methods have been developed and this paper presents a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multi-terminal network configurations.
TL;DR: Fault Detection and Diagnosis (FDD) is a well-studied area of research as discussed by the authors, where malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques.
Abstract: Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.
TL;DR: A novel Energy-Efficient Heterogeneous Fault Management scheme has been proposed to manage these heterogeneous faults in IWSN and the diagnosis accuracy rate is enhanced up to 17% as compared with existing techniques.
TL;DR: An asynchronous fault detection filter is presented which can follow up the system modes by resorting to a dual hidden Markov model and sufficient conditions are devised to make the resultant Markov jump systems be stochastically stable and hold a specified H ∞ performance level.
TL;DR: Three novel Deep Learning classification and regression models based on Deep Recurrent Neural Networks (DRNN) for Fault Region Identification (FRI), Fault Type Classification (FTC), and Fault Location Prediction (FLP) are introduced.
TL;DR: The embedding of artificial intelligence and internet of things techniques for fault detection and diagnosis into simple hardware, such as low-cost chips, may be economical and technically feasible for photovoltaic plants located in remote areas, with costly and challenging accessibility for maintenance.
Abstract: Currently, a huge number of photovoltaic plants have been installed worldwide and these plants should be carefully protected and supervised continually in order to be safe and reliable during their working lifetime. Photovoltaic plants are subject to different types of faults and failures, while available fault detection equipment are mainly used to protect and isolate the photovoltaic plants from some faults (such as arc fault, line-to-line, line-to-ground and ground faults). Although a good number of international standards (IEC, NEC, and UL) exists, undetectable faults continue to create serious problems in photovoltaic plants. Thus, designing smart equipment, including artificial intelligence and internet of things for remote sensing and fault detection and diagnosis of photovoltaic plants, will considerably solve the shortcomings of existing methods and commercialized equipment. This paper presents an overview of artificial intelligence and internet of things applications in photovoltaic plants. This research presents also the most advanced algorithms such as machine and deep learning, in terms of cost implementation, complexity, accuracy, software suitability, and feasibility of real-time applications. The embedding of artificial intelligence and internet of things techniques for fault detection and diagnosis into simple hardware, such as low-cost chips, may be economical and technically feasible for photovoltaic plants located in remote areas, with costly and challenging accessibility for maintenance. Challenging issues, recommendations, and trends of these techniques will also be presented in this paper.
TL;DR: A model-based fault diagnosis algorithm is developed and validated that uses the switches of an RBS to improve the fault isolability and it is shown simulatively and experimentally that additional faults are isolated by the presented active approach.
Abstract: With the increasing demand for electric vehicles, the interest in battery systems is growing. In order to enable safe operation of these complex energy storage systems, methods of fault diagnosis are needed. Particularly, reconfigurable battery systems (RBSs) with switches are promising on the way to fault tolerance as they allow the system to be reconfigured in the event of a fault. In this article, a model-based fault diagnosis algorithm is developed and validated that uses the switches of an RBS to improve the fault isolability. Since the algorithm changes the structure of the system in order to differentiate between nonisolable faults, it is classified as an active fault diagnosis algorithm. The deviations between sensor measurements and model, called residuals, are stochastically analyzed. For fault isolation, a fuzzy clustering approach is used. A constrained sigma-point Kalman filter minimizes model uncertainties and therefore increases the sensitivity and robustness of the fault diagnosis approach. Furthermore, the filter allows estimating the fault amplitude in case of a fault. Based on active sequential hypothesis testing, a policy to calculate the next switch position is proposed and investigated. It is shown simulatively and experimentally that additional faults are isolated by the presented active approach.
TL;DR: The results confirm that the proposed method effectively detects potential fault conditions in multivariate dynamic systems by detecting the fault symptoms early as possible.
TL;DR: In this paper, the adaptive extended Kalman filter (AEKF) is used to smooth sensor readings of a CAV based on a nonlinear car-following model.
Abstract: In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following model. Using the car-following model the subject vehicle (i.e., the following vehicle) utilizes the leading vehicle’s information to detect sensor anomalies by employing previously-trained One Class Support Vector Machine (OCSVM) models. This approach allows the AEKF to estimate the state of a vehicle not only based on the vehicle’s location and speed, but also by taking into account the state of the surrounding traffic. A communication time delay factor is considered in the car-following model to make it more suitable for real-world applications. Our experiments show that compared with the AEKF with a traditional $\chi ^{2}$ -detector, our proposed method achieves a better anomaly detection performance. We also demonstrate that a larger time delay factor has a negative impact on the overall detection performance.
TL;DR: The proposed method for anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability, and comparison with previous studies shows superior performance.
Abstract: Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O\&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the UK is considered with samples from thirteen months before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology's ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.
TL;DR: A just-in-time-learning-aided canonical correlation analysis (CCA) is proposed for the monitoring and fault detection of multimode processes and has better performance than conventional methods in terms of fault detection rate while still tracking changes in the system.
Abstract: In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is proposed for the monitoring and fault detection of multimode processes. A canonical correlation analysis (CCA)-based fault detection method has been applied to single-operating-mode processes. However, CCA has limitations in handling processes with multiple operating points. These limitations are illustrated by a numerical example. To reduce the time for searching relevant data, K -means is integrated into the JITL to build the local CCA model. Furthermore, the proposed method is compared with commonly used kernel-based methods in terms of computational complexity and interpretability of the results. Finally, the validity and efficacy of the proposed method are shown using an industrial benchmark process. Results show that the proposed method has better performance than conventional methods in terms of fault detection rate while still tracking changes in the system.