A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems
TL;DR: In this paper, a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters is proposed to solve the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors.
Abstract: Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.
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TL;DR: Current gaps and challenges on use of BNs in fault diagnosis in the last decades with focus on engineering systems are explored and several directions for future research are explored.
Abstract: Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.
314 citations
Cites methods from "A Data-Driven Fault Diagnosis Metho..."
...In BN-based inverter fault diagnosis, the following identification methods are defined: 1) system reports a single open-circuit of switch with highest posterior probability when it is higher than 70%, or 50% higher than the second highest one; and 2) system reports double open-circuit failure of switch with highest and second highest posterior probabilities when both are higher than 70% or 50% higher than the third highest one [131]....
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TL;DR: The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration.
Abstract: This article gives an overview of the artificial intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This article is accompanied by an Excel file listing the relevant publications for statistical analytics.
287 citations
Cites methods from "A Data-Driven Fault Diagnosis Metho..."
...One of the typical probabilistic graphical methods is the Bayesian network [117]....
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TL;DR: The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.
Abstract: Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.
182 citations
TL;DR: The resilience value of an engineering system can be predicted using the proposed methodology, which provides implementation guidance for engineering planning, design, operation, construction, and management.
166 citations
TL;DR: In this paper, fault diagnosis and fault-tolerant control strategies have been studied comprehensively for dual three-phase permanent-magnet synchronous motor (PMSM) drives to improve the reliability.
Abstract: In this paper, fault diagnosis and fault-tolerant control strategies have been studied comprehensively for dual three-phase permanent-magnet synchronous motor (PMSM) drives to improve the reliability. Based on direct torque control (DTC) with space vector modulation, a series of diagnostic and tolerant control methods have been proposed for five types of faults, namely, speed-sensor fault, dc-link voltage-sensor fault, current-sensor fault, open-phase fault, and open-switch fault. First, diagnosis and tolerant schemes are proposed for speed-sensor fault by estimating the rotor angle speed with the rotating speed of stator flux. Second, diagnosis and tolerant schemes are proposed for dc-link voltage-sensor fault by combining the current model based stator flux observer with the voltage model based stator flux observer. Third, a three-step method is designed to diagnose three types of faults related to current signals, namely, current-sensor fault, open-phase fault, and open-switch fault simultaneously. A vector space decomposition based current estimation method is proposed to achieve fault-tolerant control for the current-sensor fault, and the voltage compensation based fault-tolerant control is presented for both open-phase and open-switch faults. The experiments have been taken on a laboratory prototype to verify the effectiveness of the proposed fault diagnosis and fault-tolerant schemes.
144 citations
Cites background from "A Data-Driven Fault Diagnosis Metho..."
...In practice, any fault mentioned above will degrade the system performance, and even cause the system breakdown [8], [16]....
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References
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TL;DR: The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade.
Abstract: With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.
2,026 citations
"A Data-Driven Fault Diagnosis Metho..." refers methods in this paper
...Generally, fault diagnosis methods can be categorized into model-based methods, signal-based methods, and data-driven methods [3], [4]....
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Book•
01 Aug 2014TL;DR: This book provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.
Abstract: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
1,070 citations
"A Data-Driven Fault Diagnosis Metho..." refers methods in this paper
...In this study, Pearl’s belief propagation algorithm is used for inference [32]....
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TL;DR: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints.
Abstract: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented.
722 citations
"A Data-Driven Fault Diagnosis Metho..." refers methods in this paper
...Generally, fault diagnosis methods can be categorized into model-based methods, signal-based methods, and data-driven methods [3], [4]....
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TL;DR: A comprehensive review of reliability assessment and improvement of power electronic systems from three levels: 1) metrics and methodologies of reliability assess of existing system; 2) reliability improvement of existing systems by means of algorithmic solutions without change of the hardware; and 3) reliability-oriented design solutions that are based on fault-tolerant operation of the overall systems.
Abstract: With wide-spread application of power electronic systems across many different industries, their reliability is being studied extensively. This paper presents a comprehensive review of reliability assessment and improvement of power electronic systems from three levels: 1) metrics and methodologies of reliability assessment of existing system; 2) reliability improvement of existing system by means of algorithmic solutions without change of the hardware; and 3) reliability-oriented design solutions that are based on fault-tolerant operation of the overall systems. The intent of this review is to provide a clear picture of the landscape of reliability research in power electronics. The limitations of the current research have been identified and the direction for future research is suggested.
681 citations
"A Data-Driven Fault Diagnosis Metho..." refers background in this paper
...power switches have the advantages of high efficiency, fast switching, and easy control of the gate-signal communications; however, they become faulty because of aging, overloading, or unpredicted operational conditions, and are the most vulnerable components in inverters [2]....
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Proceedings Article•
01 Jan 1985
490 citations