Recent developments of induction motor drives fault diagnosis using AI techniques
TL;DR: A review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI) 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.
Abstract: 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. In general, a diagnostic procedure starts from a fault tree developed on the basis of the physical behavior of the electrical system under consideration. In this phase, the knowledge of well-tested models able to simulate the electrical machine in different fault conditions is fundamental to obtain the patterns characterizing the faults. The fault tree navigation performed by an expert system inference engine leads to the choice of suitable diagnostic indexes, referred to a particular fault, and relevant to build an input data set for specific AI (NNs, fuzzy logic, or neuro-fuzzy) systems. The discussed methodologies, that play a general role in the diagnostic field, are applied to an induction machine, utilizing as input signals the instantaneous voltages and currents. In addition, the supply converter is also considered to incorporate in the diagnostic procedure the most typical failures of power electronic components. A brief description of the various AI techniques is also given; this highlights the advantages and the limitations of using AI techniques. Some applications examples are also discussed and areas for future research are also indicated.
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TL;DR: A review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis 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 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. In addition, 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. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
1,869 citations
Cites background from "Recent developments of induction mo..."
...to MCSA results for condition monitoring and fault detection of machines [5], [34], [71]–[74]....
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TL;DR: A comprehensive review of the PHM field is provided, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information, to enable rapid customization and integration of PHM systems for diverse applications.
1,164 citations
Cites methods from "Recent developments of induction mo..."
...[107–109], HMM [110], Fuzzy Logic [111–113], GA [113], Higher Order Statistics [114], Park's Current Vector Pattern [115], Petri Net [116], Kalman Filter [117] J....
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TL;DR: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years, and research activities are classified into four main topics.
Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.
1,003 citations
Cites background or methods from "Recent developments of induction mo..."
...starting from several electrical and mechanical quantities such as currents, voltages, fluxes, control signals, acoustic noise, and vibrations [85]....
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...In [85], the different AI techniques are summarized....
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...Fuzzy and adaptive fuzzy systems were applied to motor fault diagnosis for the following tasks [81], [85], [87], [91], [92]:...
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...In [81] and [85], the issue of detecting machine faults is tackled with different neuro-fuzzy techniques....
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...example, in the classification step, the UNN could be replaced by a fuzzy system where fuzzy sets representing the different faults could be tuned by an adaptive neural network [81], [85]....
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TL;DR: In this article, a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology is presented. And a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors is provided.
Abstract: Condition monitoring of induction motors is a fast emerging technology for online detection of incipient faults. It avoids unexpected failure of a critical system. Approximately 30-40% of faults of induction motors are stator faults. This work presents a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology. It is aimed at providing a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors. A list of 183 research publications on the subject is appended for quick reference.
541 citations
Cites background from "Recent developments of induction mo..."
...neural systems will also be widely used in the near future [121]–[129], [131], [137], [140]–[143], [147]–[151]....
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TL;DR: An in-depth literature review of testing and monitoring methods that diagnose the condition of the turn-to-turn insulation of low-voltage machines, which is a rapidly expanding area for both research and product development efforts.
Abstract: A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. To determine the conditions of the stator insulation system of motor drive systems, various testing and monitoring methods have been developed. This paper presents an in-depth literature review of testing and monitoring methods, categorizing them into online and offline methods, each of which is further grouped into specific areas according to their physical nature. The main focus of this paper is on testing and monitoring techniques that diagnose the condition of the turn-to-turn insulation of low-voltage machines, which is a rapidly expanding area for both research and product development efforts. In order to give a compact overview, the results are summarized in two tables. In addition to monitoring methods on turn-to-turn insulation, some of the most common methods to assess the stator's phase-to-ground and phase-to-phase insulation conditions are included in the tables as well.
438 citations
Cites methods from "Recent developments of induction mo..."
...[27], the diagnostic procedure using AI-based methods can be divided into the signature extraction, the fault identification, and the fault severity evaluation....
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