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Proceedings ArticleDOI

Detection of stator winding fault in induction motors using a motor square current signature analysis (MSCSA)

TL;DR: The motor square current signature analysis (MSCSA) is proposed, which uses the results of spectral analysis of the instantaneous square stator current to analyse the short-circuit fault inter-turn on an induction motor.
Abstract: In this paper the short-circuit fault inter-turn on the stator of an induction motor is analysed by an online diagnostic method. For the diagnostic method it is proposed the motor square current signature analysis (MSCSA). This method uses the results of spectral analysis of the instantaneous square stator current. The effects of stator square current spectrum are described and the related frequencies determined. This method is similar to the instantaneous power signature analysis, however has the advantage of just require one current sensor. Several simulation and experimental results are presented in order to illustrate the characteristics of the proposed method.
Citations
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Journal ArticleDOI
TL;DR: This paper presents the state of the art review describing different type of IM faults and their diagnostic schemes, and several monitoring techniques available for fault diagnosis of IM have been identified and represented.
Abstract: There is a constant call for reduction of operational and maintenance costs of induction motors (IMs) These costs can be significantly reduced if the health of the system is monitored regularly This allows for early detection of the degeneration of the motor health, alleviating a proactive response, minimizing unscheduled downtime, and unexpected breakdowns The condition based monitoring has become an important task for engineers and researchers mainly in industrial applications such as railways, oil extracting mills, industrial drives, agriculture, mining industry etc Owing to the demand and influence of condition monitoring and fault diagnosis in IMs and keeping in mind the prerequisite for future research, this paper presents the state of the art review describing different type of IM faults and their diagnostic schemes Several monitoring techniques available for fault diagnosis of IM have been identified and represented The utilization of non-invasive techniques for data acquisition in automatic timely scheduling of the maintenance and predicting failure aspects of dynamic machines holds a great scope in future

155 citations

Journal ArticleDOI
TL;DR: It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor.
Abstract: An induction motor is at the heart of every rotating machine and hence it is a very vital component. Almost in every industry, around 90% of the machines apply an induction motor as a prime mover. It is a very important driving unit of the machine. Hence, it is necessary to monitor its condition to avoid any catastrophic failure and stoppage of production. The breakdown of the induction motor would not be affordable due to remarkable financial loss, unpredicted shutdown, and the associated repair cost. Vibration is a manifestation of induction motor due to the issues in alignment, balancing, and clearances. Bearing, the most vulnerable to failure due to continuous working under fatigue loading leads to defects. These defects cause changes in the vibration signature over time. The vibration monitoring techniques helps to effectively diagnose mechanical faults such as bearing defect and stator rotor rub. The purpose of this review paper is to summarize the major faults in induction motor, recent diagnostics methods augmented with advanced signal processing techniques, and real-life applications in electric vehicles. It also discusses possible research gaps and opportunities to contribute based on the review findings in the field of condition monitoring. This article presents a detailed review of recent trends in the research of condition monitoring and fault diagnosis of the induction motor. The emphasis is given on the major faults in the induction motor covering time-domain, frequency-domain, and time–frequency domain methods along with an application of artificial intelligence techniques for fault detection. This article presents a comprehensive review of literature which highlights the development and new propositions by researchers in the field of diagnostic techniques for the different faults of induction motor in the last decade. Researchers documented applications of the different conventional methods, advanced signal processing techniques, and soft computing techniques for fault identification of induction motor. This review is carried out for fault identification of induction motor used in machines in general and in particular for identifying the faults in an induction motor of an electric vehicle. A dedicated discussion on the review findings, research gaps, future trends in the field of condition monitoring of induction motor is presented. Condition monitoring of the induction motor in an electric vehicle is also discussed in this paper. It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor. The review presented to analyze the suitability of various condition monitoring techniques for the induction motor fault identification in general and particularly its application in an electric vehicle. It is observed that the diagnosis of faults at the incipient level without using the signal processing technique is challenging. Fault diagnosis of induction motor has witnessed the changes from traditional diagnosis techniques to advanced techniques with a hybrid application of signal processing and artificial intelligence techniques. Still, there is a potential of improvement in reliability, efficiency, robustness, computational time, and real-time diagnostics of faults in IM.

51 citations

Proceedings ArticleDOI
21 May 2017
TL;DR: In this article, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed, which is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's vector, both for current and Voltage.
Abstract: In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. To score the unbalance in the d-q space, a Principal Component Analysis is applied to Park's Vector and with the first two principal components the eccentricity is calculated, while the first principal component is used to determine the phase in short-circuit. The proposed strategy has been experimentally tested on a special 400-V, 50-Hz, 4-pole, 2.2-kW induction motor with reconfigurable stator windings in which it was possible to emulate different types of inter-turn short-circuits. The results are quite promising, even only using 1-kHz sampling frequency to acquire the current and voltage waveforms in the three phases, and the use of the Fast Fourier Transform is avoided. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.

28 citations


Cites methods from "Detection of stator winding fault i..."

  • ...Techniques based on the traditional Motor Current Signature Analysis (MCSA) approach or other more recent methods based on modern signal analysis methodologies have been proposed [4], [6], [7], being many of them based on the comparison of the spectrum between the diagnosed machine and the same machine in a healthy state....

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  • ...It was not possible to compare with the method presented in [6] with the presented one due to lack of information about sample frequency and experimental setup....

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Proceedings ArticleDOI
24 Apr 2017
TL;DR: There are several methods for detecting shorted turns in the stators of induction motors and some of them are simple, straightforward and efficient.
Abstract: Stator-related failures in induction machines are often encountered in industrial applications due to the unforeseen insulation damage This paper addresses the fast prognosis of inter-turn faults It presents simulation and measurement of the stator winding short circuit faults of a three-phase induction motor A 50 Hz four-pole 2 hp induction motor was operated under healthy and faulty conditions and the increase in backward sequence current component assessed It was found that the negative sequence component is a powerful component for the detection of inter-turn faults and simple algorithms allow it to be monitored and faults detected

22 citations


Cites background from "Detection of stator winding fault i..."

  • ...This type of model is difficult and costly to implement, since it requires more advanced devices [10]....

    [...]

01 Jan 2013
TL;DR: In this article, an identification technique for fault detection of induction machines using genetic algorithm is investigated, which indicates the presence of a winding fault and provides information about its nature and location.
Abstract: In this paper, an identification technique for fault detection of induction machines using Genetic Algorithm (GA) is investigated. The condition monitoring technique proposed in this paper indicates the presence of a winding fault and provides information about its nature and location. The data required for the proposed method are motor terminal voltages, stator currents and rotor speed obtained during steady state operation. The data is then processed off-line using an induction motor model in conjunction with GA to determine the effective motor parameters. The proposed technique is demonstrated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine with both stator and rotor winding faults considered. Results confirm the effectiveness of GA to properly identify the type and location of the fault without the need for knowledge of various fault signatures.

20 citations

References
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Journal ArticleDOI
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


"Detection of stator winding fault i..." refers background in this paper

  • ...Many faults can appear in the induction motors [1,2]....

    [...]

Journal ArticleDOI
TL;DR: The fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors are introduced.
Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.

1,396 citations


"Detection of stator winding fault i..." refers background in this paper

  • ...Many faults can appear in the induction motors [1,2]....

    [...]

Journal ArticleDOI
TL;DR: An online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed, able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor.
Abstract: In this paper, an online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed. MCSA is a method for motor diagnosis with stator-current signals. The proposed system diagnoses induction motors having four types of faults such as breakage of rotor bars and end rings, short-circuit of stator windings, bearing cracks, and air-gap eccentricity. Although MCSA is one of the most powerful online methods for diagnosing motor faults, it has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced signal-and-data-processing algorithms are proposed. They are composed of an optimal-slip-estimation algorithm, a proper-sample-selection algorithm, and a frequency auto search algorithm for achieving MCSA efficiently. The proposed system is able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor. Experimental results obtained on 3.7-kW and 30-kW three-phase squirrel-cage induction motors and voltage-source inverters with a vector-control technique are discussed

539 citations


"Detection of stator winding fault i..." refers background in this paper

  • ...In fact, induction motor faults, such as broken rotor bars, abnormal levels of air gap eccentricity, shorted turns in stator windings and certain mechanical problems, will originate in the stator winding currents components that are related with the fault type [4-8]....

    [...]

Journal ArticleDOI
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.

494 citations


"Detection of stator winding fault i..." refers methods in this paper

  • ...Other techniques based on expert systems like neural networks, fuzzy-logic and genetic algorithms have also been used [12-15]....

    [...]

Journal ArticleDOI
TL;DR: In this article, a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors is presented, which are then analyzed and compared to deduce the most appropriate technique for induction motor rotor rotor fault detection.
Abstract: In recent years, marked improvement has been achieved in the design and manufacture of stator winding. However, motors driven by solid-state inverters undergo severe voltage stresses due to rapid switch-on and switch-off of semiconductor switches. Also, induction motors are required to operate in highly corrosive and dusty environments. Requirements such as these have spurred the development of vastly improved insulation material and treatment processes. But cage rotor design has undergone little change. As a result, rotor failures now account for a larger percentage of total induction motor failures. Broken cage bars and bearing deterioration are now the main cause of rotor failures. Moreover, with advances in digital technology over the last years, adequate data processing capability is now available on cost-effective hardware platforms, to monitor motors for a variety of abnormalities on a real time basis in addition to the normal motor protection functions. Such multifunction monitors are now starting to displace the multiplicity of electromechanical devices commonly applied for many years. For such reasons, this paper is devoted to a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors. Features of these techniques which are relevant to fault detection are presented. These features are then analyzed and compared to deduce the most appropriate technique for induction motor rotor fault detection.

476 citations