Fault Diagnosis Methods for Three Phase PWM Inverter Fed Induction Motor

Soufi, Youcef; Bahi, Tahar; Harkat, Mohamed-Faouzi; Mohammedi, M.

doi:10.15866/irea.v6i4.16022

Cited by 3 publications

(3 citation statements)

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“…During normal operation, these motors are exposed to many kinds of mechanical and electrical stresses that can cause severe failures. Examples of major mechanical faults include such events as broken rotor bars [1][2][3] and bearing faults [4][5][6][7]. In general, electrical faults include insulation failure, short circuits, and supply problems and are influenced by the quality of the power supply, which might exhibit voltage unbalances and variations in supply frequency under varying load conditions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Hussain¹,

Soother²,

Kalwar³

et al. 2021

IASC

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Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using signal processing techniques such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A three-phase motor model is developed in MATLAB Simulink and simulated under various fault conditions. The current signature is observed using FFT, spectrogram, and scalogram to detect the faults. It is observed that under some fault conditions, the current signature analysis remains indistinguishable from the non-fault case. Therefore, deep learning (DL) methods are adopted here to identify these faults. The time-series data of healthy and unhealthy conditions are obtained from the simulation results. The comparative investigation among DL models confirmed the superiority of the long short-term memory (LSTM) model, which achieved 97.87% accuracy in identifying the individual faults.

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Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Hussain¹,

Soother²,

Kalwar³

et al. 2021

IASC

View full text Add to dashboard Cite

show abstract

“…Major mechanical faults are bearing fault [3][4][5] and broken rotor bar [6][7][8][9][10]. Electrical fault is influenced by power quality supplied by AC grid, frequency variations, voltage disturbances, and load variation.…”

Section: Introductionmentioning

confidence: 99%

“…Motor current signature analysis (MCSA) is an important technique adopted for condition monitoring. Induction motor faults like bearing problems, broken rotor bar, eccentricity abnormalities, and stator winding faults cause variation in amplitude and frequency of motor current signature [3][4][5][6][7][8][9][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

Rajamany

Srinivasan

Rajamany

et al. 2019

Journal of Electrical and Computer Engineering

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The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.

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Optimization Techniques for Prognostics of On-Board Electromechanical Servomechanisms Affected by Progressive Faults

Vedova

Berri

2019

IREASE

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In relatively recent years, electromechanical actuators have gradually replaced systems based on hydraulic power for flight control applications. Electromechanical servosystems are typically operated by electrical machines that transfer rotational power to the controlled elements (e.g. the aerodynamic control surfaces) by means of gearings and mechanical transmission. Compared to electrohydraulic systems, electromechanical actuators offer several advantages, such as reduced weight, simplified maintenance and complete elimination of contaminant, flammable or polluting hydraulic fluids. On-board actuators are often safety critical; then, the practice of monitoring and analyzing the system response through electrical acquisitions, with the aim of estimating fault evolution, has gradually become an essential task of the system engineering. For this purpose, a new discipline, called Prognostics, has been developed in recent years. Its aim is to study methodologies and algorithms capable of identifying such failures and foresee the moment when a particular component loses functionality and is no longer able to meet the desired performance. In this paper, authors have introduced the use of optimization techniques in prognostic methods (e.g. model-based parametric estimation algorithms) and have proposed a new model-based fault detection and identification method, based on Genetic Algorithms optimization approach, able to perform an early identification of the aforesaid progressive failures, investigating its ability to identify timely symptoms alerting that a component is degrading.

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Fault Diagnosis Methods for Three Phase PWM Inverter Fed Induction Motor

Cited by 3 publications

References 0 publications

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

Optimization Techniques for Prognostics of On-Board Electromechanical Servomechanisms Affected by Progressive Faults

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