Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the...

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Main Authors: Faraz Ahmed Shaikh, Muhammad Zuhaib Kamboh, Bilal Ahmad Alvi, Sheroz Khan, Farhat Muhammad Khan
Format: Article
Language:English
Published: Sir Syed University of Engineering and Technology, Karachi. 2022-12-01
Series:Sir Syed University Research Journal of Engineering and Technology
Online Access:http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/513
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author Faraz Ahmed Shaikh
Muhammad Zuhaib Kamboh
Bilal Ahmad Alvi
Sheroz Khan
Farhat Muhammad Khan
author_facet Faraz Ahmed Shaikh
Muhammad Zuhaib Kamboh
Bilal Ahmad Alvi
Sheroz Khan
Farhat Muhammad Khan
author_sort Faraz Ahmed Shaikh
collection DOAJ
description In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults of Phase imbalance, Rotor misalignment, and High contact resistance (HCR). DWT has the ability to categorize the input signals into approximate coefficient state for low frequency signals and detailed coefficient state for high frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique based on conditional monitoring of an induction motor with precise detailed coefficients and more skilled at light loads given on a motor-shaft with relatively fast execution time compared to FFT. Furthermore, the comparison of healthy and faulty induction motors has been compiled by Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with PCA gives more perfect results. In this research, different faults have been detected with 100% accuracy using LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses.
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publisher Sir Syed University of Engineering and Technology, Karachi.
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spelling doaj-art-c2e5b51c7c994c15a209bfe3f8ec74ca2025-06-27T08:43:38ZengSir Syed University of Engineering and Technology, Karachi.Sir Syed University Research Journal of Engineering and Technology1997-06412415-20482022-12-01122Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA ClassifierFaraz Ahmed Shaikh0Muhammad Zuhaib KambohBilal Ahmad AlviSheroz KhanFarhat Muhammad KhanNazeer Hussain University In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults of Phase imbalance, Rotor misalignment, and High contact resistance (HCR). DWT has the ability to categorize the input signals into approximate coefficient state for low frequency signals and detailed coefficient state for high frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique based on conditional monitoring of an induction motor with precise detailed coefficients and more skilled at light loads given on a motor-shaft with relatively fast execution time compared to FFT. Furthermore, the comparison of healthy and faulty induction motors has been compiled by Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with PCA gives more perfect results. In this research, different faults have been detected with 100% accuracy using LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses. http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/513
spellingShingle Faraz Ahmed Shaikh
Muhammad Zuhaib Kamboh
Bilal Ahmad Alvi
Sheroz Khan
Farhat Muhammad Khan
Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier
Sir Syed University Research Journal of Engineering and Technology
title Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier
title_full Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier
title_fullStr Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier
title_full_unstemmed Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier
title_short Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier
title_sort condition based health monitoring of electrical machines using dwt and lda classifier
url http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/513
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