Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat

In this thesis, a predictive maintenance method for the development of adetection and classification method for comprehensive fault conditions in induction motors (IM) is proposed. Induction motors are taken into account because they are commonly utilized in industrial and commercial plants worldwid...

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Main Author:	Leo Uchat, Felicity Bulan
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Neural networks (Computer science) Electric power distribution. Electric power transmission Wide area networks
Online Access:	http://ir.uitm.edu.my/id/eprint/14493/ http://ir.uitm.edu.my/id/eprint/14493/1/TD_FELICITY%20BULAN%20LEO%20UCHAT%20EE%2016_5.pdf

id	uitm-14493
recordtype	eprints
spelling	uitm-144932018-03-01T02:43:43Z http://ir.uitm.edu.my/id/eprint/14493/ Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat Leo Uchat, Felicity Bulan Neural networks (Computer science) Electric power distribution. Electric power transmission Wide area networks In this thesis, a predictive maintenance method for the development of adetection and classification method for comprehensive fault conditions in induction motors (IM) is proposed. Induction motors are taken into account because they are commonly utilized in industrial and commercial plants worldwide. Fault detection and classification (FDC) of IMs are important in order to avoid unpredicted breakdown of electrical motors. The inherent failures due to unavoidable electrical stresses in motors results in motors experiencing stator faults, rotor faults and unbalanced voltage faults. If these faults are not identified in the early stage, it may become catastrophic to the operation of the motor. In this thesis, the detection and classification of induction motor faults due to electrical related failures using Motor Current Signature Analysis (MCSA) and Feedforward Neural Network (FNN) neural network is proposed. Data collection of current signal of motors with different fault conditions is carried out by using laboratory experiments. The data collected which consists of the three phase stator current signals in different motor fault conditions is analysed using MCSA method. Power spectral density (PSD) method is then utilized to extract three phase stator current signals to obtain the frequency spectrum of stator currents via Fast Fourier Transform (FFT) as the data input which is fed into the FNN classifier. As it is important to choose proper training algorithm for training the FNN, therefore three different FNN training algorithms are compared in terms of their accuracy, number of iterations and training time. 2016 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/14493/1/TD_FELICITY%20BULAN%20LEO%20UCHAT%20EE%2016_5.pdf Leo Uchat, Felicity Bulan (2016) Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Neural networks (Computer science) Electric power distribution. Electric power transmission Wide area networks
spellingShingle	Neural networks (Computer science) Electric power distribution. Electric power transmission Wide area networks Leo Uchat, Felicity Bulan Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat
description	In this thesis, a predictive maintenance method for the development of adetection and classification method for comprehensive fault conditions in induction motors (IM) is proposed. Induction motors are taken into account because they are commonly utilized in industrial and commercial plants worldwide. Fault detection and classification (FDC) of IMs are important in order to avoid unpredicted breakdown of electrical motors. The inherent failures due to unavoidable electrical stresses in motors results in motors experiencing stator faults, rotor faults and unbalanced voltage faults. If these faults are not identified in the early stage, it may become catastrophic to the operation of the motor. In this thesis, the detection and classification of induction motor faults due to electrical related failures using Motor Current Signature Analysis (MCSA) and Feedforward Neural Network (FNN) neural network is proposed. Data collection of current signal of motors with different fault conditions is carried out by using laboratory experiments. The data collected which consists of the three phase stator current signals in different motor fault conditions is analysed using MCSA method. Power spectral density (PSD) method is then utilized to extract three phase stator current signals to obtain the frequency spectrum of stator currents via Fast Fourier Transform (FFT) as the data input which is fed into the FNN classifier. As it is important to choose proper training algorithm for training the FNN, therefore three different FNN training algorithms are compared in terms of their accuracy, number of iterations and training time.
format	Thesis
author	Leo Uchat, Felicity Bulan
author_facet	Leo Uchat, Felicity Bulan
author_sort	Leo Uchat, Felicity Bulan
title	Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat
title_short	Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat
title_full	Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat
title_fullStr	Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat
title_full_unstemmed	Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat
title_sort	development of a detection and classification method for induction motor faults using motor current signature analysis and feedforward neural network / felicity bulan leo uchat
publishDate	2016
url	http://ir.uitm.edu.my/id/eprint/14493/ http://ir.uitm.edu.my/id/eprint/14493/1/TD_FELICITY%20BULAN%20LEO%20UCHAT%20EE%2016_5.pdf
first_indexed	2023-09-18T22:51:45Z
last_indexed	2023-09-18T22:51:45Z
_version_	1777417590344253440

Development of a detection and classification method for induction motor faults using Motor Current Signature Analysis and Feedforward Neural Network / Felicity Bulan Leo Uchat

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