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Permanent link (DOI): https://doi.org/10.7939/R36M7K

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Theses and Dissertations

Artificial intelligence in electrical machine condition monitoring Open Access

Descriptions

Other title
Subject/Keyword
electrical machine condition monitoring
artificial intelligence
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Yang, Youliang
Supervisor and department
Zhao, Qing (Electrical and Computer Engineering)
Examining committee member and department
Reformat, Marek (Electrical and Computer Engineering)
Lipsett, Michael (Mechanical Engineering)
Department
Department of Electrical and Computer Engineering
Specialization

Date accepted
2009-10-06T16:51:35Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
Electrical machine condition monitoring plays an important role in modern industries. Instead of allowing the machines to run until failure, it is preferred to gather more information about the machine condition before the machine is shut down, so that the machine downtime can be reduced due to repair. Also, it would be very useful to track the machine condition and predict the future machine condition so that maintenance plan can be scheduled in advance. In this thesis, artificial intelligence techniques are utilized for machine condition monitoring. The thesis consists of 3 parts. In the first part, Neural Network and Support Vector Machine models are built to classify different machine conditions. In the second part, time series prediction models are built with Support Vector Regression and wavelet packet decomposition to predict the future machine vibration. Support Vector Regression is applied again in the final part of the thesis to track the machine condition and determine if the machine has thermal sensitivity issue or not. In all 3 parts, experimental results are promising and they certainly can be used in practice in order to facilitate the machine condition monitoring process.
Language
English
DOI
doi:10.7939/R36M7K
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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