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electrical machine condition monitoring, artificial intelligence

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.





Autor: Yang, Youliang

Fuente: https://era.library.ualberta.ca/


Introducción



University of Alberta Artificial Intelligence in Electrical Machine Condition Monitoring by Youliang Yang A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science Department of Electrical and Computer Engineering c Youliang Yang Fall 2009 Edmonton, Alberta 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. Examining Committee Dr.
Qing Zhao, Electrical and Computer Engineering Dr.
Marek Reformat, Electrical and Computer Engineering Dr.
Michael Lipsett, Mechanical Engineering 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...





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