Date of Award
Spring 2013
Degree Type
Thesis
Degree Name
Master of Applied Science (MASc)
Department
Mechanical Engineering
Supervisor
Saeid Habibi
Language
English
Committee Member
Stephen Veldhuis, Samir Ziada
Abstract
Unexpected failures in rotating machinery can result in production downtime, costly repairs and safety concerns. Electric motors are commonly used in rotating machinery and are critical to their operation. Therefore, fault detection and diagnosis of electric motors can play a very important role in increasing their reliability and operational safety. This is especially true for safety critical applications.
This research aims to develop a Fault Detection and Diagnosis (FDD) strategy for detecting motor faults at their inception. Two FDD strategies were considered involving wavelets and state estimation. Bearing faults and stator winding faults, which are responsible for the majority of motor failures, are considered. These faults were physically simulated on a Permanent Magnet Brushless DC Motor (PMBLDC). Experimental results demonstrated that the proposed fault detection and diagnosis schemes were very effective in detecting bearing and winding faults in electric motors.
Recommended Citation
Zhang, Wanlin, "A FAULT DETECTION AND DIAGNOSIS STRATEGY FOR PERMANENT MAGNET BRUSHLESS DC MOTOR" (2013). Open Access Dissertations and Theses. Paper 7598.
http://digitalcommons.mcmaster.ca/opendissertations/7598
McMaster University Library