Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/12737
Title: | A FAULT DETECTION AND DIAGNOSIS STRATEGY FOR PERMANENT MAGNET BRUSHLESS DC MOTOR |
Authors: | Zhang, Wanlin |
Advisor: | Habibi, Saeid Stephen Veldhuis, Samir Ziada |
Department: | Mechanical Engineering |
Keywords: | Motor;Wavelet;BLDC;Kalman filter;Estimation;Fault detection and diagnosis;Electro-Mechanical Systems;Electro-Mechanical Systems |
Publication Date: | Apr-2013 |
Abstract: | <p>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.</p> <p>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.</p> |
URI: | http://hdl.handle.net/11375/12737 |
Identifier: | opendissertations/7598 8656 3481694 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
---|---|---|---|
fulltext.pdf | 2.65 MB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.