Show simple item record

dc.contributor.authorLiu, Jie
dc.date.accessioned2008-09-24 20:28:28 (GMT)
dc.date.available2008-09-24 20:28:28 (GMT)
dc.date.issued2008-09-24T20:28:28Z
dc.date.submitted2008
dc.identifier.urihttp://hdl.handle.net/10012/4033
dc.description.abstractRolling-element bearings are widely used in various mechanical and electrical applications. Accordingly, a reliable bearing health condition monitoring system is very useful in industries to detect incipient defects in bearings, so as to prevent machinery performance degradation and malfunction. Although several techniques have been reported in the literature for bearing fault detection and diagnosis, it is still challenging to implement a bearing condition monitoring system for real-world industrial applications because of the complexity of bearing structures and noisy operating conditions. The objective of this thesis is to develop a novel intelligent system for more reliable bearing fault diagnostics. This system involves two sequential processes: feature extraction and decision-making. The proposed strategy is to develop advanced and robust techniques at each processing stage so as to improve the reliability of bearing condition monitoring. First, a novel wavelet spectrum analysis technique is proposed for the representative feature extraction. This technique applies the wavelet transform to demodulate the resonance signatures that are related to bearing health conditions. A weighted Shannon function is proposed to synthesize the wavelet coefficient functions to enhance feature characteristics. The viability of this technique is verified by experimental tests corresponding to various bearing health conditions. Secondly, an enhanced diagnostic scheme is developed for automatic decision-making. This scheme consists of modules of classification and prediction: a novel neuro-fuzzy classifier is developed to effectively integrate the strengths of the selected fault detection techniques (i.e., the resulting representative features) for a more accurate assessment of bearing health conditions; a novel multi-step predictor is proposed to forecast the future states of bearing conditions, which will be used to further enhance the diagnostic reliability. The investigation results have demonstrated that the developed intelligent diagnostic system outperforms other related bearing fault diagnostic schemes.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectBearingen
dc.subjectMachinery condition monitoringen
dc.titleAn Intelligent System for Bearing Condition Monitoringen
dc.typeDoctoral Thesisen
dc.comment.hiddenThank you.en
dc.pendingfalseen
dc.subject.programMechanical Engineeringen
uws-etd.degree.departmentMechanical and Mechatronics Engineeringen
uws-etd.degreeDoctor of Philosophyen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record


UWSpace

University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages