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An intelligent system for bearing co...

Liu, Jie.

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An intelligent system for bearing condition monitoring.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An intelligent system for bearing condition monitoring./
Author:	Liu, Jie.
Description:	135 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0619.
Contained By:	Dissertation Abstracts International71-01B.
Subject:	Engineering, Mechanical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR55536
ISBN:	9780494555361

An intelligent system for bearing condition monitoring.
Liu, Jie.

An intelligent system for bearing condition monitoring. - 135 p.

Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0619.

Thesis (Ph.D.)--University of Waterloo (Canada), 2009.

Rolling-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.

ISBN: 9780494555361Subjects--Topical Terms:

783786
Engineering, Mechanical.

An intelligent system for bearing condition monitoring.
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020 $a 9780494555361
035 $a (UMI)AAINR55536
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040 $a UMI $c UMI
100 1 $a Liu, Jie. $3 1026842
245 1 3 $a An intelligent system for bearing condition monitoring.
300 $a 135 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0619.
502 $a Thesis (Ph.D.)--University of Waterloo (Canada), 2009.
520 $a Rolling-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.
590 $a School code: 1141.
650 4 $a Engineering, Mechanical. $3 783786
690 $a 0548
710 2 $a University of Waterloo (Canada). $3 1017669
773 0 $t Dissertation Abstracts International $g 71-01B.
790 $a 1141
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR55536