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Matching pursuit feature based neural network pattern recognition of ball bearing faults

Yang, Hongyu, Mathew, Joseph, Ma, Lin, & Kosse, Vladis (2004) Matching pursuit feature based neural network pattern recognition of ball bearing faults. In International Conference of Maintenance Societies 2004, 25-28 May 2004, Sydney, Australia.

Abstract

The task of condition monitoring and fault diagnosis of rotating machinery faults is significant but is often cumbersome and labour intensive. Automating the procedure of feature extraction, fault detection and identification of rotating machinery has the advantage of reducing the reliance on experienced personnel with expert knowledge. Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. This paper proposes a schema to automate the diagnostic procedure based on a newly developed time frequency analysis technique -Matching Pursuit. Features are extracted based on Matching Pursuit analysis and subsequently input to a Feedforward Neural Network (FFNN) to classify bearing conditions including Healthy, Inner Race Fault (IRF), Outer Race Fault (ORF), and Rolling Element Fault (REF). The performance of the automatic diagnostic procedure was then evaluated using classification rate and Mean Square Error (MSE). The proposed procedure successfully classified the four conditions of rolling bearings.

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ID Code: 17940
Item Type: Conference Paper
Keywords: Fault diagnosis, Matching Pursuit, Neural Network, Pattern recognition, Feature extraction
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
Divisions: Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2004 (please consult author)
Deposited On: 17 Feb 2009 15:40
Last Modified: 09 Jun 2010 23:23

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