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A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery
School of Mechanical & Electrical Engineering, Beijing University of Chemical Technology Chao Yang District, Beijing, 100029, P.R. China
Graduate School of Bioresources, Mie University/1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan
* Author to whom correspondence should be addressed.
Received: 27 February 2009; in revised form: 7 March 2009 / Accepted: 25 March 2009 / Published: 1 April 2009
Abstract: This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to.
Keywords: Feature extraction; Information theory; Reciprocating Machinery; Fault diagnosis; Rolling element bearing; Envelope Analysis
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MDPI and ACS Style
Wang, H.; Chen, P. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery. Sensors 2009, 9, 2415-2436.
Wang H, Chen P. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery. Sensors. 2009; 9(4):2415-2436.
Wang, Huaqing; Chen, Peng. 2009. "A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery." Sensors 9, no. 4: 2415-2436.