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Sensors 2009, 9(4), 2415-2436; doi:10.3390/s90402415
Article

A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

1, 2
 and 2,*
Received: 27 February 2009; in revised form: 7 March 2009 / Accepted: 25 March 2009 / Published: 1 April 2009
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan)
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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 Feature extraction; Information theory; Reciprocating Machinery; Fault diagnosis; Rolling element bearing; Envelope Analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Wang H, Chen P. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery. Sensors. 2009; 9(4):2415-2436.

Chicago/Turabian Style

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.



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