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Open AccessArticle

Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

by Ke Li 1, Xueliang Ping 1, Huaqing Wang 2,*, Peng Chen 3,* and Yi Cao 1
1
School of Mechanical Engineering, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, Jiangsu, China
2
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chao Yang District, Beijing 100029, China
3
Department of Environmental Science and Engineering, Mie University 1577 Kurimamachiya-cho, Tsu-shi, Mie-ken 514-8507, Japan
*
Authors to whom correspondence should be addressed.
Sensors 2013, 13(6), 8013-8041; https://doi.org/10.3390/s130608013
Received: 8 April 2013 / Revised: 27 May 2013 / Accepted: 10 June 2013 / Published: 21 June 2013
(This article belongs to the Section Physical Sensors)
A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. View Full-Text
Keywords: condition diagnosis; relative crossing information (RCI); pseudo Wigner-Ville distribution (PWVD); ant colony optimization (ACO); synthesizing symptom parameter (SSP) condition diagnosis; relative crossing information (RCI); pseudo Wigner-Ville distribution (PWVD); ant colony optimization (ACO); synthesizing symptom parameter (SSP)
MDPI and ACS Style

Li, K.; Ping, X.; Wang, H.; Chen, P.; Cao, Y. Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis. Sensors 2013, 13, 8013-8041.

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