Roller Bearing Fault Diagnosis Based on ELMD and Fuzzy C-Means Clustering Algorithm

Chang Liang Liu; Xiu Mei Huang; Xian Jin Luo

doi:10.4028/www.scientific.net/AMM.602-605.1698

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Roller Bearing Fault Diagnosis Based on ELMD and...

Roller Bearing Fault Diagnosis Based on ELMD and Fuzzy C-Means Clustering Algorithm

Abstract:

For the non-stationary characteristics of rotating machinery fault vibration signal, proposed a fault diagnosis method that based on ensemble local mean decomposition (ELMD) to extract fault feature, and fuzzy C-means clustering (FCM) to perform the fault identification. ELMD method can effectively solve the problem of aliasing modes in LMD. Firstly, decomposing the fault vibration signal by ELMD, PF components were obtained in which the initial feature vector matrix, The PF components compose a initial feature vector matrix, and do singular value decomposition, using the singular value decomposition feature vector as the fault characteristic vectors. Finally, using FCM clustering as a fault classifier. Achieved the identification of different fault types. Experimental results show that this method can effectively achieve the bearing fault diagnosis.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

1698-1700

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.1698

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Online since:

August 2014

Authors:

Chang Liang Liu*, Xiu Mei Huang, Xian Jin Luo

Keywords:

ELMD, Fault Diagnosis, Fuzzy C-Means Clustering, Modal Aliasing

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