Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

Abstract:

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On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings,in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted,the eigenvector of wavelet packet of the vibrating signals is constructed,and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

813-818

DOI:

10.4028/www.scientific.net/AMR.121-122.813

Citation:

W. G. Zhao and L. Y. Wang, "Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM", Advanced Materials Research, Vols. 121-122, pp. 813-818, 2010

Online since:

June 2010

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Price:

$35.00

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