Rolling Bearing Fault Diagnosis Based on Wavelet Packet- Neural Network Characteristic Entropy

Abstract:

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On the basis of neural network based on wavelet packet-characteristic entropy(WP-CE) 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 the three layers BP neural network 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 108-111)

Edited by:

Yanwen Wu

Pages:

1075-1079

DOI:

10.4028/www.scientific.net/AMR.108-111.1075

Citation:

L. Y. Wang et al., "Rolling Bearing Fault Diagnosis Based on Wavelet Packet- Neural Network Characteristic Entropy", Advanced Materials Research, Vols. 108-111, pp. 1075-1079, 2010

Online since:

May 2010

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Price:

$35.00

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