Fault Diagnosis of Rolling Bearing Based on Rough Set and Neural Network

Abstract:

Article Preview

Artificial neural network was one of the most important methods in intelligent fault diagnosis because it has the performance of nonlinear pattern classification and the capacity of self-learning and self-organization, but it can not judge redundancy and usefulness of information. Rough set can reduce the knowledge of information system and dislodge redundant information. In this paper, fault data of rolling bearing was reduced by the greedy algorithm of rough set. Training data and test data of BP neural network had been reduced by rough set. By comparison of two test result about simply data and original data, it was indicated that resolving power was unchanged and database was simply.

Info:

Periodical:

Edited by:

Qi Luo

Pages:

974-977

DOI:

10.4028/www.scientific.net/AMM.58-60.974

Citation:

J. R. Yan et al., "Fault Diagnosis of Rolling Bearing Based on Rough Set and Neural Network", Applied Mechanics and Materials, Vols. 58-60, pp. 974-977, 2011

Online since:

June 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.