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

Jun Rong Yan; Yong Min; Xia Cui; Yan Huang

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60Fault Diagnosis of Rolling Bearing Based on Rough...

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

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

974-977

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.974

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Online since:

June 2011

Authors:

Jun Rong Yan, Yong Min, Xia Cui, Yan Huang

Keywords:

Intelligent Fault Diagnosis, Neural Network (NN), Rolling Bearing, Rough Set (RS)

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