An integrated approach to fault diagnosis based on variable precision rough set and neural networks

Authors:
Qingmin Zhou;Chenbo Yin
Affiliations:
School of Information Science and Engineering, Nanjing University of Technology, Nanjing, Jiangsu, China;School of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing, Jiangsu, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

In the paper, a method of fault diagnosis based on integration of the variable precision rough set model (VPRSM) and neural networks is proposed. By VPRSM attribute reduction, redundant attributes are identified and removed. The reduction results are used as the inputs of neural network. With the proposed method an example for rotary machinery fault diagnosis is given. The diagnosis results show that the proposed approach has better learning efficiency and diagnosis accuracy.