Application of variable precision rough set model and neural network to rotating machinery fault diagnosis

Authors:
Qingmin Zhou;Chenbo Yin;Yongsheng Li
Affiliations:
School of Information Science and Engineering, Nanjing University of Technology, Nanjing, China;School of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing, China;School of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing, China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

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Abstract

An integration method of variable precision rough set and neural network for fault diagnosis is presented and used in rotary machinery fault diagnosis. The method integrates the ability of variable precision rough set on reduction of diagnosis information system and that of neural network for fault classification. Typical faults of rotating machinery were simulated in our rotor test-bed. The power spectrum data are used as rotating machinery fault diagnosis signal. For inconsistent data and noise data in power spectrum, variable precision rough set model allows a flexible region of lower approximations by precision variables. By attribute reduction based on variable precision rough set, redundant attributes are identified and removed. The reduction results are used as the input of neural network. The diagnosis results show that the proposed approach for input dimension reduction in neural network is very effective and has better learning efficiency and diagnosis accuracy.