Intelligent Technique and Its Application in Fault Diagnosis of Locomotive Bearing Based on Granular Computing

Authors:
Zhang Zhousuo;Yan Xiaoxu;Cheng Wei
Affiliations:
Department of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, P.R. China 710049;Department of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, P.R. China 710049;Department of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, P.R. China 710049
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

This paper presents a new approach to intelligent fault diagnosis of the machinery based on granular computing. The tolerance granularity space mode is constructed by means of the inner-class distance defined in the attributes space. Different features of the vibration signals, including time domain statistical features and frequency domain statistical features, are extracted and selected using distance evaluation technique as the attributes to construct the granular structure. Finally, the proposed approach is applied to fault diagnosis of locomotive bearings, testing results show that the proposed approach can reliably recognize different faulty categories and severities.