Abstract
Intelligent fault diagnosis benefits from efficient feature selection. Neighborhood rough sets are effective in feature selection. However, determining the neighborhood value accurately remains a challenge. The wrapper feature selection algorithm is designed by combining the kernel method and neighborhood rough sets to self-adaptively select sensitive features. The combination effectively solves the shortcomings in selecting the neighborhood value in the previous application process. The statistical features of time and frequency domains are used to describe the characteristic of the rolling bearing to make the intelligent fault diagnosis approach work. Three classification algorithms, namely, classification and regression tree (CART), commercial version 4.5 (C4.5), and radial basis function support vector machines (RBFSVM), are used to test UCI datasets and 10 fault datasets of rolling bearing. The results indicate that the diagnostic approach presented could effectively select the sensitive fault features and simultaneously identify the type and degree of the fault.
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Recommended by Editor Yeon June Kang
Xiaoran Zhu is a Ph.D. student of the School of Mechanical Engineering, Xi’an Jiaotong University. His research interests are intelligent fault diagnosis, prognostics and health management, and equipment performance degradation assessment.
Youyun Zhang received her B.Eng, M.Sc, and D.Sc. degrees from Xi’an Jiaotong University in 1970, 1981, and 1989, respectively. She is currently a professor at Xi’an Jiaotong University and serves as deputy director of the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System. Her main research interests are condition monitoring and fault diagnosis, dynamics of bearing-rotor system, and source sharing and integration in network environment.
Yongsheng Zhu was born in 1973. He received his B.Eng and D.Sc. degrees from Xi’an Jiaotong University in 1996 and 2003, respectively. Currently, he serves as associate professor at Xi’an Jiaotong University. His main research interests are rolling bearing, dynamics of bearing-rotor system, and condition monitoring.
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Zhu, X., Zhang, Y. & Zhu, Y. Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features. J Mech Sci Technol 26, 2649–2657 (2012). https://doi.org/10.1007/s12206-012-0716-9
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DOI: https://doi.org/10.1007/s12206-012-0716-9