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Shock and Vibration
Volume 2016 (2016), Article ID 5468716, 10 pages
Research Article

A Novel Clustering Method Combining ART with Yu’s Norm for Fault Diagnosis of Bearings

1School of Mechanic and Automation, Wuhan University of Science and Technology, Wuhan, Hubei 430073, China
2School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430081, China

Received 8 December 2015; Revised 4 April 2016; Accepted 18 April 2016

Academic Editor: Mariano Artés

Copyright © 2016 Zengbing Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Clustering methods have been widely applied to the fault diagnosis of mechanical system, but the characteristic that the number of cluster needs to be determined in advance limits the application range of the method. In this paper, a novel clustering method combining the adaptive resonance theory (ART) with the similarity measure based on the Yu’s norm is presented and applied to the fault diagnosis of rolling element bearings, which can be adaptive to generate the number of cluster by the vigilance parameter test. Time-domain features, frequency-domain features, and time series model parameters are extracted to demonstrate the fault-related information about the bearings, and then considering the irrelevance or redundancy of some features many salient features are selected by an improved distance discriminant technique and input into the proposed clustering method to diagnose the faults of bearings. The experiment results confirmed that the proposed clustering method can diagnose the fault categories accurately and has better diagnosis performance compared with fuzzy ART and Self-Organizing Feature Map (SOFM).