DOIONLINE 
DOIONLINE Publish In |
International Journal of Management and Applied Science (IJMAS)-IJMAS |
 Journal Home Volume Issue |
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Issue |
Volume-4,Issue-8 ( Aug, 2018 ) | |||||||||
Paper Title |
Novel Fault Diagnosis for Roller Bearing by using Multi Scale Sample Entropy based Clustering | |||||||||
Author Name |
Peter W. Tse | |||||||||
Affilition |
The Smart Engineering Asset Management Laboratory, Department of Systems Engineering and Engineering Management, City University of Hong Kong, 990777, China | |||||||||
Pages |
1-6 | |||||||||
Abstract |
The rolling bearing vibration signals are nonlinear and non-stationary when a fault exists. A novel method of rolling bearing fault diagnosis based on multiscale sample entropy (MSE) and Gath-Geva (GG) clustering is proposed for feature extraction. Firstly, the MSE method is used to calculate the sample entropy value in different scales. Secondly, the principal component analysis model is chosen to reduce the dimension of the MSE eigenvector. Then the main components which include the primary fault information are regarded as the input of GG cluster algorithm. The experimental results show that the method proposed in this paper can effectively identify various rolling bearing faults with better performance than fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. In the meantime, the effect of the Gath - Geva algorithm is the best in the three cluster methods. Index Terms - Fault diagnosis, Gath - Geva cluster algorithm, Multiscale Sample Entropy, Rolling bearing. | |||||||||
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