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Abstract
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Article Information:
Intelligent Method for Faults Diagnosis of Rolling Bearings via Chaos Optimized Support Vector Machine
Hongling Qin, Xincong Zhou, Hongliang Tian and Lu Xiao
Corresponding Author: Hongling Qin
Submitted: July 09, 2012
Accepted: July 31, 2012
Published: February 01, 2013 |
Abstract:
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In a transmission system, the faults of rolling bearings occur very frequently. A tiny crack may cause huge damage on the system. Therefore, it is essential to detect the faults of rolling bearings. However, the single fault has been researched extensively while very few works have been done on the multiply faults detection (i.e., simultaneous existence of 2 or more fault types). To deal with this problem, a new method is proposed to diagnosis multi-fault of rolling bearings in this study. The vibration data was analyzed in the time and frequency domains. Then the Support Vector Machine (SVM) was used to recognize the fault patterns. In order to enhance the generalization ability of the SVM diagnosis model, the Chaos algorithm was adopted to optimize the structural parameters of the SVM. Experimental tests have been carried out on a fault simulation setup. The fault detection results show that the proposed method is competent for the multi-fault diagnosis of rolling bearings. The fault detection rate is beyond 90.0%.
Key words: Chaos optimization, fault diagnosis, rolling bearings, SVM, , ,
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Cite this Reference:
Hongling Qin, Xincong Zhou, Hongliang Tian and Lu Xiao, . Intelligent Method for Faults Diagnosis of Rolling Bearings via Chaos Optimized Support Vector Machine. Research Journal of Applied Sciences, Engineering and Technology, (04): 1373-1376.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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