Volume 7 Number 7 (Jul. 2012)
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JSW 2012 Vol.7(7): 1531-1538 ISSN: 1796-217X
doi: 10.4304/jsw.7.7.1531-1538

Multi-Layer Kernel Learning Method Faced on Roller Bearing Fault Diagnosis

Guangbin Wang1, Yilin He2, and Kuanfang He2
1Engineering Research Center of Advanced Mine Equipment, Ministry of Education, Hunan University of Science and Technology, China
2Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, China

Abstract—Bearing fault is the major fault of the rotating machinery, in order to better identify the fault of bearing, the multi-layer kernel learning methods based on local tangent space alignment (LTSA) and support vector machine (SVM) are proposed. In this method, the supervised learning is embedded into the improved local tangent space alignment algorithm, realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machine. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods.

Index Terms—fault diagnosis, multi-layer kernel, SVM, supervised, LLTSA, LLTSA

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Cite: Guangbin Wang, Yilin He, and Kuanfang He, "Multi-Layer Kernel Learning Method Faced on Roller Bearing Fault Diagnosis," Journal of Software vol. 7, no. 7, pp. 1531-1538, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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