Gear fault diagnosis based on SVM and multi-sensor information fusion

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)

Volumn 41, Issue 6, 2010, Pages 2184-2188

  Jiang, Ling Li ^a,b   Liu, Yi Lun ^a   Li, Xue Jun ^b   Chen, An Hua ^b  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b HUNAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fault diagnosis; Gear; Information fusion; Multi vibration signal; SVM (support vector machine)

Indexed keywords

CREST FACTORS; DIAGNOSTIC ACCURACY; FAULT DIAGNOSIS; FEATURE LEVEL; GEAR FAILURE; GEAR FAULT DIAGNOSIS; GEAR VIBRATION; MULTI SENSOR; MULTI-SENSOR INFORMATION FUSION; MULTI-VIBRATION SIGNAL; SINGLE SENSOR; SVM(SUPPORT VECTOR MACHINE); VIBRATION SIGNAL;

GEARS; SENSORS; SUPPORT VECTOR MACHINES;

INFORMATION FUSION;

EID: 79952061691     PISSN: 16727207     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

1. DING Kang, LI Wei-hua, ZHU Xiao-yong. Practical technology of gear and gear box fault diagnosis [M]. Beijing: China Machine Press, 2005: 1-4.
2. HAN Chong-zhao, ZHU Hong-yan, DUAN Zhan-sheng, et al. Multi-source information fusion [M]. Beijing: Tsinghua University Press, 2006: 4-11.
3. HE Jian-jun, ZHAO Rui. Hydroelectric generating sets fault diagnosis based on information fusion technology [J]. Journal of Central South University: Science and Technology, 2007, 32 (2): 333-338.
4. Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory [J]. Information Fusion, 2007, 8 (4): 379-386.
5. LI Xue-jun, Li Ping, Chu Fu-lei. Data fusion of multi-sensor vibration signal using correlation function [J]. Journal of Vibration, Measurement and Diagnosis, 2009, 29 (2): 179-183.
6. TAN Qing, XIANG Yang-hui. Application of weighted evidential theory and its information fusion method in fault diagnosis [J]. Journal of Vibration and Shock, 2008, 27 (4): 112-116.
7. Widodo A, Yang B S. Support vector machine in machine condition monitoring and fault diagnosis [J]. Mechanical Systems and Signal Processing, 2007, 21 (6): 2560-2574.
8. YU De-jie, YANG Yu, CHENG Jun-sheng. Fault diagnosis approach for gears based on EMD and SVM [J]. Chinese Journal of Mechanical Engineering, 2005, 41 (1): 140-144.
9. Samanta B. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms [J]. Mechanical Systems and Signal Processing, 2004, 18 (3): 625-644.
10. LEI Ya-guo, HE Zheng-jia, ZI Yang-yang, et al. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique [J]. Mechanical Systems and Signal Processing, 2008, 22 (2): 419-435.
11. YANG Jun-yang, ZHANG You-yun, ZHU Yong-sheng. Intelligent fault diagnosis of rolling element bearing based on SVMs and statistical characteristics [C] // Proceedings of the 2007 International Manufacturing Science and Engineering Conference. Atlanta, 2007: 525-536.
12. Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods [M]. Cambridge: Cambridge University Press, 2000: 24-46.
13. Hsu C, Chang C C, Lin C J. A practical guide to support vector classification [EB/OL]. [2009-05-19]. http://www.csie.ntu.edu.tw/~cjlin.
14. Hsu C, Lin C J. A comparison of methods for multi-class support vector machines [J]. IEEE Transactions on Neural Networks, 2002, 13 (2): 415-425.