TVAR and SVM based fault diagnosis method for rotation machine

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering

Volumn 27, Issue 9, 2007, Pages 99-103

  Zhang, Long ^a   Xiong, Guo Liang ^b   Liu, He Sheng ^c   Zou, Hui Jun ^a   Chen, Hui ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b EAST CHINA JIAOTONG UNIVERSITY   (China)

^c SHANGRAO NORMAL UNIVERSITY   (China)

Author keywords

Fault diagnosis; Nonstationary signals; Rotation machine; Support vectors machine; Time varying autoregressive model

Indexed keywords

FEATURE EXTRACTION; PATTERN RECOGNITION SYSTEMS; ROTATION; VIBRATIONS (MECHANICAL);

FAULT DIAGNOSIS; NONSTATIONARY SIGNALS; ROTATION MACHINE; TIME VARYING AUTOREGRESSIVE MODEL;

SUPPORT VECTOR MACHINES;

EID: 34249292325     PISSN: 02588013     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Chen Ruqing. Comparison between two fault diagnosis methods based on Neural Network[J]. Proceedings of the CSEE, 2005, 25(16): 112-115 (in Chinese).
2. Chen Changzheng, Li Qing, Lu Yifang, et al. Intelligent fault diagnosis method for turbo-generator unit[J]. Proceedings of the CSEE, 2002, 22(5): 121-124 (in Chinese).
3. Jiang Dongxiang, Diao Jinhui, Zhao Gang, et al. Study on methods of vibration fault diagnosis based on time-frequency countor for turbine generator unit[J]. Proceedings of the CSEE, 2005, 25(6): 146-151 (in Chinese).
4. Peng Zhike, He Yongyong, Lu Qing, et al. Using wavelet method to analyze fault feature of rub rotor in generator[J]. Proceedings of the CSEE, 2003, 23(5): 75-79 (in Chinese).
5. Ma Xiaojiang, Wang Fengli, Cai Yue, et al. Application of local wave time-frequency distribution to early faults diagnosis of rotor system[J]. Proceedings of the CSEE, 2004, 24(3): 161-164,168 (in Chinese).
6. Qian Lijun, Jiang Dongxiang. Application of wavelet transform in the condition monitoring of cracked rotor during startup process[J]. Proceedings of the CSEE, 2003, 23(5): 86-89,121 (in Chinese).
7. Yang Shixii Hu Jingsong, Wu Zhaotong, et al. The comparison of vibration signals' time-frequency analysis between EMD based HT and WT method in rotating machinery[J]. Proceedings of the CSEE, 2003, 23(6): 102-107 (in Chinese).
8. Yu Zhiwei, Su Baoku, Zeng Ming. Application of wavelet packet in fault diagnosis system of large scale dc motor rotor[J]. Proceedings of the CSEE, 2005, 25(22): 158-162 (in Chinese).
9. Huang Xiaoguang, Wang Yonghong, Weng Shilie. Gas turbine fault diagnosis based on the BP algorithm[J]. Proceedings of the CSEE, 2000, 20(12): 72-74 (in Chinese).
10. Ding Feng. Pattern recognition technique of time series analysis in monitoring and diagnosis of complex mechanical systems[J]. Modular Machine Tool and Automatic Manufacturing Technique, 2005, 10: 45-47 (in Chinese).
11. Xu Feng, Wang Zhifang, Wang Baosheng. Research on AR model application to forecast trend of vibration signals[J]. Journal of Tsinghua University. 1999, 29(4): 57-59 (in Chinese).
12. Yin Andong, Yang Zhengmin. Fault diagnosis of vehicle transmission gear based on time series analysis and neural networks[J]. Automotive engineering, 2005, 27(4): 502-505 (in Chinese).
13. Lu Shuang, Hou Yuejin, Tian ye. Fault diagnosis of rolling bearing based on AR model and radial function neural networks[J]. Journal of Mechanical Transmission, 2004, 28(5): 10-14 (in Chinese).
14. Chukiet Sodsri. Time-varying autoregressive models for nonstationary acoustic signal and its frequency analysis[D]. USA: Pennsylvania State University, 2003.
15. EOM K B. Analysis of acoustic signatures from moving vehicles using time-varying autoregressive models[J]. Multidimensional Systems and Signal Processing, 1999, 10(4): 357-378.
16. Wang Wenhua, Wang Hongyu. An algorithm property analysis for time-varying parameters estimation of nonstationary random signal model[J]. Journal of Dalian University of Technology, 1997, 37(1): 97-102 (in Chinese).
17. Zhang Long. Time-varying autoregressive model and its application in fault diagnosis of rotation machine under nonstationary conditions[D]. Nanchang: East China Jiaotong University, 2005 (in Chinese).
18. Sun Bin, Zhou Yunlong. Identification method of Gas-liquid two-phase flow regime based on support vector machine and wavelet packet energy feature[J]. Proceedings of the CSEE, 2005, 25(17): 93-99 (in Chinese).
19. Xiao Jianhua, Wu Jinpei, Fan Keqing, et al. The Gear fault diagnosis based on support vector machine[J]. Manufacturing Information Engineering of China, 2003, 32(2): 107-109 (in Chinese).
20. Zhang Zhousuo, Li Lingjun, He Zhengjia. Research on diagnosis method of machinery fault based on support vector machine[J]. Journal of Xi'an Jiaotong University, 2002, 36(12): 1303-1306 (in Chinese).