Mine hoist braking system fault diagnosis based on a support vector machine

Zhongguo Kuangye Daxue Xuebao/Journal of China University of Mining and Technology

Volumn 35, Issue 6, 2006, Pages 813-817

  Guo, Xiao Hui ^a,b   Ma, Xiao Ping ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b JIANGSU NORMAL UNIVERSITY   (China)

Author keywords

Braking system; Fault diagnosis; Mine hoist; Support vector machine; Wavelet packet

Indexed keywords

CLASSIFICATION (OF INFORMATION); FAILURE ANALYSIS; LEARNING SYSTEMS; PATTERN RECOGNITION; WAVELET TRANSFORMS;

BRAKING SYSTEM; FAULT DIAGNOSIS; SUPPORT VECTOR MACHINE; WAVELET PACKET;

MINE HOISTS;

EID: 33846037336     PISSN: 10001964     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (12)

1. LIU Ke-wei, YANG Zhao-jian. Research on diagnosis for hoist system based on artificial neural network[J]. Journal of Taiyuan University of Science and Technology, 2002, 33(7): 441-442.
2. ZHOU Jin, XIAO Xing-ming. Safety detection of braking system based on wavelet packet and neural network[J]. Journal of Transducer Technology, 2005, 24(1): 63-66.
3. MA Xiao-ping, XIAO Xing-ming. Hoister fault diagnosis system based on expert system[J]. Journal of China University of Mining and Technology, 1999, 28(3): 499-501.
4. VAPNIK V N. The Nature of Statistical Learning Theory[M]. Springer: Verlag, 1995.
5. DAUBECHIES I. The wavelet transform, time-frequency localization and signal analysis[J]. IEEE Trans Inform Theory, 1990, 36(5): 961-1005.
6. CRITIANINI N. SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge: Cambridge University Press, 2000.
7. GUNN S R. Support vector machines for classification and regression[R]. Southampton; University of Southampton, 1998: 1-28.