Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA

Journal of China University of Mining and Technology

Volumn 18, Issue 4, 2008, Pages 567-570

  XIA, Shi xiong ^a   NIU, Qiang ^a   ZHOU, Yong ^a   ZHANG, Lei ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

Author keywords

fault condition detection; kernel method; KPCA; PCA

Indexed keywords

ELECTRIC FAULT LOCATION; FAULT DETECTION; FOURIER TRANSFORMS; MATHEMATICAL TRANSFORMATIONS; PACKET NETWORKS; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING;

FAULT CONDITION DETECTION; FAULT CONDITIONS; FAULT DEVELOPMENT; FEATURE SPACES; FREQUENCY LOCALIZATION; INPUT FEATURES; KERNEL METHOD; KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA); KPCA; NEW ALGORITHM; NEW METHODS; NON-LINEAR; PCA; PRINCIPAL COMPONENTS; TRANSIENT SIGNALS; WAVELET PACKET (WP); WAVELET PACKET TRANSFORM (WPT);

FEATURE EXTRACTION;

EID: 58649096151     PISSN: 10061266     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1006-1266(08)60295-3     Document Type: Article

References (9)

1. Ribeiro R.L. Fault detection of open-switch damage in voltage-fed PWM motor drive systems. IEEE Trans Power Electron 18 2 (2003) 587-593
2. Sottile J. An overview of fault monitoring and diagnosis in mining equipment. IEEE Trans Ind Appl 30 5 (1994) 1326-1332
3. Peng Z.K., and Chu F.L. Application of wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 17 (2003) 199-221
4. Roth V., and Steinhage V. Nonlinear discriminant analysis using kernel function. Advances in Neural Information Proceeding Systems (2000), MIT Press, MA 568-574
5. Twining C., and Taylor C. The use of kernel principal component analysis to model data distributions. Pattern Recognition 36 1 (2003) 217-227
6. Muller K.R., Mika S., Ratsch S., et al. An introduction to kernel-based learning algorithms. IEEE Trans on Neural Network 12 2 (2001) 181
7. Xiao J.H., Fan K.Q., and Wu J.P. A study on SVM for fault diagnosis. Journal of Vibration, Measurement & Diagnosis 21 4 (2001) 258-262
8. Zhao L.J., Wang G., and Li Y. Study of a nonlinear PCA fault detection and diagnosis method. Information and Control 30 4 (2001) 359-364
9. Xiao J.H., and Wu J.P. Theory and application study of feature extraction based on kernel. Computer Engineering 28 10 (2002) 36-38