Abstract
In this paper, the application of nonlinear feature extraction based on wavelet kernel KPCA for faults diagnosis is presented. Mexican hat wavelet kernel is intruded to enhance Kernel-PCA nonlinear mapping capability. The experimental data sets of rotor working under four conditions: normal, oil whirling, rub and unbalance are used to test the WKPCA method. The feature reduction results of WKPCA are compared with that of PCA method and KPCA method. The results indicate that WKPCA can classify the rotor fault type efficiently. The WKPCA is more suitable for nonlinear feature reduction in fault diagnosis area.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sun, R., Tsung, F., Qu, L.: Evolving kernel principal component analysis for fault diagnosis. Computers and Industrial Engineering 53(2), 361–371 (2007)
Scholkopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)
Qian, H., Liu, Y.B., Lv, P.: Kernel Principal Components Analysis for early identification of gear tooth crack. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Dalian, pp. 5748–5751 (2006)
Zhao, H., Yuen, P.C., Kwok, J.: A novel incremental principal component analysis and its application for face recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(4), 873–886 (2006)
Feng, W., Junyi, C., Binggang, C.: Nonlinear feature fusion scheme based on kernel PCA for machine condition monitoring. In: Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007, Harbin, pp. 624–629 (2007)
Smola, A.J., Scholkopf, B., Muller, K.-R.: The connection between regularization operators and support vector kernels. Neural Networks 11(4), 637–649 (1998)
Zhang, L., Zhou, W., Jiao, L.: Wavelet Support Vector Machine. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(1), 34–39 (2004)
Sun, W., Chen, J., Li, J.: Decision tree and PCA-based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing 21(3), 1300–1317 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, L., Dong, G.M., Chen, J., Zhu, Y., Pan, Y.N. (2008). Rotating Fault Diagnosis Based on Wavelet Kernel Principal Component. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)