Abstract
Data of nonlinear chemical industry process have characterics of containing noises and random disturbances. An improved fault detection method based on wavelet denoising and kernel principal component analysis (KPCA) method is developed, it can not only denoise and anti-disturb, but also can transform nonlinear problems in the input space into linear problems in the feature space. So this can solves the poor performances of principal component analysis (PCA) method in nonlinear problems. The proposed method is applied to Tennessee Eastman (TE) process. The simulation results verify that the proposed method is superior to PCA method obviously in fault detection.
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References
Wang, X., Kruger, U., Irwin, G.W.: Process monitoring approach using fast moving window PCA. Industrial & Engineering Chemistry Research 44(15), 5691–5702 (2005)
Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)
Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Computers and Chemical Engineering 17(3), 245–255 (1993)
Monsef, H., Lotfifard, S.: Internal fault current identification based on wavelet transform in power transformers. Electric Power Systems Research 77, 1637–1645 (2007)
Singh, G.K., AlKazzaz, S.A.S.: Isolation and Identification of Dry Bearing Faults in Induction Machine Using Wavelet Transform 42, 849–861 (2009)
Cho, H.W.: Nonlinear feature extraction and classification of multivariate process data in kernel feature space. Expert Systems with Applications 32(2), 534–542 (2007)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Zhao, X., Wang, X. (2012). A Fault Detection Algorithm Based on Wavelet Denoising and KPCA. In: Jin, D., Lin, S. (eds) Advances in Future Computer and Control Systems. Advances in Intelligent and Soft Computing, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29387-0_46
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DOI: https://doi.org/10.1007/978-3-642-29387-0_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29386-3
Online ISBN: 978-3-642-29387-0
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