Abstract
A multi-scale independent component analysis (ICA) approach is investigated for industrial process monitoring. By integrating the ability of wavelet on multi-scale analysis and that of ICA on extracting independent components for non-Gaussian process variables, the multivariate statistical monitoring techniques can obtain improved performance. Contrastive tests have been carried out on the famous benchmark chemical plant among ICA-like and PCA-like methods, which reveals that multi-scale ICA approach has lower missed detection rate of faults.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mason, R.L., Young, J.C.: Multivariate Statistical Process Control with Industrial Application. SIAM, USA (2002)
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)
Kano, M., Nagao, K., Hasebe, S., Hashimoto, I., Bakshi, B.: Comparison of Statistical Process Monitoring Methods: Application to the Eastman Challenge Problem. Computers and Chemical Engineering 24, 175–181 (2000)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 4th edn. Prentice Hall, Englewood Cliffs (1998)
Roberts, S., Everson, R.: Independent Component Analysis: Principles and Practice. Cambridge University, Cambridge (2001)
Ypma, A., Tax, D.M.J., Duin, R.P.W.: Robust Machine Fault Detection with Independent Component Analysis and Support Vector Data Description. Proc. IEEE Neural Networks for Signal Processing, 67–76 (1999)
Hyvarinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13, 411–430 (2000)
Lee, J.-M., Yoo, C., Lee, I.-B.: Statistical Process Monitoring with Independent Component Analysis. Journal of Process Control 14, 467–485 (2004)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transaction of Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)
Bakshi, B.R.: Multiscale PCA with Application to Multivariate Statistical Process Monitoring. AICHE J. 44(7), 1596–1610 (1998)
Kisilev, P., Zibulevsky, M., Zeevi, Y.Y.: A Multiscale Framework for Blind Source Separation of Linearly Mixed Signals. Journal of Machine Learning Research 4, 1339–1364 (2003)
Cover, T.M., Thomas, J.A.: Elements of Information theory. Wiley, New York (1991)
Hyvarinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans. Neural Networks 10(3), 626–634 (1999)
Daubechies, I.: Ten Lectures on Wavelet. Capital City Press (1992)
Wang, M.P., Jones, M.C.: Kernel Smoothing. Chapman & Hall, London (1995)
Downs, J.J., Vogel, E.F.: A Plant-Wide Industrial Process Control Problem. Computers and Chemical Engineering 17(3), 245–255 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, F., Wu, CY. (2006). Improvement on Multivariate Statistical Process Monitoring Using Multi-scale ICA. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_47
Download citation
DOI: https://doi.org/10.1007/11679363_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)