A Hybrid Scheme for on-Line Diagnostic Decision Making Using Optimal Data Representation and Filtering Technique

Authors: Hyun-Woo Cho

Abstract:

The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.

Keywords: Diagnostics, batch process, nonlinear representation, data filtering, multivariate statistical approach

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070431

References:

[1] S. J. Qin, "Statistical process monitoring: basics and beyond," Journal of Chemometrics, vol. 17, pp. 480-502, 2003.
[2] S. Bersimisl, S. Psarakis, and J. Panaretos, "Multivariate statistical process control charts: an overview," Quality and Reliability Engineering International, vol. 23, pp. 517-543, 2007.
[3] X. Meng, A. J. Morris, and E. B. Martin, "On-line monitoring of batch processes using PARAFAC representation," Journal of Chemometrics, vol. 17, pp. 65-81, 2003.
[4] V. A. Sotiris, P. W. Tse, and M. G. Pecht, "Anomaly detection through a bayesian support vector machine," IEEE Transactions on Reliability, vol. 59, pp. 277-286 , 2010.
[5] R. Lombardo, J.-F. Durand, and A. P. Leone, "Multivariate additive PLS spline boosting in agro-chemistry studies," Current Analytical Chemistry, vol. 8, pp. 236-253, 2012.
[6] L. H. Chiang, E. L. Russell, and R. D. Braatz, "Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis," Chemometrics and Intelligent Laboratory Systems, vol. 50, pp. 243-252, 2000.
[7] J. C. Wong, K. A. Mcdonald, and A. Palazoglu, "Classification of abnormal plant operation using multiple process variable trends," Journal of Process Control, vol. 11, pp. 409-418. 2001.
[8] G. Baudat, and F. Anouar, " Generalized discriminant analysis using a kernel approach," Neural Computation, vol. 12, pp. 2385-2404, 2000.
[9] R. Rosipal, and L. J. Trejo, "Kernel partial least squares regression in reproducing Kernel Hilbert space," Journal of Machine Learning Research, vol. 2, pp. 97-123, 2001.
[10] B. Scholkopf, A. J. Smola, and K. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, pp. 1299-1319, 1998.
[11] A. Boulesteix, G. Tutz, and K. Strimmer, "A CART-based approach to discover emerging patterns in microarray data," Bioinformatics, vol. 19, pp. 2465-2472, 2003.
[12] J. Westerhuis, S. de Jong, and A. K. Smilde, "Direct orthogonal signal correction," Chemometrics and Intelligent Laboratory Systems, vol. 56, pp. 13-25, 2001.
[13] H. W. Cho, and K. J. Kim, "A method for predicting future observations in the monitoring of a batch process," Journal of Quality Technology, vol. 35, pp. 59-69, 2003.