Batch process monitoring by kernel similarity-based support vector data description

WANG Jianlin1 MA Linyu1 LIU Weimin1 QIU Kepeng1 YU Tao1

(1.College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China 100029)

【Abstract】Kernel distance-based support vector data description (SVDD) for batch process monitoring exhibited poor monitoring precision by setting control limit from the largest kernel distance in historical process dataset but ignoring hyperspherical irregularity in high dimensional space. A kernel similarity based SVDD monitoring method was proposed for batch process monitoring. Kernel similarity was taken as kernel function value between support vectors and data samples for testing. The weighted summation of kernel similarity and distance of support vectors at various time points was utilized to set dynamic control limit for data samples of batch process to be monitored. Batch process monitoring was achieved by judging if kernel distance of test sample exceeded the dynamic control limit. This monitoring method considered irregularity of hypersphere, local distribution characteristics of process dataset in high dimensional space, and spontaneity of data samples, so that it could improve accuracy in batch process monitoring. The method effectiveness was demonstrated by numerical simulation and metal etching process in semiconductor manufacturing.

【Keywords】 kernel similarity; support vector data description; dynamic monitoring; batch process;

【DOI】

【Funds】 National Natural Science Foundation of China (61240047) Natural Science Foundation of Beijing, China (4152041)

Download this article

    References

    [1] JIANG Q C, YAN X F. Just-in-time reorganized PCA integrated with SVDD for chemical process monitoring [J]. AICh E Journal, 2014, 60 (3): 949–965.

    [2] HU Y, MA H H, SHI H B. Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares [J]. Chemometrics and Intelligent Laboratory Systems, 2013, 123 (3): 15–27.

    [3] RASHID M M, YU J. Nonlinear and non-Gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach [J]. Industrial & Engineering Chemistry Research, 2012, 51 (33): 10910–10920.

    [4] ZHAO C H, SUN Y X. Step-wise sequential phase partition (SSPP) algorithm based statistical modeling and online process monitoring [J]. Chemometrics and Intelligent Laboratory Systems, 2013, 125 (5): 109–120.

    [5] ZHAO C H, WANG F L, YAO Y, et al. Phase-based statistical modeling, online monitoring and quality prediction for batch processes [J]. Acta Automatica Sinica, 2010, 36 (3): 366–374 (in Chinese).

    [6] ZHANG J M, GE Z Q, XIE L, et al. Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD [J]. CIESC Journal, 2009, 60 (1): 169–171 (in Chinese).

    [7] WANG P L, GE Z Q, SONG Z H. Online fault monitoring for batch processes based on adaptive multi-model ICA-SVDD [J]. Chinese Journal of Scientific Instrument, 2009, 30 (7): 1347–1352 (in Chinese).

    [8] MACGREGOR J F, KOURTI T. Statistical process control of multivariate processes [J]. Control Engineering Practice, 1995, 3 (3): 403–414.

    [9] KITTIWACHANA S, FERREIRA D L S, LLOYD G R, et al. One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process [J]. Journal of Chemometrics, 2010, 24 (3/4): 96–110.

    [10] KITTIWACHANA S, FERREIRA D L S, FIDO L A, et al. Self-organizing map quality control index [J]. Analytical Chemistry, 2010, 82 (14): 5972–5982.

    [11] NING X H, TSUNG F G. A density-based statistical process control scheme for high-dimensional and mixed-type observations [J]. IIE Transactions, 2012, 44 (4): 301–311.

    [12] NOMIKOS P, MACGREGOR J F. Monitoring batch processes using multiway principal component analysis [J]. AICh E Journal, 1994, 40 (8): 1361–1375.

    [13] NOMIKOS P, MACGREGOR J F. Multi-way partial least squares in monitoring batch processes [J]. Chemometrics & Intelligent Laboratory Systems, 1995, 30 (1): 97–108.

    [14] CHANG K Y, LEE J M, VANROLLEGHEM P A, et al. On-line monitoring of batch processes using multiway independent component analysis [J]. Chemometrics and Intelligent Laboratory Systems, 2004, 71 (2): 151–163.

    [15] LU N Y, GAO F R, WANG F L. Sub-PCA modeling and on-line monitoring strategy for batch processes [J]. AICh E Journal, 2004, 50 (1): 255–259.

    [16] YAO Y, GAO F R. A survey on multistage/multiphase statistical modeling methods for batch processes [J]. Annual Reviews in Control, 2009, 33 (2): 172–183.

    [17] KANG J H, YU J, KIM S B. Adaptive nonparametric control chart for time-varying and multimodal processes [J]. Journal of Process Control, 2016, 37: 34–45.

    [18] GANI W, TALEB H, LIMAM M. An assessment of the kernel-distance-based multivariate control chart through an industrial application [J]. Quality and Reliability Engineering International, 2011, 27 (4): 391–401.

    [19] GE Z Q, SONG Z H. Bagging support vector data description model for batch process monitoring [J]. Journal of Process Control, 2013, 23: 1090–1096.

    [20] LEE J M, YOO C K, LEE I B. Fault detection of batch processes using multiway kernel principal component analysis [J]. Computers & Chemical Engineering, 2004, 28 (9): 1837–1847.

    [21] ZHANG Y W, HU Z Y. On-line batch process monitoring using hierarchical kernel partial least squares [J]. Chemical Engineering Research and Design, 2011, 89 (10): 2078–2084

    [22] SUN R X, TSUNG F G. A kernel-distance-based multivariate control chart using support vector methods [J]. International Journal of Production Research, 2003, 41 (13): 2975–2989.

    [23] CAMCI F, CHINNAM R B, ELLIS R D. Robust kernel distance multivariate control chart using support vector principles [J]. International Journal of Production Research, 2008, 46 (18): 5075–5095.

    [24] NING X H, TSUNG F G. Improved design of kernel distance-based charts using support vector methods [J]. IIE Transactions, 2013, 45 (4): 464–476.

    [25] SUKCHOTRAT T, KIM S B, TSUNG F G. One-class classification-based control charts for multivariate process monitoring [J]. IIE transactions, 2009, 42 (2): 107–120.

    [26] KHEDIRI I B, WEIHS C, LIMAM M. Kernel k-means clustering based local support vector domain description fault detection of multimodal processes [J]. Expert Systems with Applications, 2012, 39 (2): 2166–2171.

    [27] TAX D M J, DUIN R P W. Support vector domain description [J]. Pattern recognition letters, 1999, 20 (11): 1191–1199.

    [28] TAX D M J, DUIN R P W. Support vector data description [J]. Machine Learning, 2004, 54 (1): 45–66.

    [29] SAKLA W, CHAN A, JI J, et al. An SVDD-based algorithm for target detection in hyperspectral imagery [J]. Geoscience and Remote Sensing Letters, IEEE, 2011, 8 (2): 384–388.

    [30] GE Z Q, GAO F R, SONG Z H. Batch process monitoring based on support vector data description method [J]. Journal of Process Control, 2011, 21 (6): 949–959.

    [31] YAO M, WANG H G, XU W L. Batch process monitoring based on functional data analysis and support vector data description [J]. Journal of Process Control, 2014, 24 (7): 1085–1097.

This Article

ISSN:0438-1157

CN: 11-1946/TQ

Vol 68, No. 09, Pages 3494-3500

September 2017

Downloads:0

Share
Article Outline

Abstract

  • Introduction
  • 1 Process monitoring based on SVDD
  • 2 Batch process monitoring based on kernel similarity SVDD
  • 3 Experimental verification
  • 4 Conclusion
  • References