Support vector machines for detection of analyzer faults - A case study
Applications of Large Scale Industrial Systems, Volume # 1 | Part# 1
Mats Nikus; Mikko Vermasvuori; Nikolai Vatanski; Sirkka-Liisa Jamsa-Jounela
Digital Object Identifier (DOI)
fault detection,monitoring,support vector machines,classification,regression,dearomatization process
The aim of the work presented in this paper is to assess the ability of support vector machines (SVM) for detecting measurement faults. Two different support vector machine approaches for detecting faults are tested and compared to neural networks. The first method is based on a SVM regression model together with an analysis of the residuals whereas the second method is based on a SVM classifier. The methods were applied to a rigorous first principles based dynamic simulator of a dearomatization process.
 Chang C.-C. and C.-J. Lin (2001), Libsvm: a library for support vector machines, SVM software on the internet, available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.  Jemwa G. T. and C. Aldrich (2005), Monitoring of an industrial liquid-liquid extraction system with kernel-based methods, Hydrometallurgy, 78, 41-51.  Hinkley, D. V. (1971), Inference about the change-point from cumulative sum tests, Biometrika, 58, 509-523.  Kulkarni A., Jayaraman V. K. and B. D. Kulkarni (2005), Kowledge incorporated support vector machines to detect faults in the Tennessee Eastman process, Computers & Chemical Engineering, 29, 2128-2133.  Platt J. C. (1998), Fast training of support vector machines using sequential minimal optimization, in: Advances in Kernel Methods -Support Vector Learning, (B. Schölkopf, C. J. C. Burges, and A. J. Smola (Ed)) 1st Edition, MIT Press, Cambridge, Massachusetts, 185-208.  Pöyhönen, S., A. Arkkio, P. Jover and H. Hyötyniemi (2005), Coupling pairwise support vector machines for fault classification, Control Engineering Practice, 13, 759-769.  Ribeiro B. (2005), Support vector machines for quality monitoring in a plastic injection moulding process, IEEE Transactions on systems, man, and cybernetics-Part C: Applications and reviews, 35, 401-410.  Saxén B. and H. Saxén, NNDT - A neural network development tool - User's guide, Technical Report 94-8, Heat Eng. Lab, Åbo Akademi University, Åbo, Finland, 1994.  Vapnik V. N. (1998), Statistical learning theory, John Wiley & Sons, New York, NY.  Venkatasubramanian V., R. Rengaswamy, K. Yin, S. N. Kavuri (2003), A review of process fault detection and diagnosis Part I: Quantitative model-based methods, Computers & Chemical Engineering, 27, 293-311.  Vermasvuori M. T., N. Vatanski and S-L JämsäJounela (2005), Data-based fault detection of the online analysers in a dearomatisation process, 1st Workshop on Networked Control System and Fault Tolerant Control, Ajaccio, France, October 6-7th, 2005 (www.strepnecst.org/1st_workshop/Vermasvuori.pdf)  Wang H. Q., Zhang P and S. X. Ding (2006), Kernel and SVM based theory for industrial process modelling and fault diagnosis. EU project NeCST internal presentation, Paris, March 2006.