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Reconstruction-based contribution for process monitoring. (English) Zbl 1188.90074

Summary: This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As an alternative to the traditional contribution plot method, a reconstruction-based contribution for fault diagnosis is proposed based on monitored indices, SPE, \( T^{2}\) and a combined index \(\varphi \). Analysis of the diagnosability of the traditional contributions and the reconstruction-based contributions is performed. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes.

MSC:

90B25 Reliability, availability, maintenance, inspection in operations research

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[1] Box, G. E.P., Some theorems on quadratic forms applied in the study of analysis of variance problems I effect of inequality of variance in the one-way classification, Annals of Mathematics and Statistics, 25, 290-302 (1954) · Zbl 0055.37305
[2] Cherry, G.; Qin, S. J., Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis, IEEE Transactions on Semiconductor Manufacturing, 19, 2, 159-172 (2006)
[3] Chiang, L. H.; Russell, E. L.; Braatz, R. D., Fault detection and diagnosis in industrial systems, (Advanced textbooks in control and signal processing (2001), Springer-Verlag: Springer-Verlag London Great Britain) · Zbl 0982.93005
[4] Dunia, R.; Qin, S. J., Subspace approach to multidimensional fault identification and reconstruction, AIChE Journal, 44, 1813-1831 (1998)
[5] Dunia, R.; Qin, S. J.; Edgar, T. F.; Mcavoy, T. J., Identification of faulty sensors using principal component analysis, AIChE Journal, 42, 2797-2812 (1996)
[6] Gertler, J., Fault detection and diagnosis in engineering systems (1998), Marcel Dekker Inc.
[7] Gertler, J.; Cao, J., PCA-based fault diagnosis in the presence of control and dynamics, AIChE Journal, 50, 2, 388-402 (2004)
[8] Gertler, J.; Li, W.; Huang, Y.; Mcavoy, T. J., Isolation-enhanced principal component analysis, AIChE Journal, 45, 2, 323-334 (1999)
[9] Isermann, R., Process fault detection based on modeling and estimation methods—A survey, Automatica, 20, 4, 387-404 (1984) · Zbl 0539.90037
[10] Kano, M.; Nagao, K.; Hasebe, S.; Hashimoto, I.; Ohno, H., Statistical process monitoring based on dissimilarity of process data, AIChE Journal, 48, 6, 1231-1240 (2002)
[11] Miller, P., Swanson, R.E., & Heckler, C.F. (1993). Contribution plots: The missing link in multivariate quality control. In Fall Conf. of the ASQC and ASA; Miller, P., Swanson, R.E., & Heckler, C.F. (1993). Contribution plots: The missing link in multivariate quality control. In Fall Conf. of the ASQC and ASA · Zbl 0925.93034
[12] Nomikos, P. (1997). Statistical monitoring of batch processes. In Preprints of Joint Statistical Meeting; Nomikos, P. (1997). Statistical monitoring of batch processes. In Preprints of Joint Statistical Meeting
[13] Nomikos, P.; MacGregor, J. F., Multivariate SPC charts for monitoring batch processes, Technometrics, 37, 1, 41-59 (1995) · Zbl 0825.62740
[14] Qin, S. J., Statistical process monitoring: Basics and beyond, Journal of Chemometrics, 17, 480-502 (2003)
[15] Qin, S. J.; Valle-Cervantes, S.; Piovoso, M., On unifying multi-block analysis with applications to decentralized process monitoring, Journal of Chemometrics, 15, 715-742 (2001)
[16] Qin, S. J.; Li, W., Detection, identification and reconstruction of faulty sensors with maximized sensitivity, AIChE Journal, 45, 1963-1976 (1999)
[17] Qin, S. J.; Li, W., Detection and identification of faulty sensors in dynamic processes, AIChE Journal, 47, 7, 1581-1593 (2001)
[18] Raich, A.; Cinar, A., Statistical process monitoring and disturbance diagnosis in multivariate continuous processes, AIChE Journal, 42, 995-1009 (1996)
[19] Singhal, A.; Seborg, D., Pattern matching in multivariate time series databases using a moving-window approach, Industrial & Engineering Chemistry Research, 41, 3822-3838 (2002)
[20] Singhal, A.; Seborg, D., Evaluation of a pattern matching method for the tennessee eastman challenge process, Journal of Process Control, 16, 601-613 (2006)
[21] Westerhuis, J. A.; Gurden, S. P.; Smilde, A. K., Generalized contribution plots in multivariate statistical process monitoring, Chemometrics and Intelligent Laboratory Systems, 51, 95-114 (2000)
[22] Willsky, A. S., A survey of design methods for failure detection in dynamic systems, Automatica, 12, 601-611 (1976) · Zbl 0345.93067
[23] Wise, B. M.; Gallagher, N. B.; Bro, R.; Shaver, J. M.; Winding, W.; Koch, R. S., PLS toolbox user manual (2006), Eigenvector Research Inc.
[24] Wise, B. M.; Gallagher, N. B., The process chemometrics approach to process monitoring and fault detection, Journal of Process Control, 6, 329-348 (1996)
[25] Yoon, S.; MacGregor, J. F., Statistical and causal model-based approaches to fault detection and isolation, AIChE Journal, 46, 1813-1824 (2000)
[26] Yoon, S.; MacGregor, J. F., Fault diagnosis with multivariate statistical models, part I: Using steady state fault signatures, Journal of Process Control, 11, 387-400 (2001)
[27] Yue, H.; Qin, S. J., Reconstruction based fault identification using a combined index, Industrial & Engineering Chemistry Research, 40, 4403-4414 (2001)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.