×

zbMATH — the first resource for mathematics

Data-driven adaptive observer for fault diagnosis. (English) Zbl 1264.93115
Summary: We present an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

MSC:
93C40 Adaptive control/observation systems
93B07 Observability
94C12 Fault detection; testing in circuits and networks
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] J. Gertler, Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY, USA, 1998.
[2] J. Chen and R. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic, Norwell, Mass, USA, 1999. · Zbl 1235.70110 · doi:10.1006/jsvi.1999.2399
[3] R. Patton, P. Frank, and R. Clark, Issues of Fault Diagnosis for Dynamic Systems, Springer, Berlin, Germany, 2000.
[4] M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki, and J. Schröder, Diagnosis and Fault-Tolerant Control, Springer, Berlin, Germany, 2006. · Zbl 1126.93004 · doi:10.1007/978-3-540-35653-0
[5] R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, Berlin, Germany, 2006.
[6] S. Ding, Model-Based Fault Diagnosis Techniques, Springer, Berlin, Germany, 2008. · Zbl 1243.68111 · doi:10.1016/j.jpdc.2008.07.008
[7] L. Ljung, System Identification: Theory for the User, Prentice Hall, Englewood Cliffs, NJ, USA, 1987. · Zbl 0615.93004
[8] P. V. Overschee and B. D. Moor, Subspace Identification for Linear Systems, Kluwer Academic, Dordrecht, The Netherlands, 1996. · Zbl 0888.93001
[9] W. Favoreel, B. De Moor, and P. Van Overschee, “Subspace state space system identification for industrial processes,” Journal of Process Control, vol. 10, no. 2-3, pp. 149-155, 2000. · doi:10.1016/S0959-1524(99)00030-X
[10] S. Qin, “An overview of subspace identification,” Computers and Chemical Engineering, vol. 30, no. 10-12, pp. 1502-1513, 2006. · doi:10.1016/j.compchemeng.2006.05.045
[11] I. Hwang, S. Kim, Y. Kim, and C. E. Seah, “A survey of fault detection, isolation, and reconfiguration methods,” IEEE Transactions on Control Systems Technology, vol. 18, no. 3, pp. 636-653, 2010. · doi:10.1109/TCST.2009.2026285
[12] V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. Kavuri, “A review of process fault detection and diagnosis. Part I: Quantitative model-based methods,” Computers and Chemical Engineering, vol. 27, pp. 293-311, 2003.
[13] P. Zhang and S. Ding, “On fault detection in linear discrete-time, periodic, and sampled-data systems (survey),” Journal of Control Science and Engineering, vol. 2008, Article ID 849546, 19 pages, 2008.
[14] B. Shen, Z. Wang, H. Shu, and G. Wei, “Robust H\infty finite-horizon filtering with randomly occurred nonlinearities and quantization effects,” Automatica, vol. 46, no. 11, pp. 1743-1751, 2010. · Zbl 1218.93103 · doi:10.1016/j.automatica.2010.06.041
[15] B. Shen, Z. Wang, and Y. S. Hung, “Distributed H\infty -consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case,” Automatica, vol. 46, no. 10, pp. 1682-1688, 2010. · Zbl 1204.93122 · doi:10.1016/j.automatica.2010.06.025
[16] H. Dong, Z. Wang, D. W. C. Ho, and H. Gao, “Robust H\infty filtering for Markovian jump systems with randomly occurring nonlinearities and sensor saturation: the finite-horizon case,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3048-3057, 2011. · Zbl 1391.93234 · doi:10.1109/TSP.2011.2135854
[17] H. Dong, Z. Wang, J. Lam, and H. Gao, “Fuzzy-model-based robust fault detection with stochastic mixed time-delays and successive packet dropouts,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 42, no. 3, part B, pp. 365-376, 2012.
[18] S. X. Ding, P. Zhang, B. Huang, and E. L. Ding, “Subspace method aided data-driven design of observer based fault detection systems,” in Proceedings of the 16th Triennial World Congress of International Federation of Automatic Control (IFAC ’05), pp. 167-172, Prague, Czech Republic, July 2005.
[19] S. Ding, S. Yin, P. Zhang, E. Ding, and A. Naik, “An approach to data-driven adaptive residual generator design and implementation,” in Proceedings of the 7th IFAC Symposium on Fault Detection and Supervision and Safety of Technical Processes, Barcelona, Spain, 2009.
[20] S. X. Ding, P. Zhang, A. Naik, E. L. Ding, and B. Huang, “Subspace method aided data-driven design of fault detection and isolation systems,” Journal of Process Control, vol. 19, no. 9, pp. 1496-1510, 2009. · doi:10.1016/j.jprocont.2009.07.005
[21] S. Yin, A. Naik, and S. Ding, “Data-driven design of fault diagnosis scheme for periodic systems,” in Proceedings of the 7th Workshop on Advanced Control and Diagnosis, Zielona Gora, Poland, 2009. · doi:10.1016/S0098-1354(02)00160-6
[22] S. Ding, S. Yin, Y. Wang, Y. Wang, Y. Yang, and B. Ni, “Data-driven design of observers and its applications,” in Proceedings of the 18th IFAC World Congress, Milano, Italy, 2011.
[23] S. Ding, P. Zhang, E. Ding, P. Engel, and W. Gui, “A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis,” in Proceedings of the 18th IFAC World Congress, Milano, Italy, 2011.
[24] L. Chiang, E. Russell, and R. Braatz, Fault Detection and Diagnosis in Industrial Systems, Springer, London, UK, 2001. · Zbl 1209.62296 · doi:10.1111/j.0006-341X.2001.00435.x
[25] P. Ioannou and J. Sun, Robust Adaptive Control, Prentice Hall, 1996. · Zbl 0839.93002
[26] G. Tao, Adaptive Control Design and Analysis, Wiley-Interscience, Hoboken, NJ, USA, 2003. · Zbl 1061.93004 · doi:10.1002/0471459100
[27] K. Astrom and B. Wittenmark, Adaptive Control, Addison-Wesley, 1995.
[28] B. W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman & Hall, London, UK, 1986. · Zbl 0617.62042
[29] E. Martin and A. Morris, “Non-parametric confidence bounds for process performance monitoring charts,” Journal of Process Control, vol. 6, no. 6, pp. 349-358, 1996. · doi:10.1016/0959-1524(96)00010-8
[30] X. Zhang, M. Polycarpou, and T. Parisini, “Design and analysis of fault isolation scheme for a class of uncertain nonlinear systems,” Annual Reviews in Control, vol. 32, pp. 107-121, 2008. · doi:10.1016/j.arcontrol.2008.03.007
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. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.