Decentralized and distributed active fault diagnosis: Multiple model estimation algorithms

Straka, Ondřej ; Punčochář, Ivo

International Journal of Applied Mathematics and Computer Science, Tome 30 (2020) no. 2, pp. 239-249.

Résumé

The paper focuses on active fault diagnosis (AFD) of large scale systems. The multiple model framework is considered and two architectures are treated: the decentralized and the distributed one. An essential part of the AFD algorithm is state estimation, which must be supplemented with a mechanism to achieve feasible implementation in the multiple model framework. In the paper, the generalized pseudo Bayes and interacting multiple model estimation algorithms are considered. They are reformulated for a given model of a large scale system. Performance of both AFD architectures is analyzed for different combinations of multiple model estimation algorithms using a numerical example.

Détail

Keywords: fault diagnosis, large scale system, multiple model
Mots-clés : diagnostyka błędu, system dużej skali, model wieloskładnikowy

Bibliographie

[1] Ajgl, J. and Šimandl, M. (2011). Particle based probability density fusion with differential Shannon entropy criterion, Proceedings of the 14th International Conference on Information Fusion, Chicago, IL, USA, pp. 1–8.

[2] Ashari, A.E., Nikoukhah, R. and Campbell, S.L. (2012). Active robust fault detection in closed-loop systems: Quadratic optimization approach, IEEE Transactions on Automatic Control 57(10): 2532–2544.

[3] Bar-Shalom, Y. and Li, X. (1993). Estimation and Tracking: Principles, Techniques, and Software, Artech House, Boston, MA.

[4] Bar-Shalom, Y., Li, X.R. and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation, John Wiley Sons, New York, NY.

[5] Bertsekas, D.P. (2012). Dynamic Programming and Optimal Control, 4th Edn, Athena Scientific, Belmont, MA.

[6] Blackmore, L., Rajamanoharan, S. and Williams, B.C. (2008). Active estimation for jump Markov linear systems, IEEE Transactions on Automatic Control 53(10): 2223–2236.

[7] Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2016). Diagnosis and Fault-tolerant Control, 3rd Edn, Springer-Verlag, Berlin.

[8] Blom, H.A.P. and Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with Markovian switching coefficients, IEEE Transactions on Automatic Control 33(8): 780–783.

[9] Eide, P. and Maybeck, P.S. (1996). An MMAE failure detection system for the F-16, IEEE Transactions on Aerospace and Electronic Systems 32(3): 1125–1136.

[10] Gustafsson, F. (2009). Automotive safety systems, IEEE Signal Processing Magazine 26(4): 32–47.

[11] Hofbaur, M.W. and Williams, B.C. (2004). Hybrid estimation of complex systems, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 34(5): 2178–2191.

[12] Isermann, R. (2011). Fault-Diagnosis Applications, Springer, Heidelberg.

[13] Julier, S.J. and Uhlmann, J.K. (1997). A non-divergent estimation algorithm in the presence of unknown correlations, Proceedings of the 1997 American Control Conference, Albuquerque, NM, USA, Vol. 4, pp. 2369–2373.

[14] Katipamula, S. and Brambley, M.R. (2011). Methods for fault detection, diagnostics, and prognostics for building systems: A Review. Part II, HVAC Research 11(2): 169–187.

[15] Niemann, H. and Poulsen, N.K. (2014). Active fault detection in MIMO systems, Proceedings of the 2014 American Control Conference, Portland, OR, USA, pp. 1975–1980.

[16] Punčochář and Straka, O. (2019). Non-centralized active fault diagnosis for stochastic systems, 2019 American Control Conference, Philadelphia, PA, USA, pp. 5052–5057.

[17] Punčochář, I. and Šimandl, M. (2014). On infinite horizon active fault diagnosis for a class of non-linear non-Gaussian systems, International Journal of Applied Mathematics and Computer Science 24(4): 795–807, DOI: 10.2478/amcs-2014-0059.

[18] Punčochář, I., Širok´y, J. and Šimandl, M. (2015). Constrained active fault detection and control, IEEE Transactions on Automatic Control 60(1): 253–258.

[19] Raimondo, D.M., Marseglia, G.R., Braatz, R.D. and Scott, J.K. (2016). Closed-loop input design for guaranteed fault diagnosis using set-valued observers, Automatica 74: 107–117.

[20] Škach, J., Punčochář I. and Lewis, F.L. (2016). Optimal active fault diagnosis by temporal-difference learning, Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, USA, pp. 2146–2151.

[21] Straka, O. and Punčochář (2019). Decentralized and distributed active fault diagnosis for stochastic systems with indirect observations, 22nd International Conference on Information Fusion, Ottawa, Canada, pp. 1–8.

[22] Tslil, O., Aharon, O. and Carmi, A. (2018). Distributed estimation using particles intersection, Proceedings of the 21st International Conference on Information Fusion, Cambridge, UK, pp. 1653–1660.

[23] Vrabie, D., Vamvoudakis, K.G. and Lewis, F.L. (2013). Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles, 1st Edn, Institution of Engineering and Technology, London.

[24] Watanabe, K. and Tzafestas, S.G. (1993). Generalized pseudo-Bayes estimation and detection for abruptly changing systems, Journal of Intelligent and Robotic Systems 7(1): 95–112.

[25] Zhang, Y. and Li, X.-R. (1998). Detection and diagnosis of sensor and actuator failures using IMM estimator, IEEE Transactions on Aerospace and Electronic Systems 34(4): 1293–1313.