Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

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Published Mar 26, 2021
Brian W. Ricks Ole J. Mengshoel

Abstract

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

How to Cite

W. Ricks, B., & J. Mengshoel, O. . (2021). Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1594
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Keywords

diagnosis, probability of detection

References
(Bickmore, 1992) T. W. Bickmore. A Probabilistic Approach to Sensor Data Validation, In Proceedings of the 28th Joint Propulsion Conference and Exhibit, (Nashville, TN), 1992.
(Button and Chicatelli, 2005) R. M. Button and A. Chicatelli. Electrical Power System Health Management. In Proceedings of the 1st International Forum on Integrated System Health Engineering and Management in Aerospace, (Napa, CA), 2005.
(Bunus et al., 2009) Peter Bunus, Olle Isaksson, Beate Frey, Burkhard Münker. RODON - A Model-Based Diagnosis Approach for the DX Diagnostic Competition. In Proceedings of 20th International Workshop on Principles of Diagnosis (DX-09), (Stockholm, SE), pp. 423-430, 2009.
(Chavira and Darwiche, 2007) M. Chavira and A. Darwiche. Compiling Bayesian Networks using Variable Elimination. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), (Hyderabad, India), pp. 2443-2449, 2007.
(Chien et al., 2002) C. Chien, S. Chen, and Y. Lin. Using Bayesian Networks for Fault Location on Distribution Feeder. IEEE Transactions on Power Delivery, vol. 17, pp. 785-793, 2002.
(Daigle et al., 2008) M. Daigle, X. Koutsoukos, and G. Biswas. An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems. In Proceedings of 11th International Workshop on Hybrid Systems: Computation and Control (HSCC-08), (St. Louis, MO), pp. 614–617, 2008.
(Darwiche, 2000) A. Darwiche. Model-Based Diagnosis under Real-World Constraints. AI Magazine, vol. 21, no. 2, pp. 57-73, 2000.
(Darwiche, 2003) A. Darwiche. A Differential Approach to Inference in Bayesian Networks. Journal of the ACM, vol. 50, no. 3, pp. 280-305, 2003.
(Gorinevsky et al., 2009) D. Gorinevsky, S. Boyd, and S. Poll. Estimation of Faults in DC Electrical Power System. In Proceedings of the American Control Conference, 2009.
(de Kleer and Williams, 1987) J. de Kleer and B. C. Williams. Diagnosing Multiple Faults, Artificial Intelligence, 32(1), pp. 97-130, 1987.
(Koller and Lerner, 2000) D. Koller and U. Lerner. Sampling in Factored Dynamic Systems. In Sequential Monte Carlo Methods in Practice, 2000.
(Kurtoglu et al., 2008) T. Kurtoglu, O. J. Mengshoel, and S. Poll. A framework for systematic benchmarking of monitoring and diagnostic systems. In Annual Conference of the Prognostics and Health Management Society (PHM-08), 2008.
(Kurtoglu et al., 2009a) T. Kurtoglu, S. Narasimhan, S. Poll, D. Garcia, L. Kuhn, J. de Kleer, A. van Gemund, and A. Feldman. First International Diagnosis Competition – DXC’09. In Proceedings of 20th International Workshop on Principles of Diagnosis (DX-09), (Stockholm, SE), pp. 383–396, 2009.
(Kurtoglu et al., 2009b) T. Kurtoglu, S. Narasimhan, S. Poll, D. Garcia, L. Kuhn, J. de Kleer, A. van Gemund and A. Feldman. Towards a Framework for Evaluating and Comparing Diagnosis Algorithms. In Proceedings of the 20th International Workshop on Principles of Diagnosis (DX-09), (Stockholm, SE), pp. 373–382, 2009.
(Lauritzen and Spiegelhalter, 1988) S. Lauritzen and D. J. Spiegelhalter. Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems (with Discussion). Journal of the Royal Statistical Society series B, vol. 50, no. 2, pages 157- 224, 1988.
(Lerner et al., 2000) U. Lerner, R. Parr, D. Koller, and G. Biswas. Bayesian fault detection and diagnosis in dynamic systems. In Proceedings of The Seventeenth National Conference on Artificial Intelligence (AAAI- 00), pp. 531–537, 2000.
(Liu and Zhang, 2002) E. Liu and D. Zhang. Diagnosis of Component Failures in Space Shuttle Main Engines using Bayesian Belief Networks: A Feasibility Study. In Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-02), (Washington D.C.), 2002.
(Mengshoel et al., 2006) O. J. Mengshoel, D. C. Wilkins and D. Roth. Controlled Generation of Hard and Easy Bayesian Networks: Impact on Maximal Clique Tree in Tree Clustering. Artificial Intelligence (170), pp. 1137- 1174, 2006.
(Mengshoel, 2007) O. J. Mengshoel. Designing Resource- Bounded Reasoners Using Bayesian Networks: System Health Monitoring and Diagnosis. In Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), (Nashville, TN), pp. 330-337, 2007.
(Mengshoel et al., 2008) O. J. Mengshoel, A. Darwiche, K. Cascio, M. Chavira, S. Poll, and S. Uckun. Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft. In Proceedings of the Twentieth Innovative Applications of Artificial Intelligence Conference (IAAI- 08), (Chicago, IL), pp. 1699-1705, 2008.
(Mengshoel et al., 2009) O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study. Accepted for publication in IEEE Transactions on Systems, Man and Cybernetics- Part A: Systems and Humans, 2009.
(Narasimhan and Biswas 2007) S. Narasimhan and G. Biswas. Model-Based Diagnosis of Hybrid Systems. IEEE Transactions on Systems, Man and Cybernetics- Part A: Systems and Humans, 37(3): pp. 348-361, 2007.
(Olesen, 1993) K. G. Olesen. Causal Probabilistic Networks with Both Discrete and Continuous Variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(3), pp. 275-279, 1993.
(Pearl, 1988) J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann, 1988.
(Poll et al., 2007) S. Poll, A. Patterson-Hine, J. Camisa, D. Garcia, D. Hall, C. Lee, O. J. Mengshoel, C. Neukom, D. Nishikawa, J. Ossenfort, A. Sweet, S. Yentus, I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos. Advanced Diagnostics and Prognostics Testbed. In Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), (Nashville, TN), pp. 178-185, 2007.
(Ricks and Mengshoel, 2009) B. Ricks and O. J. Mengshoel. The diagnostic challenge competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems. In Proceedings of 20th International Workshop on Principles of Diagnosis (DX-09), (Stockholm, SE), pp. 415–422, 2009.
(Yongli et al., 2006) Z. Yongli, H. Limin, and L. Jinling. Bayesian Network-Based Approach for Power System Fault Diagnosis. IEEE Transactions on Power Delivery, vol. 21, pp. 634-639, 2006.
Section
Technical Papers