Bayesian networks, which may be compiled to arithmetic circuits in the interest of speed and predictability, provide a probabilistic method for system fault diagnosis. Currently, there is a limitation in arithmetic circuits in that they can only represent discrete random variables, while important fault types such as drift and offset faults are continuous and induce continuous sensor data. In this paper, we investigate how to handle continuous behavior while using discrete random variables with a small number of states. Central in our approach is the novel integration of a method from statistical quality control, known as cumulative sum (CUSUM), with probabilistic reasoning using static arithmetic circuits compiled from static Bayesian networks. Experimentally, an arithmetic circuit model of the ADAPT Electrical Power System (EPS), a real- world EPS located at the NASA Ames Research Center, is considered. We report on the validation of this approach using PRODIAGNOSE, which had the best performance in three of four industrial track competitions at the International Workshop on Principles of Diagnosis in 2009 and 2010 (DXC-09 and DXC-10). We demonstrate that PRODIAGNOSE, augmented with the CUSUM technique, is successful in diagnosing faults that are small in magnitude (offset faults) or drift linearly from a nominal state (drift faults). In one of these experiments, detection accuracy dramatically improved when CUSUM was used, jumping from 46.15% (CUSUM disabled) to 92.31% (CUSUM enabled).
How to Cite
Bayesian reasoning, fault diagnosis, Bayesian inference, probabilistic, ProDiagnose, fault diagnostics, arithmetic circuit, CUSUM, drift, offset, monitoring change, hybrid, statistical quality control
Chavira, M., & Darwiche, A. (2007). Compiling Bayesian Networks Using Variable Elimination. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07) (p. 2443-2449). Hyder- abad, India.
Choi, A., Darwiche, A., Zheng, L., & Mengshoel, O. J. (2011). Data Mining in Systems Health Management: Detection, Diagnostics, and Prognostics. In A. Srivas- tava & J. Han (Eds.),
(chap. A Tutorial on Bayesian Networks for System Health Management). Chapman and Hall/CRC Press.
Darwiche, A. (2003). A Differential Approach to Inference in Bayesian Networks. Journal of the ACM, 50(3), 280– 305.
Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks. Cambridge, UK: Cambridge University Press.
Dechter, R. (1999). Bucket Elimination: A Unifying Framework for Reasoning. Artificial Intelligence, 113(1-2), 41-85. Available from citeseer.nj.nec.com/ article/dechter99bucket.html
Kozlov, A., & Koller, D. (1997). Nonuniform Dynamic Discretization in Hybrid Networks. In In Proc. UAI (pp. 314–325). Morgan Kaufmann.
Kurtoglu, T., Feldman, A., Poll, S., deKleer, J., Narasimhan, S., Garcia, D., et al. (2010). Second International Diagnostic Competition (DXC10): In- dustrial Track Diagnostic Problem Descriptions (Tech. Rep.). NASA ARC and PARC. Available from http://www.phmsociety.org/ competition/dxc/10/files
Kurtoglu, T., Narasimhan, S., Poll, S., Garcia, D., Kuhn, L., deKleer, J., et al. (2009, June). First International Diagnosis Competition - DXC’09. In Proc. of the Twentieth International Workshop on Principles of Diagnosis (DX’09) (pp. 383–396). Stockholm, Sweden.
Langseth, H., Nielsen, T. D., Rum ́ı, R., & Salmeron, A. (2009). Inference in hybrid Bayesian networks. Re- liability Engineering & System Safety, 94(10), 1499– 1509.
Lauritzen, S., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems (with discussion). Journal of the Royal Statistical Society series B, 50(2), 157– 224.
Lerner, U., Parr, R., Koller, D., & Biswas, G. (2000). Bayesian Fault Detection and Diagnosis in Dynamic Systems. In Proceedings of the Seventeenth national Conference on Artificial Intelligence (AAAI-00) (p. 531-537). Available from citeseer.ist.psu .edu/lerner00bayesian.html
Mengshoel, O. J. (2007). Designing Resource-Bounded Rea- soners using Bayesian Networks: System Health Monitoring and Diagnosis. In Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07) (pp. 330–337). Nashville, TN.
Mengshoel, O. J., Chavira, M., Cascio, K., Poll, S., Darwiche, A., & Uckun, S. (2010). Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study. IEEE Trans. on Systems, Man, and Cybernetics, 40(5), 874– 885.
Mengshoel, O. J., Poll, S., & Kurtoglu, T. (2009). Develop- ing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft. In Proc. of the IJCAI-09 Workshop on Self-⋆ and Autonomous Systems (SAS): Reasoning and Integration Challenges.
Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41, 100 - 115.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.
Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., et al. (2007). Advanced Diagnostics and Prognostics Testbed. In Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07) (pp. 178–185). Nashville, TN.
Ricks, B. W., & Mengshoel, O. J. (2009a). The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems. In Proc. of the 20th International Workshop on Principles of Diagnosis (DX-09). Stockholm, Sweden.
Ricks, B. W., & Mengshoel, O. J. (2009b). Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study. In Proc. of Annual Conference of the PHM Society, 2009 (PHM-09). San Diego, CA.
Ricks, B. W., & Mengshoel, O. J. (2010). Diagnosing Intermittent and Persistent Faults using Static Bayesian Networks. In Proc. of the 21st International Workshop on Principles of Diagnosis (DX-10). Portland, OR.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.