Diagnosis and prognosis methodologies have used dynamic Bayesian networks (DBNs) to fuse many types of information. These methodologies, however, fuse problem- specific information and focus only on a subset of information types. By using only a subset of information, the interactions between or individual behaviors of subsystems, components, and faults may not be fully realized. In this paper, a general framework for system level diagnosis and prognosis of a mechanical system in the presence of heterogeneous information using dynamic Bayesian network (DBN) is proposed. Due to their ability to fuse heterogeneous information — information in a variety of formats from various sources — and give a probabilistic representation of a system, DBNs provide a platform naturally suited for diagnosis and uncertainty quantification. In the proposed methodology, a DBN is first constructed via an established machine learning algorithm from heterogeneous information. The DBN is then used to track the system and detect faults by monitoring the Bayes’ factor of the system state estimate. When a fault occurs, the underlying system model changes, and the Bayes’ factor of the DBN system model decreases. The state estimate provided by tracking indicates the most likely fault scenario and quantifies the diagnosis uncertainty. Estimation of remaining useful life and quantification of uncertainty in prognosis can then proceed based upon the diagnosis results. The proposed methodology is then demonstrated using a cantilever beam example with a possible loose bolt at the connection or a crack in the middle of the span.
How to Cite
diagnosis, particle filtering, prognosis, DBN
Boyen, X., & Koller, D. (1998). Tractable inference for complex stochastic processes. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, UAI’98 (pp. 33–42). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Retrieved from http://dl.acm.org/citation.cfm?id=2074094.207409 9
Camci, F., & Chinnam, R. B. (2005). Dynamic Bayesian networks for machine diagnostics: hierarchical hidden Markov models vs. competitive learning. 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN ’05. Proceedings (Vol. 3, pp. 1752– 1757 vol. 3). Presented at the 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN ’05. Proceedings, IEEE. doi:10.1109/IJCNN.2005.1556145
Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309–347. doi:10.1007/BF00994110
de Campos, L. M. (2006). A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests. J. Mach. Learn. Res., 7, 2149–2187.
Friedman, N. (1998). The Bayesian Structural EM Algorithm. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.24.1555
Friedman, N., Murphy, K., & Russell, S. (1998). Learning the Structure of Dynamic Probabilistic Networks. UAI’98 Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (pp. 139– 147). Morgan Kaufmann Publishers Inc. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.32.6615
Jha, S., Wenchao Li, & Seshia, S. A. (2009). Localizing transient faults using dynamic bayesian networks. High Level Design Validation and Test Workshop, 2009. HLDVT 2009. IEEE International (pp. 82– 87). Presented at the High Level Design Validation and Test Workshop, 2009. HLDVT 2009. IEEE International, IEEE. doi:10.1109/HLDVT.2009.5340170
Lauritzen, S. L. (1992). Propagation of Probabilities, Means, and V ariances in Mixed Graphical Association Models. Journal of the American Statistical Association, 87(420), 1098–1108. doi:10.2307/2290647
Lerner, U. (2001). Exact inference in networks with discrete children of continuous parents. in: J. Breese, D. Koller (Eds.), Uncertainty in Artificial Intelligence (pp. 319–328). Morgan Kaufmann.
McNaught, K. R., & Zagorecki, A. (2009). Using dynamic Bayesian networks for prognostic modelling to inform maintenance decision making. IEEE International Conference on Industrial Engineering and Engineering Management, 2009. IEEM 2009 (pp. 1155–1159). Presented at the IEEE International Conference on Industrial Engineering and Engineering Management, 2009. IEEM 2009, IEEE. doi:10.1109/IEEM.2009.5372973
Paris, P. C., Gomez, M. P., & Anderson, W. E. (1961). A Rational Analytic Theory of Fatigue. The Trend in Engineering, 13, 9–14.
Przytula, K. W., & Choi, A. (2008). An Implementation of Prognosis with Dynamic Bayesian Networks. 2008 IEEE Aerospace Conference (pp. 1–8). Presented at the 2008 IEEE Aerospace Conference, IEEE. doi:10.1109/AERO.2008.4526616
Ristic, B., & Arulampalam, S. (2004). Beyond the Kalman filter : particle filters for tracking applications. Boston, MA: Artech House.
Roychoudhury, I., Biswas, G., & Koutsoukos, X. (2006). A Bayesian approach to efficient diagnosis of incipient faults. IN PROC. 17TH INT. WORKSHOP PRINCIPLES OF DIAGNOSIS, 243– 250.
Sahin, F., Yavuz, M. Ç., Arnavut, Z., & Uluyol, Ö. (2007). Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Computing, 33(2), 124–143. doi:16/j.parco.2006.11.005
Sankararaman, S., & Mahadevan, S. (2011). Uncertainty quantification in structural damage diagnosis.
Structural Control and Health Monitoring, 18(8),807–824. doi:10.1002/stc.400
Shwe, M. A., Middleton, B., Heckerman, D. E., Henrion,M., Horvitz, E. J., Lehmann, H. P., & Cooper, G. F. (1991). Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base. Methods of Information in Medicine, 30, 241–55.
Straub, D. (2009). Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks. Journal of Engineering Mechanics, 135(10), 1089– 1099. doi:10.1061/(ASCE)EM.1943-7889.0000024
Vaswani, N. (2004). Bound on Errors in Particle Filtering with Incorrect Model Assumptions and Its Implication for Change Detection.
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.