A Bayesian Approach for Maintenance Action Recommendation



Published Nov 1, 2020
Vassilis Katsouros Vassilis Papavassiliou Christos Emmanouilidis


This paper presents a Bayesian approach for maintenance action recommendation tested on the PHM 2013 Data Challenge dataset. The Challenge focused on maintenance action recommendation based on historical cases and the algorithms were evaluated on their ability to recommend confirmed problem types. The proposed approach is based on a Bayesian inference methodology and deals with recommending an already known problem type for each case. The recommender can be viewed as a classifier among the confirmed problem types. For each such problem type class the a priori probabilities for the events which characterize the problem type from the training data are estimated. When testing cases are presented, the recommender calculates the a posteriori probabilities for each of the confirmed problem types and suggests the type of problem that corresponds to the maximum a posteriori (MAP) probability.

Abstract 32 |



diagnostics, Bayesian inference, PHM data challenge, Maintenance action recommendation

Eker, OF., Camci, F., and Jennions, IK., (2012), Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets, Proc. of PHM 2012, First European Conference of the Prognostics and Health Management Society 2012, 3-5 Jul 2012, Dresden, Germany.
Elnahrawy E. and Nath B. (2004). Context-Aware Sensors. H. Karl, A. Willig, A. Wolisz (Eds.): EWSN 2004, LNCS 2920, pp. 77–93. doi: 10.1007/978-3-540-24606-0_6
Karandikar, J. M., Abbas, A. & Schmitz, T. L. (2013). Remaining useful tool life predictions in turning using Bayesian inference. International Journal of Prognostics and Health Management, vol. 4 (2) 025, pages 11.
Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Cambridge, Massachusetts, USA.
Mehta, P., Werner, A., and Mears, L, (2013), Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion, Journal of Intelligent Manufacturing, (in press), doi 10.1007/s10845-013-0787-1.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Morello, B., Zerhouni, N., and Varnier, C., (2012), PRONOSTIA: An Experimental Platform for Bearings Accelerated Life Test, IEEE International Conference on Prognostics and Health Management, IEEE PHM 2012, June, 2012, Denver, CO, USA.
Saha, B. & Goebel, K. (2008). Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques. Proceedings of IEEE Aerospace Conference., March 1-8, Big Sky, MO. doi: 10.1109/AERO.2008.4526631
Zhou, Y., Bo, J., Jie, Z., and Mingwei, G., (2013), Performance Metrics Assessment Method on Aircraft Prognostics and Health Management, Proc. of ICMTMA 2013, The Fifth International Conference on Measuring Technology and Mechatronics, 16-17 Jan 2013, Hong, Kong. pp. 799-802, doi: 10.1109/ICMTMA.2013.199
Technical Papers