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