Distributional Smoothing in Bayesian Fault Diagnosis

Previously, we demonstrated the potential value of constructing asset-specific models for fault diagnosis. We also examined the effects of using split probabilities, where prior probabilities come from asset-specific statistics and likelihoods from fleet-wide statistics. In this paper, we build upon that work to examine the efficacy of smoothing probability distributions between asset-specific and fleet-wide distributions to further improve diagnostic accuracy. In the current experiments, we also add environmental differentiation to asset differentiation under the assumption that data are acquired in the context of online health monitoring. We hypothesize that the overall diagnostic accuracy will be increased with the smoothing approach relative to a fleet-wide model or a set of asset-specific models. The hypothesis is largely supported by the results. Future work will concentrate on improving the smoothing mechanism and in the context of small data sets.