Asset-specific bayesian diagnostics in mixed contexts

In this paper we build upon previous work to examine the efficacy of blending probabilities in asset-specific classifiers to improve diagnostic accuracy for a fleet of assets. In previous work we also introduced the idea of using split probabilities. We add environmental differentiation to asset differentiation in the experiments and assume that data is acquired in the context of online health monitoring. We hypothesize that overall diagnostic accuracy will be increased with the blending approach relative to the single aggregate classifier or split probability asset-specific classifiers. The hypothesis is largely supported by the results. Future work will concentrate on improving the blending mechanism and working with small data sets.