Learning temporal Bayesian networks for power plant diagnosis

Diagnosis in industrial domains is a complex problem because it includes uncertainty management and temporal reasoning. Dynamic Bayesian Networks (DBN) can deal with this type of problem, however they usually lead to complex models. Temporal Nodes Bayesian Networks (TNBNs) are an alternative to DBNs for temporal reasoning that result in much simpler and efficient models in certain domains. However, methods for learning this type of models from data have not been developed. In this paper we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method has three phases: (i) obtain an initial interval approximation, (ii) learn the network structure based on the intervals, and (iii) refine the intervals for each temporal node. The number of possible sets of intervals is obtained for each temporal node based on a clustering algorithm and the set of intervals that maximizes the prediction accuracy is selected. We applied this method to learn a TNBN for diagnosis and prediction in a combined cycle power plant. The proposed algorithm obtains a simple model with high predictive accuracy.