A Comparison of Baysian Approaches to Learning in Fault Isolation

Fault isolation is the task of localizing faults in a process, given observations from it. To do this, a model describing the relations between faults and observations is needed. In this paper we focus on learning such models both from training data and from prior knowledge. There are several challenges in learning for fault isolation. The number of data and the available computing resources are often limited. Furthermore, there may be previously unobserved fault patterns. To meet these challenges we take on a Bayesian approach. We compare five different approaches to learning for fault isolation, and evaluate their performance on a real application, namely the diagnosis of an automotive engine.