Development of Bayesian diagnostic models using troubleshooting flow diagrams

Bayesian networks have recently become a modeling technique of choice for development of flexible, accurate, and complex diagnostic systems. These characteristics are obtained, however, at the significant cost of data and expert knowledge. It is often the case that a troubleshooting flow diagram, the most popular way of representing troubleshooting procedures, is already available for the system and can be used as a starting point for design of the Bayesian network. It turns out that conversion of the flow diagram into a Bayesian network is very similar to conversion into a diagnostic case base. We compare the case base and Bayesian network obtained by conversion with the original flow diagram, from the point of view of their diagnostic performance. We also describe a procedure for cost and time efficient enhancement of the original case base and Bayesian network. We discuss the sequencing algorithms necessary to use case bases and Bayesian networks in troubleshooting, with particular attention to decision tree and Value of Information based sequencing. We have used our design procedure in development of several complex diagnostic systems for troubleshooting of satellites, vehicles, and test equipment.