Construction of Bayesian networks for diagnostics

Bayesian networks have been proposed by many authors as the modeling technique of choice for the development of diagnostic systems. This paper describes a procedure for efficient creation of Bayesian networks for diagnostics. We have applied this procedure in diagnostic systems for diesel locomotives, satellite communication systems, and satellite testing equipment. We divide the process into several phases: problem decomposition, sub-problem definition, design and testing of a Bayesian networks for subsystems, and finally integration into a complete Bayesian network, We describe all of the steps of the network design process, especially details of knowledge acquisition and the integration of information from different sources. We develop the networks starting with the simplest forms of Bayesian networks, increasing their complexity as needed while carefully balancing model accuracy and knowledge acquisition cost