A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems
The work reported here provides a framework of diagnostic advisory system for improved operational availability in complex nuclear power plant systems. The rule-based approach typically used for conventional expert systems is abandoned in this work. This is because of the inability of rule-based approaches to properly model the inherent uncertainties and complexities of the relationships involved in the diagnosis of actual complex engineering systems. Rather, our advisory system employs Bayesian belief network (BBN) as a high-level reasoning tool for incorporating inherent uncertainty for use in probabilistic inference. We demonstrate that a rule-based knowledge representation is simply a special case of a general BBN. First, we outline a sequential algorithm to be used in formulating the BBN-based diagnostic operational advice. Then, a prototype BBN-based representation is encoded explicitly through topological symbols and links between them, oriented in a causal direction. Once new system state related evidence from an associated sensor network is entered into this advisory system, it provides an operational advice concerning how to maintain both operational availability and safety. Based upon the framework presented here, further development of our diagnostic maintenance network, integrating a comprehensive sensor network, can be expected to lead to substantial economic gains.
bayesian belief network nuclear power plants sequential inference algorithm operator interface module