Synthesis and optimization of a Bayesian belief network based observation platform for anomaly detection under partial and unreliable observations

Complex engineering systems, such as nuclear processing systems, need to be closely monitored to meet given operational requirements. Previous work has developed diagnosers for detecting and counting occurrences of anomaly patterns (e.g., physical faults, facility misuse) in such systems within discrete event dynamic system (DEDS) framework. This work illustrates the application of this general methodology for the design and optimization of a diagnoser based on Bayesian belief networks (BBNs). Two advantages of this approach are as follows. The first is that current monitoring implementations using BBNs, which is popular in the industry, can be easily expanded and optimized based on the BBN-based diagnosers developed here. The second is that BBN-based diagnosers for tracking anomaly patterns do not require as much computer memory and computation effort as DEDS-based diagnosers. For the BBN-based diagnosers designed here, an optimization problem for finding a sensor configuration that balances sensor cost and diagnoser performance is formulated and solved. Simulation results show that a BBN-based diagnoser performs well in detecting and counting the occurrences of anomalies, while sensor configuration optimization results indicate that improved sensor configurations can be found such that sensor cost is significantly reduced while maintaining acceptable monitoring performance.