Distributed Diagnosis of Dynamic Systems Using Dynamic Bayesian Networks ?

This paper presents a Dynamic Bayesian Network (DBN)-based distributed diagnosis scheme, where each distributed diagnoser generates globally correct diagnosis results without a centralized coordinator by communicating a minimal number of measurements so that each diagnoser satisfies local observability properties, and the overall diagnoser is globally observable. We present a procedure for designing the distributed diagnosers by factoring a system DBN into the maximal number of smaller DBN Factors (DBN-Fs) that are conditionally independent of other DBN-Fs, given the communicated measurements. Since each conditionally independent DBN-F is observable, Bayesian inference schemes can be applied to each factor independently for distributed tracking of system behavior for isolation and identification of faults without loss of accuracy. We prove that each local diagnoser guarantees globally correct diagnosis results, and present some experimental results for an electrical circuit to demonstrate the efficacy of our diagnosis scheme.