Using Factored Bond Graphs for Distributed Diagnosis of Physical Systems

This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system bond graph (BG) into a set of structurally observable bond graph factors (BG-Fs). Each BG-F is systematically translated into a corresponding DBN Factor (DBN-F), which is then used in its corresponding local diagnoser for quantitative fault detection, isolation, and identification. By construction, the random variables in each DBN-F are conditionally independent of the random variables in all other DBN-Fs, given a subset of communicated measurements considered as system inputs. Each DBN-F and BG-F pair is used to derive a local diagnoser that generates globally correct diagnosis results by local analysis. Together, the local diagnosers diagnose all single faults of interest in the system. We demonstrate on an electrical system how our distributed diagnosis scheme is computationally more efficient than its centralized counterpart, but without compromising the accuracy of the diagnosis results.