Probabilistic Techniques for Diagnosis of Multiple Component Degradations

We present probabilistic techniques that make synergistic use of available process information for diagnosis and detection of component fault manifestation in a multicomponent system. We begin by describing the motivation for using probabilistic techniques for systems diagnostics and then define probabilistic expressions that embody the diagnostics knowledge of interest. We show that a combination of a Bayesian expression with the solution to the Chapman-Kolmogoroff equation contains the diagnostic information of interest while explicitly making use of available process information including plant data or measurements, mathematical system models, and individual component reliability data. Given these probabilistic expressions, we introduce a practical means of obtaining the necessary constituent probability density functions corresponding to feasible component transitions via an adaptive Kalman filtering formulation. To demonstrate the consolidated probabilistic technique, we consider a low-order model of a balance of plant of a boiling water reactor, represented by 11 system variables, 9 component characteristics, and 5 observations. We simulate 5 to 10% degradations in two components subject to 1% signal noise in two different transient events. Our test calculations indicate that the proposed algorithm is able to provide correct fault detection and diagnosis of the faulted components and fault magnitudes, together with a rank-ordered likelihood of the binary faults.