A MultilogicProbabilistic Signed Directed Graph FaultDiagnosis Approach Based on Bayesian Inference.

Signed directed graph (SDG), as awidely applied fault diagnosisapproach, is unable to express complicated logic relations other thanlogic OR and usually results in spurious interpretations. To solvethe problem, a semiquantitative fault diagnosis approach based onthe model of multilogic probabilistic SDG (MPSDG) with Bayesian inferenceis proposed. The MPSDG model introduces the logic gates to describemultiple logic causalities between process variables, and the prioriprobabilistic parameters in MPSDG are decided by the historical malfunctionfrequencies and the deviation of variables. When a failure occurs,the backtracking algorithm using the consistent rule is conductedimmediately, and the posterior probabilities of each searched faultare computed and sorted by a set of Bayesian inference mechanisms.Thus, the real reason is further distinguished. This MPSDG based faultdiagnosis approach is applied to two examples: a continuous stirredtank heater (CSTH) process and a Tennessee Eastman (TE) process. Theexperimental results demonstrate that the proposed approach is superiorto the conventional SDG approach and can diagnose the production faultsmore accurately. [ABSTRACT FROM AUTHOR]