Abstract
This paper described a probabilistic knowledge-based fault diagnosis system implemented on the recausticizing section of a pulp and paper mill. Knowledge is represented by Possible Cause and Effect Graph methodology, which is an enhanced Signed Digraph approach. Probability is calculated by the incorporation of a Bayesian belief network into Possible Cause and Effect Graph methodology. In addition to the capability of handling multiply connected nodes, cyclic loops and delayed alarms, the system also includes an algorithm for adaptive conditional probability update. Human feedback is used to determine if the diagnosis is "good" or "bad" and conditional probability of the exogenous causes will be either "rewarded" or "penalized" accordingly.
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