MALFUNCTION DIAGNOSIS USING QUANTITATIVE MODELS WITH NON-BOOLEAN REASONING IN EXPERT SYSTEMS

An approach to chemical plant fault diagnosis is presented that utilizes patterns of violation and satisfaction of the quantitative constraints governing the process. Process knowledge consists of a list of the operational constraints on the plant together with sufficient conditions for violation of each constraint. Interpretation of the pattern of constraint violations is treated by Boolean and non-Boolean techniques. It is shown that non-Boolean reasoning techniques increase the stability and sensitivity of the diagnosis in the presence of noise. The techniques introduced in this paper are easily implemented in rule-based expert systems using certainty factors.