SQFDiag: Semiquantitative model-based fault monitoring and diagnosis via episodic fuzzy rules

A method for chemical process fault diagnosis using semiquantitative model generated behavior envelopes is described in this paper. The method generat es a sequence of rules for each fault class, with any rule in a sequence va lid within the bounds of its time interval. This can be viewed as a qualita tive description of the trend of numerical sensor measurements. For each va riable in each fault class two sequences of episodic fuzzy rules are automa tically generated one for the lower and one for the upper numerical behavio r envelope. The diagnostic system monitors a process via the measured senso rs. The measurements are matched against the fuzzy rules for the current ti me in the rule base. In case of an overlapping region defined by behavior e nvelopes, the introduced distance and time based fault belief sealing allow s ranking of fault candidates. A novel abnormal situation will not pass the introduced system undetected due to a novel class detection mechanism. The diagnostic performance of the system is shown in two case studies. The sys tem detected the correct fault even in cases of nearly total overlapped fau lt regions: bounded by behavior envelopes.