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

Abstract

A method for chemical process fault diagnosis using semiquantitative model generated behavior envelopes is described in this paper. The method generates a sequence of rules for each fault class, with any rule in a sequence valid within the bounds of its time interval. This can be viewed as a qualitative description of the trend of numerical sensor measurements. For each variable in each fault class two sequences of episodic fuzzy rules are automatically generated one for the lower and one for the upper numerical behavior envelope. The diagnostic system monitors a process via the measured sensors. The measurements are matched against the fuzzy rules for the current time in the rule base. In case of an overlapping region defined by behavior envelopes, the introduced distance and time based fault belief scaling allows 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 system detected the correct fault even in cases of nearly total overlapped fault regions bounded by behavior envelopes.

DOI: 10.1109/3468.759283

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Cite this paper

@article{zyurt1999SQFDiagSM, title={SQFDiag: semi-quantitative model-based fault monitoring and diagnosis via episodic fuzzy rules}, author={Ibrahim Burak {\"{O}zyurt and Lawrence O. Hall and Aydin K. Sunol}, journal={IEEE Trans. Systems, Man, and Cybernetics, Part A}, year={1999}, volume={29}, pages={294-306} }