Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A method for chemical process fault diagnosis using semi-quantitative model generated behavior envelopes is described. 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 vied 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 lor 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 the case of an overlapping region defined by behavior envelopes, the distance introduced 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. In two case studies, the system detected the correct fault even in cases of nearly total overlapped fault regions bounded by behavior envelopes.
['Ibrahim Burak Özyurt', 'Lawrence O. Hall', 'Aydin K. Sunol']
I am writing a research paper on a idea SQFDiag: semi-quantitative model-based fault monitoring and diagnosis via episodic fuzzy rules
[{"from": "human", "value": "I am writing a research paper on a idea SQFDiag: semi-quantitative model-based fault monitoring and diagnosis via episodic fuzzy rules"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A method for chemical process fault diagnosis using semi-quantitative model generated behavior envelopes is described. 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 vied 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 lor 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 the case of an overlapping region defined by behavior envelopes, the distance introduced 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. In two case studies, the system detected the correct fault even in cases of nearly total overlapped fault regions bounded by behavior envelopes."}]