Journal
JOURNAL OF INTELLIGENT MANUFACTURING
Volume 12, Issue 1, Pages 57-64Publisher
KLUWER ACADEMIC PUBL
DOI: 10.1023/A:1008903614042
Keywords
fuzzy reasoning; knowledge acquisition; diagnosis system; process control; (X)over-bar control chart
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This paper describes a new diagnosis system, which is based on fuzzy reasoning to monitor the performance of a discrete manufacturing process and to justify the possible causes. The diagnosis system consists chiefly of a knowledge bank and a reasoning mechanism. The knowledge bank provides knowledge of the membership functions of unnatural symptoms that are described by Nelson's rules on (X) over bar control charts and knowledge of cause-symptom relations. We develop an approach called maximal similarity method (MSM) for knowledge acquisition to construct the fuzzy cause-symptom relation matrix. Through the knowledge bank, the diagnosis system can first determine the degrees of an observation fitting each unnatural symptom. Then, using the fuzzy cause-symptom relation matrix, we can diagnose the causes of process instability. In conclusion we provide a numerical example to illustrate the system.
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