| Summary: | 碩士 === 國立交通大學 === 資訊科學系 === 91 === In recent years, defect detection problem of the workshop has become an important issue for manufacturing domain. In order to raise the quality of the products, the root cause of the low-quality situations should be found out as soon as possible.
In this thesis, the time issue problem for the manufacturing domain is formally modeled and defined. Accordingly, the manufacturing defect detection system using root cause evaluation function which can generate a ranked list of possible root causes for the given dataset is proposed. For the extensibility and reliability, some adaptive weights are embedded into the function. Besides, for the existing datasets with known root causes, a supervised learning approach using genetic algorithm and a contradiction analysis method using similarity measurement are proposed to learn the adaptive weights of our proposed evaluation functions and judge the quality of the given dataset. Finally, the experiments have been made and the results show the proposed method can ensure the efficiency and accuracy.
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