| Summary: | 碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 96 === Generally speaking, Managers address themselves to monitor and adjust process in order to effectively enhance the quality of product. Statistical Process Control (SPC) and Engineer Process Control (EPC) have been widely applied. When the data is without attribution of self-correlated, integrating SPC and EPC can effectively discriminate the assignable causes and remove them. However, when the data is with attribution of self-correlated, there is a problem that integration of SPC and EPC may inform a false alarm. In this paper, the concept of clustering is applied to solve this problem. Considering the hierarchical property of observed data in real world, growing hierarchical self-organizing map (GHSOM) is adopted. The proposed model can discriminate the assignable causes and adjust abnormal status immediately. The experimental results reveal that our proposed method is effective and efficient for disturbance identification in correlated process data when disturbance is significant.
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