碩士 / 國立雲林科技大學 / 工業工程與管理系 / 105 / Control charts are the most popular process tools designed to determine whether a process is in control or not. When control chart generates an out-of- control signal, it means that the disturbance is present in the process, so the engineers should identify the time of assignable cause occurred; this moment is called the change point. Often however, the point in time at which signal is not actually the true change point. Thus, if the change point can identify quickly, it is possible to reduce the amount of time spent by engineers searching for the time of the assignable cause. Common change types are step change, linear change and multi-step change, etc. Assuming the change type is known, the Fuzzy shift change-point (FSCP) and Fuzzy-Statistical Clustering (FSC) estimators can effectively estimate the change points of in X-bar control chart. In practice, the change type is unknown in advance. Therefore, this study applies logistic regression model to FSCP and FSC methods to identify the change type is step change or linear change. Then, the FSCP estimators are employed to identify the process change point. The performance of the FSCP is discussed in comparison with the Fuzzy-Statistical Clustering (FSC) estimator. The results demonstrate that the proposed method offers an accurate estimate of the process change point and identify change type.
| Identifer | oai:union.ndltd.org:TW/105YUNT0031024 |
| Date | January 2017 |
| Creators | TSENG, LIN-YOU, 曾林右 |
| Contributors | CHIU, JING-ER, 邱靜娥 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 69 |
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