| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 92 === Abstract
The most popular multivariate control chart is the one based on Hotelling’s T 2 statistic. This is partially due to the fact that it is the multivariate analogue of the univariate Shewhart charting statistic and is easy to operate. Though the T 2 control chart is popular and powerful, a high false alarm rate, often seen in practice, has hindered the implementation and effective use of multivariate T 2 control charts. The high false alarm rate is usually due to two main reasons: 1. unlike univariate charts, one can’t adjust the detection sensitivities for individual variables, and 2. representative, sufficient sample observations across different PM (Preventive Maintenance) cycles, recipes and/or process chambers are difficult to collect. If the prior knowledge obtained from engineering experience can provide information on the variable criticality and how the data normally behave across different PM cycles, recipes and/or process chambers, then by accommodating the prior knowledge into T 2 control chart building the number of false alarms should be reduced effectively. In this research, we will propose methodologies to build a robust T 2 control chart, named knowledge-based T 2 control chart, by including the engineering knowledge in the chart building. The knowledge-based T2 control chart is also applied to semiconductor equipment monitoring. The result shows that the number of meaningless alarms can be reduced significantly.
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