Monitoring multivariate process variability by an exponentially weighted moving average control chart
碩士 === 雲林科技大學 === 工業工程與管理研究所碩士班 === 96 === Monitoring process variability is an important issue in statistic process control (SPC). For a multivariate process, the process variability is usually monitored by standard |S| control chart based on the determinant of the average of sample covariance matr...
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/74803741394135386740 |
| Summary: | 碩士 === 雲林科技大學 === 工業工程與管理研究所碩士班 === 96 === Monitoring process variability is an important issue in statistic process control (SPC). For a multivariate process, the process variability is usually monitored by standard |S| control chart based on the determinant of the average of sample covariance matrices. However, it is sensitive only to moderate to large shift in the process variability. In this paper, we apply the exponentially weighted moving average (EWMA) control chart to monitor the multivariate process variability. The simulation shows that the proposed control chart outperforms existing |S| control chart in detecting the shift in variation.
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