Change-Point Estimation of the Process in Poisson-based control charts Using Fuzzy Statistical Clustering Approach
2017
Hochschulschrift
Zugriff:
105
Every person pursuit high-quality in the recent years. The control chart is one of the important tools in maintaining stable process quality. How to effectively use the control chart to detect and solve the process variation is important. If we can accurately estimate the change point when the process variation is detected by control chart, it will help in finding the cause of variation. The Maximum likelihood estimate (MLE) is used in early researches of change point estimation. Alaeddini (2009) point out the limitations of the traditional MLE and proposed the fuzzy statistical clustering method instead of it. In this research, the fuzzy statistical clustering method is used to estimate the change point of c chart and Poisson EWMA chart. The results show that whether the process variation is step change or linear change, the fuzzy statistical clustering method performs good in average estimated change point and confidence interval on both charts.
Titel: |
Change-Point Estimation of the Process in Poisson-based control charts Using Fuzzy Statistical Clustering Approach
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Autor/in / Beteiligte Person: | Hsieh,Meng-Ying ; 謝孟穎 |
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Veröffentlichung: | 2017 |
Medientyp: | Hochschulschrift |
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