| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 89 === Control charts are widely used for both manufacturing and service industries. Although traditional Shewhart chart is
a very simple tool, it is insensitive to small shifts in the process mean. Cumulative sum (CUSUM) charts are known to be very sensitive in detecting small shifts in the mean. On the implementation of CUSUM charts, a set of parameters has to be determined in advance. However, it is very difficult to determine these parameters when future shifts are unknown.
The multiple CUSUM modeled by applying several standard CUSUM charts simultaneously, is more robust at signaling a wide
range of process shifts. The multiple CUSUM will improve the conventional CUSUM while requiring little additional
computational effort.
In this research, we propose a neural network as an alternative approach to the multiple CUSUM charts when monitoring the shift in the process mean. The focus is on the generation and configuration of the training data set. The performance of neural network was evaluated by estimating the average run lengths (ARL''s) using simulation. The results obtained with simulated data suggest that control scheme based on neural network is significantly more sensitive to process shifts than
the multiple CUSUM charts.
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