Integrating SPC and EPC for multivariate autocorrelated process

Lee-Ing Tong*, Chien Hui Yang, Cheng Yi Huang, Cheng Chi Shou


研究成果: Conference article同行評審

3 引文 斯高帕斯(Scopus)


Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling's T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process.

頁(從 - 到)692-696
期刊Proceedings of the International Conference on Electronic Business (ICEB)
出版狀態Published - 12月 2005
事件5th International Conference on Electronic Business, ICEB 2005 - Hong Kong, Hong Kong
持續時間: 5 12月 20059 12月 2005


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