TY - JOUR
T1 - Integrating SPC and EPC for multivariate autocorrelated process
AU - Tong, Lee-Ing
AU - Yang, Chien Hui
AU - Huang, Cheng Yi
AU - Shou, Cheng Chi
PY - 2005/12
Y1 - 2005/12
N2 - 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.
AB - 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.
KW - Autocorrelation
KW - Engineering process control
KW - Group method of data handling
KW - Hotelling's T control chart
KW - Multivariate cumulative sum control chart
KW - Multivariate process
KW - Statistical process control
UR - http://www.scopus.com/inward/record.url?scp=84873536378&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84873536378
SN - 1683-0040
SP - 692
EP - 696
JO - Proceedings of the International Conference on Electronic Business (ICEB)
JF - Proceedings of the International Conference on Electronic Business (ICEB)
T2 - 5th International Conference on Electronic Business, ICEB 2005
Y2 - 5 December 2005 through 9 December 2005
ER -