Multivariate statistical process control with artificial contrasts |
| |
Authors: | Wookyeon Hwang George Runger Eugene Tuv |
| |
Affiliation: | a Department of Industrial Engineering, Arizona State University, Tempe, AZ, USAb Intel Corporation, Analysis Control Technology, Chandler, AZ, USA |
| |
Abstract: | A multivariate control region can be considered to be a pattern that represents the normal operating conditions of a process. Reference data can then be generated and used to learn the difference between this region and random noise. Then multivariate statistical process control can be converted to a supervised learning task. This can dramatically reshape the control region and open the control problem to a rich collection of supervised learning tools. Such tools provide generalization error estimates that can be used to specify error rates. The effectiveness of such an approach is shown here. Such a computational approach is now easily accomplished with modern computing resources. Examples use random forests and a regularized least squares classifier as the learners. |
| |
Keywords: | Control chart classification supervised learning random forest regularization false alarm |
本文献已被 InformaWorld 等数据库收录! |