Multivariate statistical process control with artificial contrasts
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; false alarm; random forest; regularization; supervised learning
Document Type: Research Article
Affiliations: 1: Department of Industrial Engineering, Arizona State University, Tempe, AZ, USA 2: Intel Corporation, Analysis Control Technology, Chandler, AZ, USA
Publication date: 01 June 2007
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content