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.
Original language | English (US) |
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Pages (from-to) | 659-669 |
Number of pages | 11 |
Journal | IIE Transactions (Institute of Industrial Engineers) |
Volume | 39 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2007 |
Keywords
- Classification
- Control chart
- False alarm
- Random forest
- Regularization
- Supervised learning
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering