Abstract
Blind source separation recently has been investigated for blindly identifying variation patterns in multivariate manufacturing data, to aid in tracking down and eliminating root causes of manufacturing variation. Many different criteria can be used in blind separation algorithms, the performance and applicability of which depend on conditions that generally are not known a priori. We present a method for automatically combining the different criteria so as to directly minimize the mean squared estimation error. The resulting algorithm is more effective and more robust than counterparts that use other means of combining the criteria.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 332-343 |
| Number of pages | 12 |
| Journal | Technometrics |
| Volume | 50 |
| Issue number | 3 |
| DOIs | |
| State | Published - Aug 2008 |
Funding
This work was supported by National Science Foundation grants CMMI-0354824 and CMMI-0826081. The authors thank the anonymous referees and associate editor for their many helpful comments.
Keywords
- Blind source separation
- Factor rotation
- Manufacturing variation reduction
- Multivariate statistical process control
- Principal components analysis
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Applied Mathematics
