Abstract
Canonical correlation analysis (CCA) has been used for concurrent quality and process monitoring to extract multidimensional correlation structure between process and quality variables. In this paper, a new kernel concurrent CCA (KCCCA) algorithm is proposed for quality-relevant nonlinear process monitoring, which decomposes the original space into five subspaces, including correlation subspace, quality-principal subspace, quality-residual subspace, process-principal subspace and process-residual subspace. The proposed KCCCA considers the nonlinearity in both process and quality variables, and incorporates a regularization term as well for numerical robustness. In the case studies, the Tennessee Eastman process is employed to demonstrate the effectiveness of the proposed KCCCA.
Original language | English |
---|---|
Title of host publication | 2017 American Control Conference, ACC 2017 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 5404-5409 |
Number of pages | 6 |
ISBN (Electronic) | 9781509059928 |
ISBN (Print) | 9781509045839 |
DOIs | |
Publication status | Published - May 2017 |
Externally published | Yes |
Event | 2017 American Control Conference, ACC 2017 - Seattle, United States Duration: 24 May 2017 → 26 May 2017 |
Publication series
Name | Proceedings of the American Control Conference |
---|---|
Publisher | Institute of Electrical and Electronics Engineers |
ISSN (Print) | 0743-1619 |
ISSN (Electronic) | 2378-5861 |
Conference
Conference | 2017 American Control Conference, ACC 2017 |
---|---|
Country/Territory | United States |
City | Seattle |
Period | 24/05/17 → 26/05/17 |
Bibliographical note
Publisher Copyright:© 2017 American Automatic Control Council (AACC).