Abstract
Projection to latent structures (PLS) and concurrent PLS are approaches for solving quality-relevant process monitoring. In this paper, a new approach called concurrent kernel PLS (CKPLS) is presented to detect faults comprehensively for nonlinear processes. The new model divides the nonlinear process and quality spaces into five subspaces: the co-varying, process-principal, process-residual, quality-principal, and quality-residual subspaces. The co-varying subspace reflects nonlinear relationship between quality variables and original process variables. The process-principal and process-residual subspaces reflect the principal variations and residuals, respectively, in the nonlinear process space. Further, the quality-principal and quality-residual subspaces reflect the principal variations and residuals, respectively, in the quality space. The proposed approach is demonstrated by a numerical simulation and an application of the Tennessee Eastman process.
| Original language | English |
|---|---|
| Article number | 7310889 |
| Pages (from-to) | 1129-1137 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Automation Science and Engineering |
| Volume | 13 |
| Issue number | 2 |
| Early online date | 28 Oct 2015 |
| DOIs | |
| Publication status | Published - Apr 2016 |
| Externally published | Yes |
Funding
This work was supported in part by the Natural Science Foundation of China under Grant 61304107, Grant 61490704, Grant 61573022, and Grant 61290323, the China Postdoctoral Science Foundation funded project under Grant 2013M541242, the International Postdoctoral Exchange Fellowship Program under Grant 20130020, the Fundamental Research Funds for the Central Universities under Grant N130408002 and N130108001, and the IAPI Fundamental Research Funds under Grant 2013ZCX04 and Grant 2013ZCX05.
Keywords
- Concurrent kernel projection to latent structures (CKPLS)
- nonlinear process monitoring
- process-relevant fault detection
- quality-relevant fault detection