Abstract
In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector AR model with wavelet based denoising. Given the fault direction, a new index is proposed to detect the fault, which integrates fault detection and prognosis together. Case studies on a continuous stirred tank reactor and the Tennessee Eastman process demonstrate the effectiveness of the proposed approaches. © 2010 Elsevier Ltd.
Original language | English |
---|---|
Pages (from-to) | 1211-1219 |
Number of pages | 9 |
Journal | Control Engineering Practice |
Volume | 18 |
Issue number | 10 |
Early online date | 20 Jun 2010 |
DOIs | |
Publication status | Published - Oct 2010 |
Externally published | Yes |
Funding
This work was supported by National 973 project under Grants 2010CB731800 and 2009CB320602 and the NSFC under Grants 60721003, 60736026 and 60931160440. S. Joe Qin acknowledges the financial support from the Changjiang Professorship by the Ministry of Education of PR China.
Keywords
- Multivariate fault prognosis
- Principal component analysis
- Reconstruction based estimation
- Vector AR model
- Wavelet based denoising