Abstract
Many industrial manufacturing processes have multiple operation modes because of different strategy and varying feedstock. The traditional statistical process monitoring tools such as PCA and PLS cannot be applied since they assume that the process must have single mode operation region only. In this paper, all the factors that will affect the change of the mode are considered, a similarity factor including the similarity factor of PLS models and the mean shift of the external variables is introduced to measure the similarity of two sets of data. On basis of this similarity factor, a moving window is used and a mode identification approach for multimode process monitoring is proposed. The proposed approach is demonstrated on the benchmark Tennessee Eastman process.
| Original language | English |
|---|---|
| Pages (from-to) | 777-782 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 48 |
| Issue number | 8 |
| Early online date | 25 Sept 2015 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 - , Canada Duration: 7 Jun 2015 → 10 Jun 2015 |
Bibliographical note
The authors are grateful for the financial support for this work from National Natural Science Foundation of China (61374139 and 61490700).Keywords
- External analysis
- Mode identification
- Multiple operation mode
- Partial least square(PLS)
- Process monitoring