Name
Title | ||
|---|---|---|
A Distributed Canonical Correlation Analysis-Based Fault Detection Method for Plant-Wide Process Monitoring |
Abstract | ||
|---|---|---|
In this paper, a new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem. This paper focuses on the distributed plant-wide processes. The core of the proposed method is to reduce uncertainties using correlation information from the neighboring nodes. Furthermore, the cost of the data transmission between network nodes is also reduced by the D-CCA algorithm. When the proposed method and the existing methods are compared using the Tennessee Eastman benchmark process, the false alarm rate, fault detection rate, and the detection delay are comparable. This suggests that the proposed method is feasible. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TII.2019.2893125 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Monitoring,Correlation,Fault detection,Principal component analysis,Informatics,Covariance matrices,Uncertainty | Monitoring problem,Data transmission,Fault detection and isolation,Computer science,Canonical correlation,Node (networking),Real-time computing,Correlation,Constant false alarm rate,Principal component analysis | Journal |
Volume | Issue | ISSN |
15 | 5 | 1551-3203 |
Citations | PageRank | References |
4 | 0.40 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwen Chen | 1 | 42 | 12.85 |
| Yue Cao | 2 | 4 | 0.73 |
| Steven X. Ding | 3 | 1792 | 124.79 |
| Kai Zhang | 4 | 71 | 7.38 |
| Tim Koenings | 5 | 7 | 1.79 |
| Tao Peng | 6 | 81 | 9.60 |
| Chunhua Yang | 7 | 435 | 71.63 |
| Weihua Gui | 8 | 577 | 90.82 |