Name
Abstract | ||
|---|---|---|
For complex nonlinear systems of chemical industry process, traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix for fault detection with large sample sets. So an improved fault detection method based on feature vector selection-KPCA (FVS-KPCA) is developed. This method can evidently reduce calculational complexity of fault detection and is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDMA.2012.45 | ICDMA |
Keywords | Field | DocType |
fault detection,kernel,kernel principal component analysis,chemical industry,principal component analysis,vectors,mathematical model | Kernel (linear algebra),Feature vector,Nonlinear system,Pattern recognition,Fault detection and isolation,Kernel principal component analysis,Artificial intelligence,Engineering,Principal component analysis | Conference |
Volume | Issue | Citations |
null | null | 2 |
PageRank | References | Authors |
0.47 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoqiang Zhao | 1 | 3 | 1.53 |
| Xinming Wang | 2 | 2 | 0.81 |
| Yang Wu | 3 | 84 | 18.42 |