Name
Abstract | ||
|---|---|---|
•A nonlinear dynamic process monitoring method is presented.•The proposed method can extract the inherent slow features from the high-dimensional observed data.•A statistic index is built based on slow features to carry out process monitoring.•The method is more sensitive to process faults than the conventional KPCA-based method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.compeleceng.2014.11.003 | Computers & Electrical Engineering |
Keywords | Field | DocType |
Fault detection,Slow feature analysis,Kernel principal component analysis,Nonlinear dynamic process | Kernel (linear algebra),Nonlinear system,Pattern recognition,Computer science,Fault detection and isolation,Kernel principal component analysis,Feature extraction,Real-time computing,Artificial intelligence,Variable kernel density estimation,Pattern recognition (psychology),Kernel density estimation | Journal |
Volume | Issue | ISSN |
41 | C | 0045-7906 |
Citations | PageRank | References |
4 | 0.45 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ni Zhang | 1 | 4 | 0.45 |
| Xuemin Tian | 2 | 71 | 7.54 |
| Lianfang Cai | 3 | 15 | 1.32 |
| Deng Xiaogang | 4 | 115 | 17.49 |