Name
Title | ||
|---|---|---|
Multivariate Fault Isolation in Presence of Outliers Based on Robust Nonnegative Garrote. |
Abstract | ||
|---|---|---|
Fault isolation is essential to fault monitoring, which can be used to detect the cause of the fault. Commonly used methods include contribution plots, LASSO, Nonnegative garrote, construction-based methods, branch and bound algorithm (B & B), etc. However, these existing methods have shortcomings limiting their implementation when there exist vertical outliers and leverage points, Therefore, to further improve the fault prediction accuracy, this paper present a strategy based on robust nonnegative garrote (R-NNG) variable selection algorithm, which is proved to be robust to outliers in the TE process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-6373-2_38 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Fault isolation,Variable selection,Outliers,Robust nonnegative garrote,TE process | Conference | 762 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-Guo Wang | 1 | 52 | 9.66 |
| Zhifu Deng | 2 | 0 | 0.34 |
| Banghua Yang | 3 | 0 | 1.35 |
| Shiwei Ma | 4 | 136 | 21.79 |
| Minrui Fei | 5 | 1003 | 117.54 |
| Yuan Yao | 6 | 591 | 53.27 |
| Tao Chen | 7 | 49 | 21.43 |