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.
Multivariate Fault Isolation in Presence of Outliers Based on Robust Nonnegative Garrote
01.01.2017
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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