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.


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    Title :

    Multivariate Fault Isolation in Presence of Outliers Based on Robust Nonnegative Garrote


    Contributors:
    Wang, Jianguo (author) / Deng, Zhifu (author) / Yang, Banghua (author) / Ma, Shiwei (author) / Fei, Minrui (author) / Yao, Yuan (author) / Chen, Tao (author)


    Publication date :

    2017-01-01


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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