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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    Titel :

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


    Beteiligte:
    Wang, Jianguo (Autor:in) / Deng, Zhifu (Autor:in) / Yang, Banghua (Autor:in) / Ma, Shiwei (Autor:in) / Fei, Minrui (Autor:in) / Yao, Yuan (Autor:in) / Chen, Tao (Autor:in)


    Erscheinungsdatum :

    01.01.2017


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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