Industrial data variables show obvious high dimension and strong nonlinear correlation. Traditional multivariate statistical monitoring methods, such as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space through appropriate kernel functions so as to achieve the goal of linear separability in the new space. However, the space projection from the low dimension to the high dimension is contradictory to the actual requirement of dimensionality reduction of the data. So kernel-based method inevitably increases the complexity of data processing.


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    Title :

    Fault Identification Based on Local Feature Correlation


    Additional title:

    Intelligent Control & Learning Systems


    Contributors:
    Wang, Jing (author) / Zhou, Jinglin (author) / Chen, Xiaolu (author)


    Publication date :

    2022-01-03


    Size :

    28 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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