As a significant type of machine learning, supervised learning method is apt at learning from well-labeled training data and it is widely used in various tasks such as classification and regression. Support Vector Machine (SVM) is a powerful and popular algorithm in supervised learning, and it has been successfully applied in machinery intelligent fault diagnosis due to its excellent ability to handle complex decision boundaries and high-dimensional data, especially in small sample cases. Therefore, supervised SVM-based algorithms and their applications in machinery fault diagnosis are introduced in this chapter. To fully improve the generalization performance of SVM, the problems such as parameter optimization, feature selection, and ensemble-based incremental methods are discussed. The effectiveness of the SVM-based algorithms is validated in several fault diagnosis tasks on electrical locomotive rolling bearings, Bently rotor, and motor bearings test benches.


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    Title :

    Supervised SVM Based Intelligent Fault Diagnosis Methods


    Contributors:
    Li, Weihua (author) / Zhang, Xiaoli (author) / Yan, Ruqiang (author)


    Publication date :

    2023-09-11


    Size :

    82 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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