With the continuous increase of the running mileage and speed of the train, more and more wheels become out-of-roundness due to the collision and friction between the wheels and track. It has great significance to detect wheel polygon in order to ensure the safe operation of trains. The wheel out-of-roundness detection method based on POVMD and multinuclear LS-SVM is investigated by using POVMD algorithm to decompose the vibration signal, and then PSO to get optimal parameters which takes VMD algorithm into consideration. Such a method extracts some features from IMF components. Finally, Gaussian kernel function and directed acyclic graph classification method are chosen to build multinuclear classifier to detect wheel out-of-roundness. The experiment results show that the proposed method is effective to analyze wheel out-of-roundness.


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    Title :

    Method of Wheel Out-of-Roundness Detection Based on POVMD and Multinuclear LS-SVM


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liu, Baoming (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Fang, Lichao (author) / Li, Shibo (author) / Dai, Wang (author) / Zhang, Yong (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019



    Publication date :

    2020-04-04


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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