Abstract In order to achieve the detection for the fault diagnosis of the wind turbine generator bearing, firstly, the transformation of the wavelet packet is adopted to decompose the vibration signal into several layers, and denoise and reconstruct it. Secondly, this paper takes the combination of the wavelet node energy and the characteristic parameters of the denoised signal both in the time and frequency domain as the input feature vector to BP neural network with the function of self- determining hidden layer neurons. Finally, the results of the fault diagnosis are regarded as the output. The experimental data demonstrate that this method can effectively diagnose the fault types of the wind turbine generator bearing.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analysis of the Fault Diagnosis Method for Wind Turbine Generator Bearing Based on Improved Wavelet Packet-BP Neural Network


    Contributors:


    Publication date :

    2014-01-01


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English






    A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

    Xu, Tao / Lv, Huan / Lin, Shoujin et al. | SAGE Publications | 2023


    Bearing Resistance of Wind Turbine Generator System

    ASO, TOSHIYUKI | Online Contents | 2016


    Fault Diagnosis of Massage Chair Motor Based on Wavelet Packet Algorithm

    Lu, Lixin / Li, Hui / Li, Guiqin et al. | Springer Verlag | 2021