In the past, the method of combining signal processing and neural network was widely used in the fault diagnosis of mechanical rolling bearings to achieve the purpose of fault signal detection and identification. However, traditional methods cannot fully extract fault features. In this paper, taking mechanical rolling bearings as an example, a new rolling bearing fault diagnosis model is proposed by combining the improved wavelet packet transform with time domain convolution network. The improved Northern Goshawk algorithm is used to optimize the wavelet packet transform and analyse the fault signal; then, the time domain convolution network is used to establish a fault diagnosis model. When compared with the wavelet packet decomposition model optimized by the improved Northern Goshawk algorithm, the accuracy of the improved wavelet packet transform model in detecting inner ring faults and rolling element faults is found to be increased by 8.3% and 7.4%, respectively.


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    Title :

    Fault Diagnosis Based on Optimized Wavelet Packet Transform and Time Domain Convolution Network


    Contributors:
    Cao, Dengxue (author) / Gu, Yu (author) / Lin, Wei (author)

    Publication date :

    2023-01-01


    Remarks:

    Transactions of FAMENA ; ISSN 1333-1124 (Print) ; ISSN 1849-1391 (Online) ; Volume 47 ; Issue 3 (online first)


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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