Due to the harsh working environment of gearboxes and strong noise interference, the accuracy of fault identification is seriously affected. In addition, the traditional ResNet network has a large number of network parameters, resulting in slow training speed. Experiments show that the accuracy of the proposed Fast-SeResNet network is maintained at 99% compared to the traditional ResNet network, while the number of training parameters has been reduced by an order of magnitude, and the training time for each Batch is reduced from 66 seconds to 10 seconds with the same hardware support. The results show that the Fast-SeResNet network structure can improve the diagnostic speed to a large extent in a noisy environment with a small improvement in network accuracy, and has some practical value.


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    Title :

    Research on Gearbox Fault Diagnosis Based on Improved ResNet Network


    Contributors:


    Publication date :

    2023-08-04


    Size :

    2001005 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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