Aiming at the problem of how to recognize the difficulty of identifying industrial equipment faults in the field of industrial production, this paper proposes an image recognition method for industrial equipment faults based on GWO-GoogleNet and transfer learning. First, the original signals of different types of faults of industrial equipment are time-frequency converted into images to generate corresponding data sets. Secondly, the deep learning network with the same parameter level is selected for noise resistance test, and the GoogleNet network with the best relative noise resistance is selected. Then, with the final error of the network as the optimization goal, the parameters of batch sampling number and learning rate are optimized, and the optimized network framework is completed. Finally, based on this network framework, its trained parameters are transferred to the target domain, and a small amount of target domain data is used to fine-tune the network. The method is verified on the bearing dataset of Western Reserve University, and the results show that the method has good recognition ability on bearing fault images, and still has good fault detection ability under migration conditions.


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    Title :

    Fault image recognition method of industrial equipment based on GWO-GoogleNet and transfer learning


    Contributors:


    Publication date :

    2023-10-11


    Size :

    3929526 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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