Research on bearing fault diagnosis based on ShuffleNet-DELM
Rolling bearing signal is a typical non-stable,non-linear data,and deep learning models can effectively extract such data features.For higher accuracy,the deep learning model continues to increase the amount and parameter scale of the computing and parameters,while the computer hardware capacity and the data available for training are limited in the actual project.A bearing fault diagnosis method based on ShuffleNet-DELM is proposed.First,the one-dimensional time-series signals are transformed into two-dimensional frequency-domain tensors.Then,an improved ShuffleNetV2 model is employed to extract features,followed by classification using the deep extreme learning machine(DELM)method.This approach achieves an average accuracy of 95.47%on a dataset comprising bearing vibration signals under various operating conditions.The results show that the method has a fast response,which can further improve the classification accuracy and generalization of ShuffleNetV2 model for bearing faults,and has greater practical value.
deep learningShuffleNetdeep extreme learning machinebearing
NETL
NSTL
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