Rolling Bearing Variable Load Fault Diagnosis Based on STFT-ECA-ResNet18 Network Model
Aiming at the problems of weak adaptive ability and poor model generalization of variable load bearing fault diagnosis by traditional methods,an improved fault diagnosis method based on deep residual network is proposed.Firstly,the collected one-dimensional time series signals are converted into two-dimensional time-frequency data by short-time Fourier transform,and features are extracted from the transformed data by using two-dimensional convolutional neural network.Then,the efficient channel attention mechanism is used to obtain the channel global information and adjust its weight,so as to enhance the generalization ability of the improved network model and improve the classification effect under variable load conditions.Finally,the proposed method is verified by simulation,and the results show that the diagnosis effect is improved significantly compared with the traditional method.
fault diagnosisgeneralization of network modelshort-time Fourier transformdeep residual networkvariable working condition
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