Fault diagnosis of helicopter rolling bearing based on improved SqueezeNet

In order to solve the problem of large amount of model parameters and calculations in the existing fault diagnosis methods based on convolutional neural networks，an improved SqueezeNet model was proposed to be applied to the fault diagnosis of helicopter rolling bearings.By drawing on the idea of the VGG16 model based on the classic SqueezeNet，the model used three sizes of $\mathrm{3}\times \mathrm{3}$ convolution kernels instead of one size of $\mathrm{7}\times \mathrm{7}$ convolution kernel，and realizes the increase of network capacity，enhancement of nonlinearity，reduction of network parameters amount under the same perceptual field conditions.To further reduce the amount of network parameters，the convolutional layer，pooling layer，Fire module and pooling layer were alternated to form the model feature extraction layer.While ensuring the feature extraction capability，the amount of network parameters was further reduced.The model faults diagnosis experiment was carried out through the bearing data onto the research group.The results showed that the diagnosis accuracy of the model reached 99.65%，which was comparable to the traditional convolutional neural network and the classic SqueezeNet model.The calculation amount and the parameter amount were reduced by about 6 times and 36 times.