Abstract：Aiming at problems of rotating machinery’s fault features needing to be extracted manually,complex fault recognition being difficult and diagnosis model’s poor robustness,a novel 1D depth convolutional neural network-based rotating machinery fault diagnosis model was proposed based on the classical convolution neural network model AlexNet.This new model adopted the modified 1D convolutional kernel and pool layers to adapt 1D time domain signals.The traditional intelligent diagnosis model included two distinct modules of manual feature extraction and classification,and the proposed model combined these two modules into one.With the proposed model,multiple alternate convolution and pool layers were used to complete learning the original signal’s self-adaptive features and then all connected layers were combined to realize fault diagnosis.Bearings and gearboxes health monitoring tests showed that the proposed model can realize accurate,stable and fast fault diagnosis; compared to BP neural network,SVM,1D-LeNet5 model and the classical AlexNet model,this new model is the best; the feature extraction effectiveness of the proposed model is verified with the PCA visualized analysis.
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