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An improved deep residual network with multiscale feature fusion for rotating machinery fault diagnosis

Intelligent mechanical fault diagnosis algorithms based on deep learning have achieved considerable success in recent years. However, degradation of the diagnostic accuracy and operational speed has been significant due to unfavorable working conditions and increasing network depth. An improved version of ResNets is proposed in this paper to address these issues. The advantages of the proposed network are presented as follows. Firstly, a multi-scale feature fusion block was designed, to extract multi-scale fault feature information. Secondly, an improved residual block based on depthwise separable convolution was used to improve the operational speed and alleviate the computational burden of the network. The effectiveness of the proposed network was validated by discriminating between diverse health states in a gearbox under normal and noisy conditions. The experimental results show that the proposed network model has a higher classification accuracy than the classical convolutional neural networks, LeNet-5, AlexNet and ResNets and a faster calculation speed than the classical deep neural networks. Furthermore, a visual study of the different stages of the network model was conducted, to effectively comprehend the operational processes of the proposed model.