Dual attention dense convolutional network for intelligent fault diagnosis of spindle-rolling bearings

Abstract

Over the past few years, deep learning–based techniques have been extensively and successfully adopted in the field of fault diagnosis. As the diagnosis tasks become more complicated, the structure of the traditional convolutional neural network (CNN) has to become deeper to deal with them, while the gradient of fault features may vanish within the deep network. In addition, all the features are treated equally in the traditional CNN, which cannot make the most of the representation power of CNN. Here, we proposed a method named dual attention dense convolutional network to handle these issues, which is constructed by the dense network and the dual attention block. On one hand, the dense connections and concatenation layers can reinforce the propagation of fault features among layers and mitigate the vanishing gradient phenomenon in the deep network. On the other hand, as the features flow through the channel attention and spatial attention within the dual attention block, this attention mechanism can learn which feature to emphasize or suppress and then obtain the cross-channel and cross-spatial weights of the features. These weights can make the most of the abundant information, elevating the expressive power of network. After passing through these dense and attention blocks, the generated high-level features are then fed into the final classification layer to obtain diagnosis results. The effectiveness of the dual attention dense convolutional network is validated by eight datasets of spindle bearings under various machinery conditions. Compared with eight other approaches including support vector machines, random forest, and six existing deep learning models, the results indicate that the proposed dual attention dense convolutional network possesses higher accuracy, fewer parameters and computations, and faster convergence under complex operational conditions.