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.

A compound convolutional neural network used to process small perturbation

“…The accuracy of the network predictions mentioned in these works are all greater than 93% [9,10,11,12,13]. DenseNet is widely used in other industrial fields, such as bearing fault detection [14]. DenseNet is also used in this work as an image classification method.…”

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network

“…Studies showed that the training time of traditional convolutional neural networks was too long, the accuracy was not ideal, and could not get diversified image features. Gradient dispersion would be generated in the process of backpropagation [42][43][44][45]. Based on these studies, we designed a multi-scale high-density convolutional neural network based on the multi-scale convolutional neural network and Densenet structure.…”

A novel low-cost bearing fault diagnosis method based on convolutional neural network with full stage optimization in strong noise environment

“…So as to realize automatic fault feature extraction and excellent diagnosis performance, the deep learning method has become a priority alternative. At present, due to its superior feature extraction ability and performance in classification task, lots of researchers apply deep learning method to bearing fault diagnosis [9], such as auto-encoder(AE) [10], deep belief network (DBN) [11],convolutional neural network (CNN) [12,13], etc. Among them, CNN is the most widely utilized to solve various complex problems in bearing fault diagnosis field, thanks to its flexible structure and less trainable parameters brought by weight sharing mechanism.…”