AKRNet: A novel convolutional neural network with attentive kernel residual learning for feature learning of gearbox vibration signals

Vibration signals have been widely used for machine health monitoring and fault diagnosis. However, due to the complex working conditions, vibration signals collected from gearbox are generally nonlinear and non-stationary, which may contain multiple time scales and much noise. Considering these physical characteristics of vibration signals, in this paper, a novel deep neural network (DNN), attentive kernel residual network (AKRNet), is proposed for multi-scale feature learning from vibration signals. Firstly, multiple branches with different kernel widths are used to extract multi-scale features from vibration signals. Secondly, an attentive kernel selection is proposed to fuse the multiple branches features, where dynamic selection is developed to adaptively highlight the informative feature maps, while suppress the useless feature maps. Thirdly, an attentive residual block is developed to improve the feature learning performance, which not only can alleviate gradient vanishing, but also further enhances the impulsive features in feature maps. Finally, the effectiveness of AKRNet for feature learning of vibration signals is verified on two gearbox test rigs. The experimental results show that AKRNet has good capacity of feature learning on vibration signals. It performs better on gearbox fault diagnosis than other typical DNNs, e.g., stacked auto-encoders (SAE), one-dimensional CNN (1-D CNN) and residual network (ResNet). (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

A novel deep neural network (AKRNet) is proposed for multi-scale feature learning from vibration signals, which performs better on gearbox fault diagnosis compared to other typical DNNs.