A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions. (March 2020)

• Record Type: Journal Article
• Authors: Xu, Kun; Li, Shunming; Wang, Jinrui; An, Zenghui; Xin, Yu
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 4(2020)
• Page Start: 1167
• Page End: 1182
• Publication Date: 2020-03
• Subjects: Deep learning -- fault diagnosis -- convolutional neural networks -- noisy environment -- different workloads; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407019861028
• Languages: English
• ISSNs: 0954-4070

Abstract

Deep learning method is gradually applied in the field of mechanical equipment fault diagnosis because it can learn complex and useful features automatically from the vibration signals. Among the many intelligent diagnostic models, convolutional neural network has been gradually applied to intelligent fault diagnosis of bearings due to its advantages of local connection and weight sharing. However, there are still some drawbacks. (1) The training process of convolutional neural network is slow and unstable. It has more training parameters. (2) It cannot perform well under different working conditions, such as noisy environment and different workloads. In this paper, a novel model named adaptive and fast convolutional neural network with wide receptive field is presented to overcome the aforementioned deficiencies. The prime innovations include the following. First, a deep convolutional neural network architecture is constructed using the scaled exponential linear unit activation function and global average pooling. The model has fewer training parameters and can converge rapidly and stably. Second, the model has a wide receptive field with two medium and three small length convolutional kernels. It also has high diagnostic accuracy and robustness when the environment is noisy and workloads are changed compared with other models. Furthermore, to demonstrate how the wide receptive field convolutional neural network model works, the reasons for high model performance are analyzed and the learned features are also visualized. Finally, the wide receptive field convolutional neural network model is verified by the vibration dataset collected in the background of high noise, and the results indicate that it has high diagnostic performance.