Multi-branch and multi-scale dynamic convolutional network for small sample fault diagnosis of rotating machinery

• Published in: IEEE sensors journal 2023-03, p.1-1
• Main Authors: Liang, Haopeng, Cao, Jie, Zhao, Xiaoqiang
• Format: Article
• Language: English
• Subjects: attention mechanism; Convolution; Convolutional neural networks; Deep learning; Fault diagnosis; Feature extraction; feature splitting; Machinery; multi-branch and multi-scale dynamic convolutional network; Rotating machinery; small sample fault diagnosis; Transfer learning
• ISSN: 1530-437X
• DOI: 10.1109/JSEN.2023.3255203

Deep learning methods have been widely used in the field of fault diagnosis of rotating machinery. As one of the deep learning methods, multi-scale convolutional neural network (MSCNN) can achieve accurate fault diagnosis by extracting feature information of vibration signals at different scales. However, in practical applications, a small number of fault samples limit the performance of MSCNN. To overcome this problem, a small sample rotating machinery fault diagnosis method based on multi-branch and multi-scale dynamic convolutional network (MBSDCN) is proposed. Firstly, a feature splitting strategy is proposed, which splits a single input into multi-branch inputs to ensure that each multi-scale convolutional layer is matched to the appropriate input. Secondly, a channel reconstruction (CR) attention mechanism is designed, and CR attention mechanism can form a direct mapping of high-dimensional space with the fully connected layer through the reconstruction processing of channels, thus making the connection between feature channels closer. Finally, a novel multi-scale feature extraction model is constructed, in which multi-scale convolutional layers are applied to extract multi-branch input features, and the CR attention mechanism is used to calibrate the contribution of multi-scale convolution layers. The performance of MBSDCN is evaluated with CWRU rolling bearing dataset and UOC gearbox dataset and is compared with some advanced deep learning methods. The comparison results indicate that the proposed MBSDCN can accurately diagnose faults under small sample conditions. In addition, the experimental results in noisy environments indicate that the proposed MBSDCN has satisfactory anti-noise performance under small sample conditions and noisy environments.