Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis

作者：Chen, Junbin; Huang, Ruyi; Zhao, Kun; Wang, Wei; Liu, Longcan; Li, Weihua^*

来源：IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3517010.

DOI：10.1109/TIM.2021.3077673

• Convolutional neural network (CNN)
• fault diagnosis
• feature alignment
• multiscale
• rolling bearing

In recent years, deep learning methods, especially convolutional neural network (CNN), have received extensive attentions and applications in fault diagnosis. However, recent studies have shown that the shift-invariance of CNN is not good enough, resulting in fragile feature extraction and sharp reduction in model performance when the shift occurs in the input. To improve the shift-invariance of CNN, considering the periodic characteristics of vibration signals, a multiscale CNN with feature alignment (MSCNN-FA) is proposed for bearing fault diagnosis under different working conditions. First, by analyzing the operating principles of the convolutional layer and pooling layer, a feature alignment module including single- stride convolution layer, adaptive max-pooling layer, and global average pooling layer is designed to obtain aligned features. Next, to extract shift-invariant robust features from vibration signals, a multiscale convolution strategy is utilized, and a feature-aligned multiscale feature extractor is constructed. Finally, a classifier composed of fully connected (FC) layers is constructed for bearing fault diagnosis. The effectiveness of the method is verified by a rolling bearing experiment, which outperforms other related existing CNN-based methods in terms of diagnosis accuracy and feature robustness.