A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples

Rotor bearing health is crucial for ensuring the operational stability of rotating equipment. Deep learning-based fault diagnosis methods have achieved widespread success due to their superior fault identification capability. However, conventional deep learning methods that rely on large quantities of data are not feasible for most important mechanical equipment since obtaining fault data is difficult. To address this problem, we propose channel attention siamese networks (CASN) with metric learning for intelligent bearing fault diagnosis with extremely small samples. First, in the feature learning phase, pairs of sample inputs are constructed, and feature extraction is performed by a shared encoder. Then, in the disparity learning phase, the differences between features of sample pairs are mapped as metric distances. Based on the metric distance between the unlabeled and labeled data, the fault type of the unlabeled data can be predicted in the test phase. The experimental results show that CASN achieves over 97% accuracy when the sample size is extremely small. In addition, even under the conditions of noise interference and signal transmission distortion, our model still has reliable diagnostic ability.