ANS-net: anti-noise Siamese network for bearing fault diagnosis with a few data

Fault diagnosis has been limited due to data scarcity. Accordingly, this study focuses on fault diagnosis representation for rolling bearing with few fault data and noisy conditions. Data-driven-based methods have achieved huge success in fault diagnosis, but considerable data are required to maintain promising results and robustness to noises. Therefore, we propose the ANS-net framework in measuring intrinsic difference with a few vibration signals of rolling bearing and build a fault diagnosis model. Few-shot learning tests are employed under a few data and different noisy conditions. The ANS-net is composed of two identical combined networks and a relation layer. The former part extracts feature vectors from a sample pair, and the latter part measures the similarities of the both output vectors. Noisy signals are filtered by the cut-off operation before the input layer and the first convolutional layer with a wide kernel. The scaled exponential linear unit is used in each layer and combined with alpha-dropout layer in self-normalizing layers to map data into a certain distribution to improve the adaptability of the network. Extensive experiments are conducted to validate the proposed method. When tested under a few data, the ANS-net demonstrates better recognition rate than the baselines. When tested under logical noisy conditions, the ANS-net shows better robustness than the baselines. The ANS-net also performs better than the baseline methods with various loads and new categories.