A Novel Meta-Learning Network With Adaptive Input Length and Attention Mechanism for Bearing Fault Diagnosis Under Different Working Conditions

In recent years, rolling bearing fault diagnosis technology based on deep learning (DL) has provided a more intelligent and reliable method for the safe operation of mechanical systems due to its powerful feature learning ability. However, in real industrial scenarios, the acquisition of fault samples is limited and knotty, which makes it difficult for DL methods that require a large number of fault samples to be successful. To overcome the above problems, in this article, a novel meta-learning network with adaptive input (AI) and attention mechanism is proposed for rolling bearing fault diagnosis with small samples under different working conditions. First, inspired by the envelope demodulation signal processing method, an AI length selection strategy considering the different working conditions is proposed, which improves the disadvantages of Gram angle field (GAF) 2-D coding method with the traditional fixed input. Second, the residual structure and attention mechanism are introduced to make the network have stronger feature extraction and generalization performance and further improve the classification accuracy. Finally, the effectiveness of the method is verified on the Case Western Reserve University (CWRU) bearing fault datasets and the high-speed train axle box bearing fault datasets conducted by us. The results show that the proposed improved model-agnostic meta-learning (MAML) network is superior to the other four mainstream meta-learning methods under the same conditions, and satisfactory fault diagnosis results can be obtained on the bearing fault datasets under different working conditions.