A meta-learning network with anti-interference for few-shot fault diagnosis

Considering the changing working conditions of rotating machinery in operation, it is often difficult to collect data accurately in some severe fault states, and the lack of data can lead to poor performance of deep learning-based fault diagnosis models. In the few-shot scenario, traditional meta-learning methods yield superior predictions by identifying ideal initialization parameters. When the update gradients of tasks are in different directions, meta-learning modifies the weights of samples in the current tasks to average them out, which may lead to negative transfer between tasks and poor generalization of biases. Based on the above problems, we propose a meta-learning network with anti-interference (AIML) for few-shot fault diagnosis, which is obtained by combining a dynamic fine-tuning technique to increase the gradient agreement of the tasks. AIML consists of two functions: the feature encoding network (FEN) and the base network. AIML uses the property of shared parameters about transfer learning to learn shared feature representations of different tasks, while FEN dynamically adjusts the loss weights of conflicting tasks due to meta-learning with two-level optimization techniques. First, in the internal loop, AIML updates the gradient of the base network while the network parameters of FEN are fixed. Instead of immediately computing the gradient of the optimal parameters for the new task, the FEN is then integrated in the outer loop to learn the meta-representation, and the weights obtained for the various tasks relate to the gradient for meta-optimization. Only during the outer loop stage of the meta-training is the FEN updated. Three publicly available datasets are used to assess the performance of AIML, and the results show that it is more effective at resolving problems involving few-shot fault diagnosis.