Meta-Learning With Adaptive Learning Rates for Few-Shot Fault Diagnosis

Abstract

Deep learning-based methods have been developed and widely used for fault diagnosis, which rely on the sufficient data. However, fault data are extremely limited in some real-case scenarios. In this article, a meta-learning with adaptive learning rates (MLALR) method is proposed for few-shot fault diagnosis. MLALR learns from auxiliary tasks to find initialization parameters of the model that can adapt to target tasks with a few data. The keys of MLALR are the proposed adaptive learning rates for meta-training and fine-tuning, whose values are adjusted according to the distributions of extracted features to tackle the two common problems of few-shot learning, i.e., overfitting and underfitting. The loss functions are further improved to promote the model generalization capability and training stability. The effectiveness of the proposed method is validated using two bearing datasets. MLALR obtains higher accuracies and stabilities than the baseline methods and three other state-of-the-art methods.