A Novel Deep Model with Meta-Learning for Rolling Bearing Few-Shot Fault Diagnosis

Machine learning, especially deep learning, has been highly successful in data-intensive applications; however, the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement. This leads to the so-called few-shot learning (FSL) problem, which requires the model rapidly generalize to new tasks that containing only a few labeled samples. In this paper, we proposed a new deep model, called deep convolutional meta-learning networks, to address the low performance of generalization under limited data for bearing fault diagnosis. The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data. The proposed method was compared to several FSL methods, including methods with and without pre-training the embedding mapping, and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain. The comparisons are carried out on 1-shot and 10-shot tasks using the Case Western Reserve University bearing dataset and a cylindrical roller bearing dataset. The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions. In addition, we found that the pretraining process does not always improve the prediction accuracy.