Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning

As an essential component of many mission-critical equipment, mechanical bearings need to be monitored to identify any traces of abnormal conditions. Most of the latest data-driven methods applied to bearing anomaly detection are trained using a large amount of fault data collected a priori. However, in many practical applications, it may be unsafe and time-consuming to collect enough data samples for each fault category, which brings challenges to training a robust classifier. This paper proposes a few-shot learning framework for bearing anomaly detection based on model-agnostic meta-learning (MAML), which aims to train an effective fault classifier using very limited data. In addition, it can use training data and learn to more effectively identify new fault conditions. A case study on the generalization of new artificial faults shows that this method can achieve up to 25% overall accuracy when compared to a benchmark study based on the Siamese network. Finally, the generalization ability of MAML is also competitive when compared with some state-of-the-art few-shot learning methods in terms of identifying realistic bearing damages using a sufficient amount of training data from artificial damages.