Few-Shot Bearing Anomaly Detection Based on Model-Agnostic Meta-Learning

The rapid development of artificial intelligence and deep learning technology has provided many opportunities to further enhance the safety, stability, and accuracy of industrial Cyber-Physical Systems (CPS). As indispensable components to many mission-critical CPS assets and equipment, mechanical bearings need to be monitored to identify any trace of abnormal conditions. Most of the data-driven approaches applied to bearing anomaly detection up-to-date are trained using a large amount of fault data collected a priori. In many practical applications, however, it can be unsafe and time-consuming to collect sufficient data samples for each fault category, making it challenging to train a robust classifier. In this paper, we propose a few-shot learning approach for bearing anomaly detection based on model-agnostic meta-learning (MAML), which targets for training an effective fault classifier using limited data. In addition, it can leverage the training data and learn to identify new fault scenarios more efficiently. Case studies on the generalization to new artificial faults show that the proposed method achieves an overall accuracy up to 25% higher than a Siamese-network-based benchmark study. Finally, the robustness of the generalization capability of MAML is further validated by case studies of applying the algorithm to identify real bearing damages using data from artificial damages.