  The rapid development of artificial intelligence and deep learning 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 fault diagnosis
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
framework for bearing fault diagnosis 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 framework achieves an overall accuracy
up to 25% higher than a Siamese network-based benchmark study. Finally, the
robustness and the generalization capability of the proposed framework are
further validated by applying it to identify real bearing damages using data
from artificial damages, which compares favorably against 6 state-of-the-art
few-shot learning algorithms using consistent test environments.
