Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning

TL;DR

This paper introduces a model-agnostic meta-learning approach for bearing fault diagnosis that effectively classifies faults with limited data, outperforming existing methods and demonstrating strong generalization to real-world damage scenarios.

Contribution

It presents a novel few-shot learning framework based on MAML for bearing fault diagnosis, enabling effective classification with minimal data and better generalization than existing algorithms.

Findings

Achieves up to 25% higher accuracy than Siamese network benchmarks.

Demonstrates strong generalization to real bearing damages.

Outperforms 6 state-of-the-art few-shot learning algorithms.

Abstract

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.