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.
翻译:人工智能和深层学习的迅速发展为进一步增强工业网络-物理系统的安全、稳定和准确性提供了许多机会。作为许多关键任务关键CTS资产和设备不可或缺的组成部分,需要监测机械轴承,以确定任何异常状况的踪迹。大多数用于进行故障诊断的最新数据驱动方法都是利用事先收集的大量过失数据进行培训。然而,在许多实际应用中,为每个故障类别收集足够的数据样本可能不安全和费时,因此培训一个强有力的分类员具有挑战性。在本文中,我们提议了一个根据模型-认知性元学习(MAML)进行过失诊断的几张镜头学习框架,目标是利用有限的数据培训有效的过失分类员,此外,它能够利用培训数据,并学会更高效地确定新的过失假设。关于对新的人为错误的概括化的个案研究表明,拟议框架的总体准确性比以Siamese网络为基础的基准研究高出25%。最后,我们提出一个基于模型-认知性元化的元分解分析(MAML)进行故障诊断的几张镜头学习框架的精度和一般化能力,利用有限数据测试环境来进一步验证。