Mallick Mainak, Shim Young-Dae, Won Hong-In, Choi Seung-Kyum
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Department Smart Manufacturing Technology, Sungkyunkwan University, Suwon-si 16419, Gyeonggi-do, Republic of Korea.
Sensors (Basel). 2025 Mar 12;25(6):1745. doi: 10.3390/s25061745.
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning parameters and limited generalization during meta-testing. To address these challenges, we proposed an ensemble-based meta-learning approach integrating majority voting with model-agnostic meta-learning (MAML), and operational grouping was implemented via Latin hypercube sampling (LHS) to enhance few-shot learning ability and generalization along with maintaining stable output. This approach demonstrates superior accuracy in classifying a significantly larger number of defective mechanical classes, particularly in cross-domain few-shot (CDFS) learning scenarios. The proposed methodology is validated using a synthetic vibration signal dataset of robotic arm faults generated via a digital twin. Comparative analysis with existing frameworks, including ANIL, Protonet, and Reptile, confirms that our approach achieves higher accuracy in the given scenario.
与数字孪生相结合的模型无关元学习（MAML）对预测性维护（PdM）具有变革性，特别是在装配线中的机器人手臂上，其中手臂的快速准确故障分类至关重要。尽管该框架获得了显著的关注，但它面临着重大挑战，如对学习参数的超敏感性以及元测试期间的有限泛化能力。为了解决这些挑战，我们提出了一种基于集成的元学习方法，将多数投票与模型无关元学习（MAML）相结合，并通过拉丁超立方采样（LHS）实施操作分组，以增强少样本学习能力和泛化能力，同时保持稳定的输出。该方法在对大量有缺陷的机械类别进行分类时表现出卓越的准确性，特别是在跨域少样本（CDFS）学习场景中。所提出的方法通过使用由数字孪生生成的机器人手臂故障的合成振动信号数据集进行了验证。与包括ANIL、Protonet和Reptile在内的现有框架的对比分析证实，我们的方法在给定场景中实现了更高的准确性。
