发表机构
University of Catania（卡塔尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于NASA C-MAPSS数据集的联邦RUL估计基准FedCMAPSS，定义五类任务模拟工业挑战，评估多种联邦优化算法，为联邦预测性维护提供标准基础。
AI 中文摘要
数据驱动的预测与健康管理已成为工业4.0的关键推动因素，但鲁棒的剩余使用寿命（RUL）估计模型的开发常受限于失效运行数据的稀缺性。联邦学习提供了无需共享传感器数据即可协同训练预测模型的有前景范式，但目前相关研究缺乏通用评估框架。为解决这一缺口，本文提出FedCMAPSS——基于常用的NASA C-MAPSS数据集的联邦RUL估计基准。我们定义了五个标准化任务，旨在模拟从理想独立同分布（IID）场景到极端统计异质性的现实工业挑战，并对多种神经网络架构上的最先进联邦优化算法开展系统评估。通过建立可复现基线并公开源代码与数据划分，本研究旨在为开发和比较联邦预测性维护解决方案提供标准基础。
英文摘要
Data-driven prognostics and health management has emerged as a key enabler for Industry 4.0, yet the development of robust remaining useful life (RUL) estimation models is often limited by the scarcity of run-to-failure data. While federated learning offers a promising paradigm to collaboratively train predictive models without sharing sensor data, research efforts have operated so far in the absence of a common evaluation framework. To address this gap, this paper introduces FedCMAPSS, a benchmark for federated RUL estimation based on the commonly-used NASA C-MAPSS dataset. We define a set of five standardized tasks designed to simulate real-world industrial challenges, ranging from ideal IID settings to extreme statistical heterogeneity, and conduct a systematic evaluation of state-of-the-art federated optimization algorithms across multiple neural architectures. By establishing reproducible baselines and making the source code and data splits publicly available, this work aims to provide a standard foundation for developing and comparing federated predictive maintenance solutions.
CommentsAccepted at the 21st IEEE Conference on Industrial Electronics and Applications (ICIEA 2026)
