构建联邦学习在设备剩余寿命预测中的基准测试体系
FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
- 基于NASA C-MAPSS数据集设计五类标准任务，模拟真实工业场景
- 在多种神经网络架构上评估主流联邦优化算法性能
- 提供可复现基线和公开代码，推动预测性维护模型对比研究
数据驱动的健康与使用寿命管理已成为工业4.0的关键支撑，但剩余使用寿命（RUL）估计模型的开发常受限于缺乏完整的故障至失效数据。联邦学习虽能实现不共享传感器数据的前提下协同训练预测模型，但现有研究缺乏统一的评估框架。为此，本文提出FedCMAPSS，一个基于NASA C-MAPSS数据集的联邦学习RUL估计基准。定义了五项标准化任务，涵盖从理想同分布（IID）到极端统计异质性的各类工业挑战，并对多种神经网络架构下的前沿联邦优化算法进行了系统评估。通过建立可复现的基线并公开源代码与数据划分，本工作旨在为联邦预测性维护解决方案的研发与比较提供标准化基础。
原文摘要 · Abstract (English)
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.
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