FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation

2026-08-26

AI Summary

本文针对剩余使用寿命估计中数据稀缺问题，提出基于NASA C-MAPSS数据集的联邦学习基准FedCMAPSS，并通过标准化任务评估多种算法。

Abstract

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.

Problem

• Federated Learning
• Remaining Useful Life Estimation
• Data Scarcity
• Evaluation Framework

Innovation

• Federated Learning
• Remaining Useful Life (RUL)
• Benchmark
• Statistical Heterogeneity
• Reproducible Baselines