用联邦学习预测喷气发动机剩余寿命，保护数据隐私同时提升预测精度。
Using Federated Machine Learning in Predictive Maintenance of Jet Engines
- 在不共享原始数据的前提下，通过联邦学习联合训练预测模型。
- 基于C-MAPSS数据集，实现高精度的发动机剩余使用寿命预测。
- 适合关注航空运维优化与数据隐私保护的研究者与工程师。
本文旨在利用联邦机器学习框架预测涡轮喷气发动机的剩余使用寿命（RUL）。联邦学习使多个边缘设备或服务器能够在不共享敏感数据的情况下协同训练共享模型，从而保障数据隐私与安全。通过构建非线性模型，系统能够捕捉发动机数据中复杂的关联与模式，提升RUL预测的准确性。该方法采用去中心化计算，各设备本地训练模型后，将学习到的权重上传至中央服务器进行聚合。准确预测发动机剩余寿命可优化维护计划，减少停机时间，提高运行效率，最终实现成本降低与性能提升。实验基于NASA官网公开的C-MAPSS数据集，该数据集是研究和分析不同工况下发动机退化行为的重要资源。
原文摘要 · Abstract (English)
The goal of this paper is to predict the Remaining Useful Life (RUL) of turbine jet engines using a federated machine learning framework. Federated Learning enables multiple edge devices/nodes or servers to collaboratively train a shared model without sharing sensitive data, thus preserving data privacy and security. By implementing a nonlinear model, the system aims to capture complex relationships and patterns in the engine data to enhance the accuracy of RUL predictions. This approach leverages decentralized computation, allowing models to be trained locally at each device before aggregating the learned weights at a central server. By predicting the RUL of jet engines accurately, maintenance schedules can be optimized, downtime reduced, and operational efficiency improved, ultimately leading to cost savings and enhanced performance in the aviation industry. Computational results are provided by using the C-MAPSS dataset which is publicly available on the NASA website and is a valuable resource for studying and analyzing engine degradation behaviors in various operational scenarios.
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