通过联合纵向生存建模实现协作系统故障预测

中文摘要 AI 辅助

事件发生时间建模为从纵向状态监测数据估计时间相关的故障风险、可靠性和剩余使用寿命（RUL）提供了系统框架。但将这些模型应用于分布式预测仍具挑战，因传感器轨迹和故障时间记录常分散存储，受隐私或专有约束无法集中汇总。经典Cox比例风险模型依赖涉及全局风险集的不可分离部分似然，在标准联邦学习协议下直接优化困难。本文提出用于协作系统故障预测的联合纵向生存建模框架，结合纵向传感器表示学习与客户端可分离离散时间风险目标，使多客户端能协作训练预测模型，且无需共享原始传感器测量值或个体故障记录。从多变量传感器历史中提取的时间相关表示用于估计特定间隔的故障风险、可靠性曲线和系统RUL。在模拟分散设置下对四个C-MAPSS涡轮风扇发动机退化子集的实验表明，该框架在保持跨异构运行条件和故障模式与集中训练相当性能的同时，持续优于孤立的本地训练，展现了联合纵向生存建模在协作、数据感知状态监测和系统故障预测方面的潜力。

英文摘要

Time-to-event modeling provides a systematic framework for estimating time-dependent failure risk, reliability, and remaining useful life (RUL) from longitudinal condition monitoring data. However, applying these models to distributed prognostics remains challenging because sensor trajectories and failure-time records are often stored across organizations or operational sites and cannot be centrally pooled due to privacy or proprietary constraints. Moreover, the classical Cox proportional hazards model relies on a nonseparable partial likelihood involving global risk sets, making direct optimization difficult under standard federated learning protocols. This paper presents a federated longitudinal-survival modeling framework for collaborative system failure prognostics. The proposed framework combines longitudinal sensor representation learning with a client-separable discrete-time hazard objective, enabling multiple clients to collaboratively train a prognostic model without sharing raw sensor measurements or individual failure records. Time-dependent representations extracted from multivariate sensor histories are used to estimate interval-specific failure hazards, reliability curves, and system RUL. Experiments on the four C-MAPSS turbofan engine degradation subsets under simulated decentralized settings demonstrate that the proposed framework consistently improves prognostic performance over isolated local training while maintaining performance comparable to centralized training across heterogeneous operating conditions and failure modes. These results demonstrate the potential of federated longitudinal-survival modeling for collaborative, data-aware condition monitoring and system failure prognostics.

发表机构

• Fowler School of Engineering, Chapman University（查普曼大学福勒工程学院）
• Grand Valley State University（大峡谷州立大学）

机构由 AI 辅助整理，请以论文原文为准。