发表机构
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对预测性维护中剩余使用寿命和故障模式预测问题，提出将其转化为向量通用价值函数预测，用多步时间差分估计器估计，在相关模拟和数据上实验，结果表明该方法能改进预测，尤其在标签稀缺时，可利用碎片化记录。
AI 中文摘要
剩余使用寿命（RUL）预测和故障模式分类是预测性维护的核心任务。许多数据驱动流程采用固定窗口监督学习及完整终端标签，在观测不完整或单元标识不可用时，无法自然编码连续退化状态预测间的时间递归。本文将预测问题转化为对吸收性退化过程的向量通用价值函数（GVF）预测，把RUL和故障模式概率视为时间上一致的目标，而非独立窗口级标签，并用多步时间差分估计器TD($n,\lambda$)进行估计。理论支持确定了向量GVF的贝尔曼不动点，刻画了线性投影TD极限及其与可实现性下完整回报蒙特卡罗回归的关系，并解释了何时自举TD目标比蒙特卡罗回报的变异性更小。在事件触发多模式模拟和NASA C - MAPSS标签稀缺的拼接数据上，相对于有监督的相同骨干蒙特卡罗控制，TD改进了RUL和故障模式预测，特别是在完整标签稀缺的情况下。实际上，碎片化、无标识的退化记录可贡献局部贝尔曼转移，而非在获得完整的故障标签前被丢弃。
英文摘要
Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance. Many data-driven pipelines use fixed-window supervised learning with complete terminal labels; such routes do not naturally encode the temporal recursion linking successive degradation-state predictions when observations are partial or unit identities are unavailable. We formulate prognostics as vector General Value Function (GVF) prediction on an absorbing degradation process, treating RUL and failure-mode probabilities as temporally consistent targets rather than independent window-level labels, and estimate them with a multi-step temporal-difference estimator, TD($n,λ$). Supporting theory identifies the Bellman fixed point of the vector GVFs, characterizes the linear projected-TD limit and its relation to complete-return Monte Carlo regression under realizability, and explains when bootstrapped TD targets are less variable than Monte Carlo returns. On an event-triggered multimode simulation and NASA C-MAPSS label-scarce stitch data, TD improves RUL and failure-mode prediction relative to a supervised same-backbone Monte Carlo control, especially under scarce complete labels. Practically, fragmented, identity-free degradation records can contribute local Bellman transitions instead of being discarded until complete run-to-failure labels are available.
