# 时间序列基础模型嵌入用于剩余使用寿命估计

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-10 08:00
- AIHOT 分数：54
- AIHOT 链接：https://aihot.virxact.com/items/cmq99n3g909vqslld2eu5y4oq
- 原文链接：https://arxiv.org/abs/2606.11990

## AI 摘要

提出一种轻量学习方法：使用冻结的预训练时间序列基础模型 Chronos-2 提取上下文窗口特征，结合小型回归神经网络进行多元传感器流的剩余使用寿命（RUL）预测。在两种设备类型的真实工业数据上，Chronos-2 嵌入特征在相同预处理和评估协议下，一致优于循环、卷积、Transformer 和梯度提升基线。研究还发现更长的上下文窗口显著提升预测性能，表明时间序列基础模型为工业 RUL 估计提供了实用且数据高效的替代方案。

## 正文

Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models. In this work, we introduce a lightweight learning approach, in which we leverage a frozen pretrained time-series foundation model (TSFM) and combine it with a small regression head for RUL estimation from multivariate sensor streams. More specifically, we use Chronos-2 as a frozen backbone to extract context window features and train a lightweight regression neural network for RUL prediction. Experiments on real-world industrial sensor data from two device types show that Chronos-2 features consistently improve over recurrent, convolutional, Transformer-based, and gradient-boosting baselines under the same preprocessing and evaluation protocol. We further analyze the impact of context length and find that performance improves significantly with longer histories, indicating that TSFM representation offer a practical and data-efficient alternative for RUL estimation in industrial settings.