用预训练时间序列模型提取特征，实现低数据依赖的设备剩余寿命预测。
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
- 冻结预训练时间序列模型提取多传感器特征，搭配轻量回归头预测寿命。
- 在两种工业设备上性能优于多种主流基线模型，且对历史长度敏感。
- 适合数据稀缺场景，可显著降低标注需求，适用于工业预测性维护。
剩余使用寿命（RUL）预测对工业预测性维护至关重要，但许多基于学习的方法依赖大量特征工程或标注数据来训练特定任务的序列模型。本文提出一种轻量化方法：利用冻结的预训练时间序列基础模型（TSFM）与小型回归头结合，从多变量传感器流中进行RUL估计。具体地，采用Chronos-2作为冻结主干网络提取上下文窗口特征，并训练一个轻量级神经网络回归器完成预测。在来自两种设备类型的实时工业传感器数据上进行实验表明，在相同预处理和评估协议下，Chronos-2特征持续优于循环、卷积、Transformer及梯度提升基线模型。进一步分析显示，随着历史窗口长度增加，性能显著提升，说明TSFM表示为工业场景中的RUL估计提供了高效且数据友好的替代方案。
原文摘要 · Abstract (English)
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.
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