用于剩余使用寿命估计的时间序列基础模型嵌入

摘要

剩余使用寿命（RUL）预测对工业预测性维护至关重要，然而许多基于学习的方法依赖于大量特征工程或大规模标注数据集来训练特定任务的序列模型。本文提出一种轻量级学习方法，该方法利用冻结的预训练时间序列基础模型（TSFM），并结合小型回归头对多变量传感器流进行RUL估计。具体而言，我们采用Chronos-2作为冻结骨干网络提取上下文窗口特征，并训练轻量级回归神经网络进行RUL预测。在两种设备类型的真实工业传感器数据上的实验表明，在相同的预处理和评估协议下，Chronos-2特征持续优于循环神经网络、卷积神经网络、基于Transformer和梯度提升的基线方法。我们进一步分析了上下文长度的影响，发现更长的历史数据能显著提升性能，这表明TSFM表示为工业场景中的RUL估计提供了一种实用且数据高效的替代方案。

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.