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.

翻译：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。本研究提出了一种轻量化学习方法，利用冻结的预训练时间序列基础模型（TSFM），并结合一个小型回归头，从多元传感器流中进行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，并训练一个轻量级回归神经网络用于RUL预测。在两种设备类型的真实工业传感器数据上进行的实验表明，在相同的预处理和评估协议下，Chronos-2特征始终优于循环神经网络、卷积网络、基于Transformer的模型以及梯度提升基准方法。我们进一步分析了上下文长度的影响，发现随着历史时间序列的延长，性能显著提升，这表明TSFM表示为工业环境中的RUL估计提供了一种实用且数据高效的替代方案。