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This study demonstrates that freezing the Chronos-2 time-series foundation model as a backbone significantly outperforms traditional recurrent, convolutional, and Transformer-based baselines for Remaining Useful Life estimation. By pairing the frozen backbone with a lightweight regression head, the approach offers a data-efficient alternative that reduces reliance on extensive feature engineering or large labeled datasets. The findings suggest that leveraging long historical contexts via foundation models is a practical strategy for industrial predictive maintenance tasks.
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
arXiv:2606.11990v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.11990 [cs.LG]
(or arXiv:2606.11990v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.11990 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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