aiqbench analysis
This research demonstrates how frozen time-series foundation models like Chronos-2 can significantly outperform traditional baselines for Remaining Useful Life estimation using minimal labeled data. By leveraging pretrained embeddings, the approach offers a practical, data-efficient solution for industrial predictive maintenance without requiring extensive feature engineering or large datasets.
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
arXiv:2606.11990v2 Announce Type: replace 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:2606.11990v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.11990 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 246 words
