Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

Amir El-Ghoussani, Michele De Vita, Ronald Naumann, Valiseios Belagiannis

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arXiv:2606.11990v1 Announce Type: cross 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...

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