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 featu
The proliferation of pretrained foundation models in various domains is driving efforts to extend their utility to time-series data, building on advances in general AI architectures.
This development allows for more accurate and efficient predictive maintenance without extensive feature engineering or large labeled datasets, significantly reducing operational costs and improving machinery longevity in industrial settings.
Industrial predictive maintenance can become more accessible and effective for diverse equipment types, potentially democratizing advanced anomaly detection and remaining useful life (RUL) estimation.
- · Industrial manufacturing
- · Predictive maintenance software providers
- · Equipment operators
- · AI model developers specializing in time-series
- · Providers of traditional, feature-engineering-heavy RUL solutions
- · Companies reliant on manual maintenance scheduling
Widespread adoption of foundation models for industrial asset management.
Increased operational efficiency and reduced downtime across various industries.
Reallocation of human capital from reactive maintenance to more strategic operational roles, potentially impacting industrial labor markets.
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Read at arXiv cs.LG