{
  "id": 3477,
  "url": "https://arxiv.org/abs/2605.30593v1",
  "title": "Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction",
  "summary": "Engine Health Management (EHM) depends on reliable forecasting of Remaining Useful Life (RUL) and on tracking thermal indicators such as turbine gas temperature (TGT). In practice, real-world fleet data are heterogeneous and non-stationary, and point predictions alone are insufficient for risk-aware maintenance decisions. This paper presents a multi-task scientific machine learning framework for turbine prognostics that jointly predicts turbine gas temperature untrimmed (TGTU), Delta Turbine Gas",
  "authors": "Jostein Barry-Straume, Changmin Son, Adrian Sandu, Gavan Burke, Rekha Sundararajan, Andrew Rimell et al.",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-28T21:39:53.000Z",
  "fetched_at": "2026-07-14T16:30:18.853Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3477",
  "original_url": "https://arxiv.org/abs/2605.30593v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}