{
  "id": 6458,
  "url": "https://arxiv.org/abs/2604.01725v1",
  "title": "LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis",
  "summary": "General aviation fault diagnosis and efficient maintenance are critical to flight safety; however, deploying deep learning models on resource-constrained edge devices poses dual challenges in computational capacity and interpretability. This paper proposes LiteInception--a lightweight interpretable fault diagnosis framework designed for edge deployment. The framework adopts a two-stage cascaded architecture aligned with standard maintenance workflows: Stage 1 performs high-recall fault detection",
  "authors": "Zhihuan Wei, Xinhang Chen, Danyang Han, Yang Hu, Jie Liu, Xuewen Miao et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-02T07:45:10.000Z",
  "fetched_at": "2026-07-14T16:32:28.613Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6458",
  "original_url": "https://arxiv.org/abs/2604.01725v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}