LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis
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
Record details
Published: 2 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (2 April 2026), “LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis,” evidence record 6458, https://ethics.ai/record/6458 (originally published by arXiv).
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