AI RESEARCH
LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis
arXiv CS.LG
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ArXi:2604.01725v1 Announce Type: cross 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