AI-driven automation of aviation equipment inspection: Insights from a complex adaptive systems perspective
Peishu Wu, Weimin Wen, Li Han, Zeyu Li, Nianyin Zeng
Abstract
Advanced equipment inspection is shifting from manual routines to AI-driven pipelines. While current methods face three key constraints: (1) degraded and limited data under variable sensing, (2) unreliable detection of subtle / occluded defects, and (3) the need for efficient applications on resource-constrained conditions.1,2 Consequently, maintaining detection accuracy under stringent computing resources is essential. Among popular lightweight techniques, knowledge distillation (KD) operates via a student-teacher paradigm, requiring no architectural surgery and minimal hardware-specific tuning.
Topics & Concepts
Perspective (graphical)AutomationAviationSystems engineeringEngineeringComputer scienceAeronauticsArtificial intelligenceAerospace engineeringMechanical engineeringScheduling and Optimization Algorithms