AI-driven automation of aviation equipment inspection: Insights from a complex adaptive systems perspective

Dear Editor,

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. To bridge the theory-practice gap, we propose a complex adaptive systems (CAS)-inspired framework that advances visual non-destructive testing by addressing data limitations, model performance degradation, and deployment challenges.

Damage recognition mechanism inspired by data-driven CAS

Our framework is conceptually aligned with the principles of CAS, wherein robustness and adaptability emerge from local-global interactions, dynamic feedback, and hierarchical coordination, which are essential for intelligent maintenance under uncertainty and domain-transformation scenarios. The overall framework is displayed in Figure 1A. In the data layer, a text-image interactive generation framework addresses the scarcity and domain-restricted availability of samples. It localizes and enhances damage regions while employing foreground-background disentanglement with noise injection to generate diverse and realistic samples. The textual prompts can be fused into the decoder layer to synthesize high-quality samples.