Aircraft interior defects, including seat structural damage, cushion degradation, liquid contamination, and foreign object presence, contribute to increased maintenance burden, extended ground time, and operational inefficiencies. Current inspection practices rely predominantly on manual visual checks, which are time-intensive and limited in detecting concealed anomalies.
This paper presents a non-contact, AI-enabled inspection framework integrating millimeter-wave (mmWave) radar sensing with high-definition optical imaging for automated aircraft seat condition assessment. The proposed system captures interior scans when the aircraft is unoccupied and compares them against a digitally established baseline reference obtained under certified, defect-free conditions. Data fusion and machine learning algorithms analyze deviations to identify surface and subsurface defects at seat-level resolution and generate zone-based maintenance maps.
The primary technical contribution lies in combining subsurface-capable mmWave sensing with AI-driven deviation analytics to enable detection of concealed cushion defects and foreign objects, including service tools, which are not reliably identified through conventional visual inspection alone. The system outputs structured maintenance reports identifying seat location, defect classification, and severity prioritization, thereby supporting targeted corrective action and reducing troubleshooting time.
In addition to in-service aircraft applications, the framework is extendable to seat production and final assembly inspection environments. Establishing a digital baseline during manufacturing enables inline quality validation, structural compliance verification, and traceable lifecycle data creation. This unified digital inspection approach supports predictive maintenance modeling, reduces rework and downstream maintenance events, and enhances overall aircraft interior safety and reliability assurance.