Aircraft cabin Diagnosis - Inspection of the Aircraft cabins and seats using AI

Operational challenges related to aircraft interiors particularly damaged seats, compromised seat cushions, and liquid or food spillages have a direct impact on airline operations and passenger satisfaction. Allowing passengers to use defective seating not only degrades the travel experience but also risks damaging the airline’s reputation. Moreover, addressing these issues often results in increased aircraft ground time, contributing to significant financial implications for airline operators. To mitigate these challenges, a predictive maintenance approach leveraging artificial intelligence (AI) is proposed. This system utilizes radar scans and high-definition camera images captured when the aircraft is unoccupied to detect interior issues. The collected data is then compared against a baseline “master scan” taken under ideal, clean, and properly maintained conditions. Advanced AI algorithms analyze these comparisons to accurately identify problem areas, down to specific seat locations and types of defects. The generative AI system outputs detailed zone maps and a seat-level list of identified issues, enabling maintenance teams to quickly locate and resolve problems with high precision and minimal errors. This leads to significantly reduced maintenance times, less aircraft idle time, and improved operational efficiency. Additionally, technology can be expanded and developed to identifying the presence of foreign objects such as tools inadvertently left behind by service personnel or personal items forgotten by passengers. The detection of such objects, particularly service tools, is critical, as their presence may pose serious safety risks during flight operations. In summary, the integration of AI-driven predictive maintenance for aircraft interiors not only supports proactive issue resolution but also strengthens safety protocols and preserves airline brand integrity.