With the rapid iteration of artificial intelligence technology, "AI+maintenance" has become the core driver of digital transformation in the civil aircraft maintenance industry. Several civil aviation maintenance enterprises have successively deployed AI technology applications, achieving breakthroughs in manual management, fault screening, safety control, and other fields. This has propelled the maintenance industry from an "experience-driven" model to a "data-driven" one, ushering in a new era of intelligence.
China Eastern Airlines Technology and China Eastern Airlines Digital Technology have jointly overcome technical difficulties and independently developed a maintenance manual management and control platform, effectively addressing industry pain points such as inconsistent data standards across different manufacturers and the lack of systematic support for all-model manuals. Based on China Eastern Airlines' years of accumulated maintenance work card translation database, combined with the translation engine of AI large model fine-tuning technology, this platform achieves precise localization of all-model maintenance manuals, breaking the barriers of professional terminology. At the same time, it builds a PC and mobile terminal coordination system, supporting cross-terminal viewing of 9 major aircraft models including C919, Airbus A320, and Boeing 737NG, as well as 22 types of manuals. It opens up the data link between work cards and maintenance manuals, making maintenance operation processes smoother. In the future, this platform will also expand scenarios such as "one-click interconnection" between work cards and manuals, fault diagnosis, and intelligent matching of manuals, creating a smart hub for maintenance.
Ameco Hangzhou Branch focuses on the pain points of fault screening and develops an AI-based intelligent repetitive fault screening system, upgrading the traditional manual screening mode to intelligent operation. This system utilizes large language model natural language processing technology to convert fault information into spatial vectors. By calculating the cosine similarity of vectors, it can quickly and accurately identify repetitive faults, significantly improving screening efficiency and accuracy. Data shows that after the system was put into use, taking the branch's 54 aircraft as an example, it saved an average of 3 hours of manual labor per day, equivalent to the workload of 0.5 full-time employees in a year. At the same time, it effectively reduced the risk of flight delays and minimized economic losses for airlines.
The Henan base of China Southern Airlines Engineering and Technology Company Limited has applied AI technology to safety management and control, establishing an intelligent platform for safety information management. This platform automatically categorizes safety reporting information through AI algorithms, achieving an accuracy rate of over 90%. Additionally, it creates a visual dashboard for safety information, making key information such as key personnel and high-risk issues readily apparent, enabling dynamic tracking and precise implementation of safety management measures. Furthermore, the base has constructed a knowledge graph for safety information, which will be integrated with a large language model for aircraft maintenance in the future to uncover hidden associations in safety information and further enhance the level of intelligence in safety management. Industry experts indicate that the in-depth application of AI technology will continuously improve maintenance efficiency, reduce costs, ensure safety, and promote high-quality development in the civil aviation maintenance industry.