Machine Learning-Driven Predictive Maintenance for China's Large Aircraft: Theories, Challenges, and Strategic Applications

With the rapid development of China's aviation industry, domestically produced large aircraft are gradually emerging in the passenger market. In the context of increasingly complex aircraft systems and heightened operational requirements, traditional scheduled maintenance methods can no longer deliver the safety, reliability, and cost-effectiveness needed. Predictive maintenance, which is driven by data analysis and intelligent algorithms offers a forward-looking approach to ensuring safe operation, reducing unplanned downtime, and lowering maintenance costs. This paper explores the theoretical foundations and practical applications of predictive maintenance for China's large aircraft. Through the analysis of selected case studies, we summarize the strengths and limitations of current practices and propose a feasible strategy centered on data fusion, intelligent monitoring, and collaborative decision-making. Our findings provide timely insights into further upgrading maintenance technology and strengthening overall operational assurance in the domestic aviation sector.