中文摘要：

随着高速铁路的不断提速，高铁轻量化设计中广泛采用高强铝合金材料，但高速列车齿轮箱体服役安全评价亟待完善．本文针对高速列车齿轮箱体使用的铝合金材料服役特性，搭建了声发射检测拉伸试验系统，运用BP神经网络算法对声发射信号进行训练与识别，实现对箱体材料拉伸损伤表征识别与材料服役状态的安全预警．本研究为材料损伤状态的无损实时识别提供了一种识别与预警方法．

英文摘要：

With the rapid development of high-speed rails, high-strength aluminum alloys are widely used in the lightweight design, but the service safety assessment of gear boxes in high-speed trains needs to be improved in China. An acoustic emission tensile test system was built for high-speed train gearbox shells made of aluminum alloys. After training and recognition by a BP neural network, acoustic emission signal was used for characterizing tensile damage in the materials and warning the materials service status. The research provides a method of nondestructive real-time characterization and warning for damage in aluminum alloys.