基于支持向量机(SVM)的民用飞机重着陆智能诊断研究

Intelligent Diagnosis for Hard Landing of Aircraft Based on SVM

针对国内航空公司对于重着陆的判断方法存在的不足,提出采用支持向量机(SVM)建立重着陆的智能诊断模型;分析对重着陆产生影响的相关因素,在力学基础上揭示了重着陆的产生原理;利用快速存取记录器中记录的多个飞行参数的信息,采用B737机型的实际样本数据进行训练和验证。结果表明:该方法能有效判断出是否发生重着陆,其准确率高达92.86%,证明该重着陆智能诊断方法具有较强实际应用价值,为后续研究奠定了基础。

Due to lots of deficiencies existing in the hard landing diagnosis method of domestic airlines, an intelligent diagnosis model was established based on support vector machine for the first time. Related factors influencing hard landing were analyzed, and principles for hard landing was revealed based on mechanics. Then, the model was trained and verified up to 92.86% in accuracy with the data sample recorded by the quick access recorders from the B737 aircraft. The results show that the model could diagnose the hard landing effectively and is of important value in application. This research lays a good foundation for the subsequent research.