数据挖掘在航空信息系统中的应用

Application of Data Mining in Aeronautical Information System

随着人工智能、大数据等新技术的兴起,为航空信息系统领域的研究带来了新的思路,尤其是故障诊断、健康管理等方向。相比于传统的故障诊断方式,基于数据驱动的方法能够有效弱化过程中对专家知识的高要求。然而,在面对高维度、海量的数据时,传统的人工智能模型往往训练效率低、精度差。针对以上问题,论文通过机器学习以及大数据处理技术对参数进行场景敏感性挖掘、关联性分析,进而大幅度缩减参数维度以及减轻数据冗余。最终,通过真实场景下的案例研究,验证了论文提出的处理方法的有效性。

With the application of artificial intelligence,big data and other new technologies,new ideas have been brought to the research in the field of aeronautical information system,especially in the fields of fault diagnosis and health management.Com⁃pared with the traditional diagnosis method,the data-driven processing method weakens the higher demand for expert knowledge.However,all kinds of traditional machine learning models have low training efficiency and poor accuracy in the face of high-dimen⁃sional and massive data.In view of the above problems,this paper uses machine learning and big data processing technology to mine scene sensitivity and analyzes correlation of parameters,so as to realize the reduction of the parameter dimension and data redundan⁃cy.Finally,a case study in a real scene verifies the effectiveness of the proposed method.