聚类HMM模型在QAR数据分析中的应用研究

摘要

QAR data is the streaming data obtained from the sensor during the flight.Facing with the huge QAR data,a clustering-based HMM model is proposed.According to the characteristics of QAR data,the changing characteristics of different attributes of QAR data during malfunction or abnormality are analysed and the main impacted attributes are extracted.The state trend of the data is obtained from the data discretization of its clustering,which means,the cluster is divided into multiple state trends and the HMM model is constructed during malfunction or abnormality which the malfunction or abnormality is described in the form of status switch.Also,the HMM model of related QAR data of air jolt is constructed to verify the effectiveness of the proposed model with the example of aircraft malfunction in air jolt.%快速存取记录器QAR(Quick Access Recorder)数据是飞机飞行过程中从传感器获取的流数据.面对大量的QAR数据,提出一种基于聚类的HMM模型.针对QAR数据特点,分析发生故障或异常时QAR数据中不同属性的变化特点,提取主要影响属性进行分析.通过对其聚类进行数据离散化,得出数据的状态趋势,即将其分为多个状态趋势.对故障或异常发生的过程进行HMM建模,以状态序列的形式描述故障或异常发生的过程,并以飞机空中颠簸故障为例,建立空中颠簸相关QAR数据的HMM模型,并检验了该模型的有效性.