CAJ | 학술논문

QAR（Quick Access Recorder）数据具有高维、复杂及数据量大的特性，严重影响数据处理效率。为降低其数据量与数据复杂性，高效检索并确定当前QAR数据是否是故障数据及其故障类型，首先通过PAA表示方法对QAR数据初步压缩，然后采用FP-Growth算法思想对压缩后的数据创建FP-Tree并只保留其频繁前缀子树，最后通过子树匹配确定测试数据与故障模型数据之间的匹配度。采用真实的飞机飞行QAR数据验证了算法的有效性和准确度。
QAR（Quick Access Recorder）수거구유고유、복잡급수거량대적특성，엄중영향수거처리효솔。위강저기수거량여수거복잡성，고효검색병학정당전QAR수거시부시고장수거급기고장류형，수선통과PAA표시방법대QAR수거초보압축，연후채용FP-Growth산법사상대압축후적수거창건FP-Tree병지보류기빈번전철자수，최후통과자수필배학정측시수거여고장모형수거지간적필배도。채용진실적비궤비행QAR수거험증료산법적유효성화준학도。
The characters of QAR (quick access recorder)data such as high dimensionality,complexity and large data volume make the data processing work seriously inefficient.In order to reduce its data volume and data complexity,as well as to retrieve efficiently and check out whether the current QAR data is the fault data and the type of the fault,we first compress QAR data initially through PAA representation method,and then adopt the idea of FP-Growth algorithm to create FP-trees on the compressed data but only keep their frequent prefix-trees. At last,we determine the matching degree between test data and fault model data using subtree matching.The effectiveness and precision of the algorithm are validated with real airplane flight QAR data.