基于分段距离和子序列匹配的飞机故障检测

Airplane Fault Detection Based on Segmental Distance and Subsequence Match

针对飞机故障检测数据中重复率高数据量大,监测算法效率和准确率低的问题,本文在PAA压缩数据的基础上使用分段概率提取细分QAR数据,调整FP-Growth算法创建独具特色FP-Tree降低数据的重复度,提高数据的查询速度,提出了基于分段距离和子序列匹配算法,本文采用真实的飞机飞行QAR数据验证该算法的有效性和准确度。

As about high repetition and large volume of data in airplane fault detection data as well as low efficiency and accuracy of monitoring algorithm, this paper, based on PAA packed data, utilizes segmental probability to extract, adjust FPGrowth and establish FP-Tree, thereby reducing repetition degree of data and improving its searching speed. In addition, algorithm on the basis of segmental distance and subsequence match is proposed. In this paper, the real QAR data of flight will be adopted to verify reliability and accurateness of the algorithm.