本文關(guān)鍵詞： QAR數(shù)據(jù) 小波聚類算法 異常點(diǎn)檢測 相似性匹配 故障分析　出處：《中國民航大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著社會的發(fā)展進(jìn)步,選擇民航客機(jī)出行的人數(shù)越來越多,飛機(jī)的飛行安全得到越來越多的重視,直接或間接影響了民航的發(fā)展,如何有效的發(fā)現(xiàn)飛機(jī)飛行過程中潛在的安全隱患變得尤為重要。目前QAR被應(yīng)用于存儲飛機(jī)上機(jī)載設(shè)備中獲取的參數(shù),因此分析QAR數(shù)據(jù)中潛在的故障信息為飛機(jī)的后續(xù)故障定位、解決故障提供保障有很重要的實(shí)用意義。本文為了發(fā)現(xiàn)QAR數(shù)據(jù)中的異常點(diǎn)信息,研究了小波聚類算法在QAR數(shù)據(jù)異常點(diǎn)檢測上的應(yīng)用,并通過將獲取的異常點(diǎn)與專家經(jīng)驗(yàn)下的標(biāo)準(zhǔn)故障樣本進(jìn)行相似性匹配來發(fā)現(xiàn)發(fā)生故障的原因,從而有效的幫助飛機(jī)維修人員定位故障原因并解決故障。為此完成了以下所述的研究:1、完成了QAR數(shù)據(jù)的異常點(diǎn)檢測算法與故障分析。該方法首先獲取飛機(jī)飛行過程中巡航階段的數(shù)據(jù),通過使用小波聚類算法對其做聚類分析來發(fā)現(xiàn)QAR數(shù)據(jù)中隱藏的異常信息。然后根據(jù)構(gòu)建的標(biāo)準(zhǔn)故障模型,使用得到的異常點(diǎn)數(shù)據(jù)和標(biāo)準(zhǔn)故障模型做相似性匹配來定位故障類型,并檢驗(yàn)該方法查找的故障是否正確以檢測該方法的效率。2、對小波聚類算法進(jìn)行深入細(xì)致的分析,發(fā)現(xiàn)了小波聚類算法標(biāo)記滿足密度閾值的連通單元為同一個(gè)簇,而該連通單元中的網(wǎng)格有可能是幾個(gè)簇的邊界,也可能不屬于任何一個(gè)簇,導(dǎo)致不同的簇最后被標(biāo)記為同一個(gè)簇的MUAP問題;另外不滿足密度閾值的網(wǎng)格也可能存在屬于簇的數(shù)據(jù)對象和簇的邊界區(qū)分不準(zhǔn)的問題,從而提出改進(jìn)的小波聚類算法劃分非均勻網(wǎng)格,并對邊界網(wǎng)格進(jìn)一步細(xì)化及對不滿足密度值的網(wǎng)格進(jìn)一步處理,最后形成聚類并驗(yàn)證此方法的可行性。即改進(jìn)的小波聚類算法較傳統(tǒng)的小波聚類算法在不提高其時(shí)間復(fù)雜度數(shù)量級的基礎(chǔ)上提高了其效率。3、研究相似性度量并細(xì)致地分析了方法的原理,結(jié)合對QAR數(shù)據(jù)特點(diǎn)的分析發(fā)現(xiàn)QAR數(shù)據(jù)屬性間的相關(guān)性并選擇基于馬氏距離對異常點(diǎn)數(shù)據(jù)和標(biāo)準(zhǔn)故障模型做相似性匹配來發(fā)現(xiàn)故障類型。
[Abstract]:With the development and progress of the society, more and more people choose civil aviation passenger aircraft to travel, and the flight safety of aircraft is paid more and more attention, which directly or indirectly affects the development of civil aviation. It is very important to find the potential safety hazards in the flight process. At present, QAR is used to store the parameters obtained from the airborne equipment on the aircraft. Therefore, it is of great practical significance to analyze the potential fault information in QAR data for the subsequent fault location of the aircraft and to provide protection for the failure. In order to find the abnormal point information in the QAR data, this paper aims at finding the outliers in the QAR data. The application of wavelet clustering algorithm in outlier detection of QAR data is studied, and the reason of fault is found by matching the obtained outliers with standard fault samples under expert experience. Thus effectively helps the aircraft maintenance personnel locate the malfunction reason and solve the malfunction. For this reason, completed the following research: 1. The outlier detection algorithm and fault analysis of QAR data are completed. Firstly, the data of cruising phase in the flight process of aircraft are obtained. Wavelet clustering algorithm is used to cluster analysis to find the hidden abnormal information in QAR data. Then according to the constructed standard fault model. The outlier data and the standard fault model are used for similarity matching to locate the fault type and to check whether the fault found by this method is correct to detect the efficiency of the method. 2. The wavelet clustering algorithm is analyzed in detail, and it is found that the connected cells that meet the density threshold are the same cluster, and the grid in the connected cell may be the boundary of several clusters. It may not belong to any cluster, which leads to the MUAP problem that different clusters are finally labeled as the same cluster. In addition, the mesh which does not satisfy the density threshold may have the problem of inaccuracy between the data objects belonging to the cluster and the boundary of the cluster, so an improved wavelet clustering algorithm is proposed to divide the non-uniform mesh. The boundary mesh is further refined and the mesh that does not satisfy the density value is further processed. Finally, clustering is formed and the feasibility of this method is verified. That is, the improved wavelet clustering algorithm improves its efficiency on the basis of not increasing the order of magnitude of its time complexity compared with the traditional wavelet clustering algorithm. The similarity measurement is studied and the principle of the method is analyzed in detail. Based on the analysis of the characteristics of QAR data, the correlation between the attributes of QAR data is found and the fault type is found by matching the outlier data with the standard fault model based on Markov distance.
【學(xué)位授予單位】：中國民航大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V241;TP311.13

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