本文選題：時間序列分析 + 橋梁健康監(jiān)測信息　； 參考：《重慶交通大學(xué)》2015年碩士論文

【摘要】：隨著在役橋梁結(jié)構(gòu)安全問題日益突出以及橋梁監(jiān)測技術(shù)的不斷發(fā)展,橋梁健康監(jiān)測系統(tǒng)已得到廣泛的應(yīng)用,基于實時橋梁結(jié)構(gòu)響應(yīng)信息對橋梁結(jié)構(gòu)狀態(tài)進(jìn)行快速準(zhǔn)確地分析也已成為亟待解決的熱點問題。本文以時間序列分析為基礎(chǔ),依次對橋梁健康監(jiān)測信息的數(shù)據(jù)預(yù)處理過程、相似查詢過程、異常檢測過程進(jìn)行分析,三個過程相互銜接配合構(gòu)建了橋梁健康監(jiān)測信息異常檢測模型,并實現(xiàn)了基于時間序列分析的橋梁健康監(jiān)測信息分析系統(tǒng)。本文首先提出了基于聚類分段的單變量時間序列孤立點識別方法,以局部標(biāo)準(zhǔn)差變化為量度對整個單變量時間序列在時間軸上進(jìn)行分段,使得對于某個時間序列子段,其包含的每個樣本對象的增加或刪除都不會使這一子段的局部標(biāo)準(zhǔn)差產(chǎn)生明顯變化,但相鄰段之間的樣本局部平均值存在明顯差異。分段結(jié)束后對于段內(nèi)樣本對象不大于孤立點判定閾值(一般為1)的子段做異常處理。經(jīng)實驗分析此方法對時間序列中的孤立點具有精準(zhǔn)的挖掘能力。在對時間序列中的空缺值進(jìn)行處理的過程中,使用基于最近距離鄰法的空缺值填補方法,以局部標(biāo)準(zhǔn)差變化為量度分析找出整個時間序列中相應(yīng)空缺點近鄰范圍內(nèi)最可能相似數(shù)據(jù)段的上下界,使用相似數(shù)據(jù)段中數(shù)據(jù)的加權(quán)平均值來作為空缺數(shù)據(jù)樣本的最相似估計值。之后通過使用主成分分析法(PCA)對馬桑溪大橋的橋梁健康監(jiān)測數(shù)據(jù)集的進(jìn)行特征提取以構(gòu)建了馬桑溪大橋橋梁健康監(jiān)測數(shù)據(jù)集的CMTS數(shù)據(jù)庫,驗證了使用PCA法對橋梁健康監(jiān)測信息進(jìn)行數(shù)據(jù)壓縮的可行性。針對原有的K-means聚類算法聚類數(shù)目必須預(yù)先賦值、初始質(zhì)心隨機選取以及處理海量數(shù)據(jù)時效率較低的三個缺點,提出基于網(wǎng)格劃分和三角形三邊定理的改進(jìn)的K-means聚類算法。改進(jìn)后的K-means算法可以根據(jù)樣本數(shù)據(jù)集樣本分布特征對樣本維度空間進(jìn)行網(wǎng)格劃分,并對其中的密集網(wǎng)塊進(jìn)行統(tǒng)計以自行確定k值與初始質(zhì)心位置,并通過三角形三邊定理的引入,大大減少了聚類過程中的迭代次數(shù)與計算復(fù)雜度。通過對比改進(jìn)的K-means算法與原有K-means算法對UCI數(shù)據(jù)庫中的PAMAP2 Physical Activity Monitoring數(shù)據(jù)集進(jìn)行聚類分析的結(jié)果準(zhǔn)確性與處理效率,驗證了改進(jìn)后的K-means算法的準(zhǔn)確性與高效性。引入索引樹結(jié)構(gòu),構(gòu)建了基于B+索引樹的k近鄰相似查詢算法。利用馬桑溪大橋的橋梁健康監(jiān)測數(shù)據(jù)集的CMTS集對基于B+索引樹的k近鄰相似查詢算法進(jìn)行檢驗,驗證了其高效性與準(zhǔn)確性。以時間序列局部異常系數(shù)LOF作為檢測樣本數(shù)據(jù)是否異常的量度,對多變量時間序列的異常檢測算法進(jìn)行分析。之后對以多變量橋梁健康監(jiān)測時間序列集為研究對象的橋梁健康監(jiān)測信息異常檢測過程進(jìn)行分析,并對橋梁健康監(jiān)測數(shù)據(jù)的異常檢測模型進(jìn)行建立。最后通過整合前文中所涉及的所有方法與算法,以Matlab為平臺搭建了基于時間序列分析的橋梁健康監(jiān)測信息分析系統(tǒng),并使用牛棚特大橋?qū)崟r監(jiān)測數(shù)據(jù)集導(dǎo)入橋梁健康監(jiān)測信息分析系統(tǒng)中對異常檢測模型進(jìn)行驗證,結(jié)論與牛棚特大橋階段性檢測報告內(nèi)容相符,驗證了基于時間序列分析的橋梁健康監(jiān)測信息異常檢測模型的可行性。
[Abstract]:The bridge health monitoring system has been widely used with the increasing security of the bridge structure and the continuous development of bridge monitoring technology. The rapid and accurate analysis of bridge structure based on real-time bridge structural response information has become a hot point problem to be solved urgently. This paper is based on time series analysis. In turn, the data preprocessing process of bridge health monitoring information, similar query process, and abnormal detection process are analyzed. The bridge health monitoring information anomaly detection model is built up with the three processes, and the bridge health monitoring information analysis system based on time series analysis is realized. A piecewise single variable time series outlier recognition method is used to segment the whole single variable time series on the time axis with the local standard deviation change as the measure. The increase or deletion of each sample object in the subsection of a time series will not make a significant change in the local standard deviation of the subsection, but it is adjacent to the subsection. There are obvious differences in the local mean values between the segments. After the segment end, the sample object in the segment is not more than the subsection of the outlier decision threshold (usually 1). This method has the accurate mining ability for the outliers in the time series. Using the vacancy value filling method based on the nearest neighbor method, the upper and lower bounds of the most likely similar data segments in the nearest neighbor range of the corresponding space defects in the whole time series are found out by the variation of the local standard deviation, and the most similar estimation value of the vacant data samples is used as the weighted mean value of the data in the similar data segments. The principal component analysis (PCA) is used to extract the data set of the bridge health monitoring data of the Ma sang Xi Bridge to construct a CMTS database of the health monitoring data set of the bridge of Ma sang Xi Bridge. The feasibility of using the PCA method to compress the data of the bridge health monitoring information is verified. The number of clustering algorithms for the original K-means clustering algorithm is necessary. The improved K-means clustering algorithm based on the grid division and the triangle three edge theorem is proposed. The improved K-means algorithm can be used to mesh the sample principal dimension space according to the sample data set sample distribution characteristics, and then the improved K-means algorithm can be used to mesh the sample principal dimension space. The dense network blocks are used to determine the K value and the initial centroid position by themselves. By introducing the triangle three edge theorem, the number of iterations and computational complexity in the clustering process is greatly reduced. By comparing the improved K-means algorithm and the original K-means algorithm, the PAMAP2 Physical Activity Monitoring data set in the UCI database is carried out. The accuracy and efficiency of clustering analysis results verify the accuracy and efficiency of the improved K-means algorithm. The index tree structure is introduced to construct a k nearest neighbor query algorithm based on the B+ index tree. The CMTS set of the bridge health monitoring data set of the Ma sang Creek bridge is used for the k nearest neighbor query algorithm based on the B+ index tree. Test, verify its efficiency and accuracy. Take the time series local anomaly coefficient LOF as the measure of whether the sample data is abnormal, analyze the anomaly detection algorithm of the multivariable time series. After that, the bridge health monitoring information anomaly detection process with the multi variable bridge health monitoring time series is the research object. In the end, the bridge health monitoring information analysis system based on the time series analysis is set up by integrating all the methods and algorithms involved in the previous article, and the bridge health monitoring letter is introduced with the real-time monitoring data set of the bullpeng super large bridge to import the bridge health monitoring letter by integrating all the methods and algorithms involved in the previous article. In the interest analysis system, the anomaly detection model is verified, and the conclusion is consistent with the content of the phased detection report of the bullpeng bridge. The feasibility of the bridge health monitoring information anomaly detection model based on time series analysis is verified.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U446

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