本文選題：鐵路橋梁 + 健康監(jiān)測��； 參考：《石家莊鐵道大學(xué)》2015年碩士論文

【摘要】：隨著運輸業(yè)的蓬勃發(fā)展,橋梁在鐵路線路中所占比重越來越大,發(fā)揮著越來越重要的作用,然而,隨著橋梁數(shù)目、跨度、運營時間以及荷載的不斷增加,其運營安全性逐漸引起人們的廣泛關(guān)注,因此橋梁健康監(jiān)測系統(tǒng)的產(chǎn)生成為一種必然趨勢,監(jiān)測系統(tǒng)的建立和安裝使得橋梁管理者能夠?qū)崟r掌握橋梁的運營狀況及安全狀態(tài),及時發(fā)現(xiàn)安全隱患,減少事故的發(fā)生。但是,由于監(jiān)測系統(tǒng)的長時性,將會產(chǎn)生海量的數(shù)據(jù),這會對數(shù)據(jù)的傳輸和存儲帶來不便,因此,為了解決結(jié)構(gòu)健康監(jiān)測當(dāng)中通信帶寬受限以及節(jié)約存儲空間等問題,對于監(jiān)測數(shù)據(jù)的壓縮處理研究成為發(fā)展的必然趨勢。本文針對鐵路橋梁健康監(jiān)測數(shù)據(jù)的海量性,考慮橋梁動荷載特性以及周圍環(huán)境的影響,結(jié)合數(shù)據(jù)的周期性、循環(huán)平穩(wěn)性等特點,利用壓縮感知原理,提出針對鐵路橋梁健康監(jiān)測數(shù)據(jù)的分析和重構(gòu)算法。由于鐵路橋梁上運行列車編組的固定性以及車輛參數(shù)的一致性,使得監(jiān)測數(shù)據(jù)呈周期性變化,本文首先通過ANSYS仿真驗證其周期性的存在,再通過小波變換分析、循環(huán)譜分析等方法探索實測數(shù)據(jù)的內(nèi)在周期性,為后續(xù)分析做好鋪墊。壓縮感知理論是不同于傳統(tǒng)采樣定理的一種新興理論,在采樣過程當(dāng)中不在局限于帶寬的限制,本文根據(jù)監(jiān)測數(shù)據(jù)的特點,在深入分析壓縮感知原理的基礎(chǔ)上,對比各種重構(gòu)算法的優(yōu)缺點,采用OMP算法對監(jiān)測數(shù)據(jù)進行壓縮和重構(gòu),取得了較好的效果,證明了該算法的可行性。稀疏貝葉斯學(xué)習(xí)是近幾年新興的一個研究領(lǐng)域,將其與壓縮感知理論相結(jié)合對于塊狀稀疏信號具有較好的重構(gòu)效果,同時也克服了OMP算法計算復(fù)雜、迭代時間長的缺點。由于監(jiān)測信號呈周期性變化,因此通過差分處理使其具有塊狀稀疏的特性,從而可以利用貝葉斯壓縮感知進行壓縮重構(gòu),同樣取得了較好的重構(gòu)效果;為了使壓縮重構(gòu)過程更加簡單有效,本文采用基于稀疏貝葉斯學(xué)習(xí)的壓縮感知算法對未經(jīng)過處理的加速度信號(非稀疏性)進行壓縮和重構(gòu),取得了較好的重構(gòu)效果,證明了該算法的可行性和實用性。
[Abstract]:With the rapid development of transportation industry, bridges play a more and more important role in railway lines. However, with the increase of the number of bridges, span, operation time and load, The safety of the bridge has been paid more and more attention, so the generation of the bridge health monitoring system has become an inevitable trend. With the establishment and installation of the monitoring system, bridge managers can grasp the bridge operation status and safety status in real time. Discover safety hidden danger in time, reduce the occurrence of accident. However, because of the long-term nature of the monitoring system, a large amount of data will be produced, which will bring inconvenience to the transmission and storage of data. Therefore, in order to solve the problems of limited communication bandwidth and save storage space in structural health monitoring, The research on compression and processing of monitoring data has become an inevitable trend of development. Aiming at the magnanimity of railway bridge health monitoring data, considering the dynamic load characteristics of bridge and the influence of surrounding environment, combined with the characteristics of periodicity and cycle stability of the data, the principle of compression sensing is used in this paper. An algorithm for analysis and reconstruction of railway bridge health monitoring data is proposed. Because of the fixed train formation and the consistency of vehicle parameters on the railway bridge, the monitoring data change periodically. Firstly, the existence of periodicity is verified by ANSYS simulation, and then the wavelet transform is used to analyze it. Cyclic spectrum analysis and other methods are used to explore the periodicity of measured data so as to pave the way for subsequent analysis. Compression sensing theory is a new theory which is different from the traditional sampling theorem. It is not limited to bandwidth in the sampling process. According to the characteristics of monitoring data, this paper analyzes the theory of compressed sensing in depth. Compared with the advantages and disadvantages of various reconstruction algorithms, the OMP algorithm is used to compress and reconstruct the monitoring data, and good results are obtained, which proves the feasibility of the algorithm. Sparse Bayesian learning is a new research field in recent years. The combination of sparse Bayesian learning with compression sensing theory has a good reconstruction effect for block sparse signals, and it also overcomes the shortcomings of complex computation and long iteration time of OMP algorithm. Because the monitoring signal changes periodically, it has the characteristic of block sparseness by differential processing, so it can be compressed and reconstructed by Bayesian compression perception, and good reconstruction effect is also achieved. In order to make the process of compression and reconstruction more simple and effective, the compression sensing algorithm based on sparse Bayesian learning is used to compress and reconstruct the unprocessed acceleration signal (non-sparsity). The feasibility and practicability of the algorithm are proved.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U446

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