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  本文選題：模擬橋梁結(jié)構(gòu)　切入點：局部損傷故障識別　出處：《沈陽理工大學》2015年碩士論文

【摘要】：隨著橋梁在交通樞紐中的廣泛應用,對橋梁實時承載情況進行監(jiān)測和故障診斷得到廣泛關注。橋梁的工作環(huán)境通常比較惡劣,在時變載荷作用下,橋梁的內(nèi)部和外部結(jié)構(gòu)容易產(chǎn)生破損。此外,橋梁分布地域?qū)拸V且無專人值守,對其潛在的結(jié)構(gòu)故障進行檢測和診斷存在技術(shù)困難,因此開展模擬橋梁結(jié)構(gòu)的局部損傷故障檢測技術(shù)的研究,對提高橋梁建設質(zhì)量、在役橋梁安全管理都具有現(xiàn)實意義。本文從分析局部結(jié)構(gòu)損傷產(chǎn)生聲發(fā)射現(xiàn)象的原理入手,闡述幾種常見的局部損傷故障聲發(fā)射信號產(chǎn)生的原因。針對橋梁早期故障信號具有微弱、時變、非平穩(wěn)等特點,本文提出了利用在時頻域具有良好分辨率的小波變換結(jié)合具有非線性映射能力的神經(jīng)網(wǎng)絡的故障類型識別方法。通過對連續(xù)小波變換及其離散化進行分析,提出可以消除噪聲干擾的小波閾值消噪方法,并且利用Matlab進行了仿真驗證;針對橋梁各故障狀態(tài)機理的非線性特性,從模式識別的角度,應用BP神經(jīng)網(wǎng)絡對橋梁各個故障狀態(tài)進行識別;為了降低BP神經(jīng)網(wǎng)絡結(jié)構(gòu)的復雜性,利用統(tǒng)計分析的方法從經(jīng)過小波閾值消噪后的信號中提取特征量,作為BP神經(jīng)網(wǎng)絡的輸入;設計了基于小波閾值消噪及神經(jīng)網(wǎng)絡分類器的模擬橋梁結(jié)構(gòu)的局部損傷故障類型識別系統(tǒng)。利用聲發(fā)射信號檢測平臺對橋梁局部損傷故障發(fā)生時產(chǎn)生的聲發(fā)射信號進行檢測,最后通過故障類型識別系統(tǒng)對信號進行分析,比較準確地實現(xiàn)了對其故障類型的識別和分類。
[Abstract]:With the wide application of bridges in transportation hubs, the monitoring and fault diagnosis of bridge real-time loading are paid more and more attention. The working environment of bridges is usually very bad, which is affected by time-varying loads. The internal and external structures of bridges are easily damaged. In addition, there are technical difficulties in detecting and diagnosing the potential structural faults of bridges due to their wide distribution and lack of dedicated personnel. Therefore, it is of practical significance to carry out the research of local damage fault detection technology of simulated bridge structure to improve the quality of bridge construction and the safety management of in-service bridges. This paper begins with the analysis of the principle of acoustic emission phenomenon caused by local structure damage. This paper expounds the causes of acoustic emission signals of several common local damage faults, aiming at the weak, time-varying and non-stationary characteristics of the early fault signals of bridges. In this paper, a fault type identification method based on wavelet transform with good resolution in time-frequency domain and neural network with nonlinear mapping ability is proposed. The continuous wavelet transform and its discretization are analyzed. A wavelet threshold de-noising method which can eliminate noise interference is proposed, and the simulation is carried out by Matlab, and the nonlinear characteristics of each fault state mechanism of bridge are analyzed from the view of pattern recognition. In order to reduce the complexity of BP neural network structure, the statistical analysis method is used to extract the characteristic quantity from the signal after wavelet threshold de-noising as the input of BP neural network. Based on wavelet threshold de-noising and neural network classifier, a local damage fault identification system for bridge structure is designed. Acoustic emission signal detection platform is used to detect the acoustic emission signal generated when the bridge local damage fault occurs. Finally, the signal is analyzed by fault type recognition system, and the fault type recognition and classification are realized accurately.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U446

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本文編號：1675692