本文選題：梁型結(jié)構(gòu) + 損傷識別　； 參考：《浙江大學》2014年博士論文

【摘要】：梁型結(jié)構(gòu)廣泛應用于國民經(jīng)濟的各個方面,尤其是橋梁。橋梁作為交通運輸?shù)闹匾M成部分,是一個國家基礎設施建設的重點,同時也是經(jīng)濟發(fā)展與技術(shù)進步的象征。近些年來,梁型結(jié)構(gòu)的健康診斷技術(shù)已經(jīng)成為工程界的研究熱點,健康診斷系統(tǒng)及其理論研究也取得了很大進展。但是由于健康診斷系統(tǒng)本身的多學科交叉性、大型梁型結(jié)構(gòu)及其環(huán)境的復雜性和不確定性,使得梁型結(jié)構(gòu)的健康監(jiān)測系統(tǒng)的許多關(guān)鍵技術(shù)從理論到實際應用還存在許多不足。 本文采用理論與試驗相結(jié)合的方法,針對梁型結(jié)構(gòu)健康監(jiān)測過程中的一些關(guān)鍵技術(shù)問題進行了研究。在基于結(jié)構(gòu)振動特性的損傷識別技術(shù)的基礎上,采用了信息理論、信號處理、時頻分析、統(tǒng)計分析等領域內(nèi)的先進方法,對梁型結(jié)構(gòu)損傷識別的信號處理、特征參數(shù)識別等方面進行了探索。論文的主要工作內(nèi)容體現(xiàn)在以下幾個方面： (1)針對交通荷載的移動特性,通過研究和實踐提出了用移動荷載作用于梁型結(jié)構(gòu)上,對其產(chǎn)生的振動響應數(shù)據(jù)進行分析。當移動荷載作用于梁型結(jié)構(gòu)上時,損傷引起的振動響應數(shù)據(jù)特征參數(shù)的變化將被放大,可以提高損傷檢測特征參數(shù)的提取精度。 (2)橋梁結(jié)構(gòu)健康監(jiān)測過程中獲得的觀測數(shù)據(jù)具有非線性、非平穩(wěn)等復雜性,樣本熵可以有效地表征信號的復雜性,估計信號的非線性程度,本文提出了使用樣本熵來提取結(jié)構(gòu)損傷信息,并且使用經(jīng)驗模式分解及神經(jīng)網(wǎng)絡對該方法進行了進一步的改進。 (3)當移動荷載作用于有損傷的結(jié)構(gòu)上時,產(chǎn)生的振動信號是非平穩(wěn)信號,橋梁損傷結(jié)構(gòu)振動響應的統(tǒng)計量將隨著時間和載荷而變化。本文提出了梁型結(jié)構(gòu)損傷分析的時頻分析方法,并對時頻分析中的交叉項問題進行了討論,分析了抑制交叉項干擾的方法,并結(jié)合信息熵、神經(jīng)網(wǎng)絡進行結(jié)構(gòu)損傷識別。 (4)信號的高階統(tǒng)計量具有良好的非高斯、非平穩(wěn)信號處理能力,本文提出了損傷結(jié)構(gòu)振動信號的高階譜分析方法。由于高階譜分析結(jié)果為二維甚至更高維的,包含的信息量大,因此本文提出了雙譜的有效值熵分析方法,結(jié)合神經(jīng)網(wǎng)絡的模式識別能力進行結(jié)構(gòu)的損傷識別。 (5)為充分挖掘高階譜分析結(jié)果中所包含的結(jié)構(gòu)損傷信息,需要配合有效的降維分析方法。本文提出了一種改進的有監(jiān)督保局投影數(shù)據(jù)降維方法,通過該方法對高階譜分析結(jié)果進行特征向量的提取,并將損傷識別過程分為兩個模塊進行綜合信息融合,運用神經(jīng)網(wǎng)絡進一步進行損傷識別。
[Abstract]:Beam structure is widely used in all aspects of national economy, especially bridges. As an important part of transportation, bridge is the focus of a country's infrastructure construction, and also a symbol of economic development and technological progress. In recent years, the health diagnosis technology of beam structure has become a hot topic in engineering field, and great progress has been made in the research of health diagnosis system and its theory. However, because of the interdisciplinary nature of the health diagnosis system and the complexity and uncertainty of the large beam structure and its environment, many key technologies of the health monitoring system for the beam structure still have many shortcomings from theory to practice. In this paper, some key technical problems in the process of beam structure health monitoring are studied by combining theory with experiment. On the basis of damage identification technology based on structural vibration characteristics, advanced methods in the fields of information theory, signal processing, time-frequency analysis and statistical analysis are adopted to process the damage of beam structure. The characteristic parameter identification and other aspects are explored. The main contents of this paper are as follows: (1) aiming at the moving characteristics of traffic load, the vibration response data generated by moving load acting on beam structure are analyzed through research and practice. When the moving load acts on the beam structure, the change of the characteristic parameters of the vibration response data caused by the damage will be amplified. It can improve the accuracy of extracting characteristic parameters of damage detection. The observed data obtained in the process of bridge structure health monitoring are nonlinear, non-stationary and so on, and the sample entropy can effectively characterize the complexity of the signal. In order to estimate the nonlinearity of the signal, the sample entropy is used to extract the structural damage information. The method is further improved by empirical mode decomposition and neural network. When moving load acts on the damaged structure, the vibration signal is non-stationary. The statistics of vibration response of damaged bridge structure will change with time and load. In this paper, a time-frequency analysis method for damage analysis of beam structures is presented. The crossover terms in time-frequency analysis are discussed, and the methods to suppress cross-term interference are analyzed, and the information entropy is combined. Neural network is used to identify structural damage. The high-order statistic of the signal has good ability of non-Gao Si and non-stationary signal processing. In this paper, a method of high-order spectrum analysis for the vibration signal of damaged structure is proposed. Since the results of high-order spectral analysis are two-dimensional or higher, and contain a large amount of information, a bispectral effective value entropy analysis method is proposed in this paper. In order to fully mine the structural damage information contained in the results of higher-order spectral analysis, it is necessary to cooperate with an effective dimensionality reduction analysis method for structural damage identification based on the pattern recognition ability of neural networks. In this paper, an improved dimensionality reduction method for supervised local projection data is proposed, by which the feature vectors are extracted from the results of high-order spectral analysis, and the damage identification process is divided into two modules for comprehensive information fusion. Further damage identification using neural network.
【學位授予單位】：浙江大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TU317

【參考文獻】

相關(guān)期刊論文 前10條

1 王毅娟;從橋梁事故分析中得到的警示[J];北京建筑工程學院學報;2003年03期

2 林兵;鄭丹;周建庭;逄立偉;徐斌;代陽;胡志全;;西南地區(qū)橋梁壽命預測分析[J];重慶交通大學學報(自然科學版);2008年03期

3 楊英杰，，虞和濟;結(jié)構(gòu)損傷狀態(tài)識別的神經(jīng)網(wǎng)絡方法[J];東北大學學報;1994年02期

4 欒英泉,梁力;橋梁無損健康監(jiān)測系統(tǒng)研究簡介[J];東北公路;2000年01期

5 李愛群,繆長青,李兆霞,韓曉林,吳勝東,吉林,楊玉冬;潤揚長江大橋結(jié)構(gòu)健康監(jiān)測系統(tǒng)研究[J];東南大學學報(自然科學版);2003年05期

6 丁濤;殷志祥;;基于ANSYS的橋梁強迫振動分析[J];遼寧工程技術(shù)大學學報;2005年S2期

7 陳鐵晗;基于Bayes估計的有限元模型修正[J];福州大學學報(自然科學版);2002年05期

8 劉斌云;霍達;;現(xiàn)有混凝土橋梁結(jié)構(gòu)剩余壽命預測方法研究[J];工程建設與設計;2008年02期

9 陳德成,魏震松,曲廣吉,朱安文,肖益芳;有限元模型修正技術(shù)的工程應用[J];中國工程科學;2001年10期

10 陸秋海,李德葆,張維;利用模態(tài)試驗參數(shù)識別結(jié)構(gòu)損傷的神經(jīng)網(wǎng)絡法[J];工程力學;1999年01期

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