本文選題：粒子濾波器 + 損傷識(shí)別　； 參考：《東南大學(xué)》2016年碩士論文

【摘要】：眾所周知,結(jié)構(gòu)從投入使用開始,就面臨著環(huán)境的侵蝕、材料的老化、荷載的長(zhǎng)期作用、突變效應(yīng)以及疲勞效應(yīng)等因素的耦合作用,這將難以避免地引發(fā)結(jié)構(gòu)的抗力衰減和損傷累積,最終導(dǎo)致結(jié)構(gòu)損傷和破壞。中國(guó)建筑科學(xué)院關(guān)于我國(guó)建筑結(jié)構(gòu)的調(diào)查研究表明,由于建筑結(jié)構(gòu)的設(shè)計(jì)、施工和管理方面的原因,對(duì)于絕大多數(shù)的既有建筑結(jié)構(gòu)來說均存在著不同程度的損傷。另外,我國(guó)早期的工業(yè)民用建筑、辦公樓以及一些橋梁,已經(jīng)接近設(shè)計(jì)基準(zhǔn)年限。這些結(jié)構(gòu)物在復(fù)雜的服役環(huán)境下,也已經(jīng)受到了不同程度的損傷。準(zhǔn)確判斷結(jié)構(gòu)的損傷部位和損傷程度,既可以確保結(jié)構(gòu)的安全性和完整性,對(duì)結(jié)構(gòu)的可靠度進(jìn)行實(shí)時(shí)評(píng)定、避免災(zāi)難性的悲劇發(fā)生,也可以對(duì)既有建筑結(jié)構(gòu)做出科學(xué)合理的維修和加固方案、減少維護(hù)費(fèi)用、提高維護(hù)效率、對(duì)保障社會(huì)和人民的財(cái)產(chǎn)免受不必要的損失、加深對(duì)結(jié)構(gòu)的性能的理解和研究,同時(shí)對(duì)促進(jìn)工程結(jié)構(gòu)實(shí)踐領(lǐng)域的進(jìn)一步發(fā)展研究均具有重要的現(xiàn)實(shí)意義。隨著結(jié)構(gòu)健康監(jiān)測(cè)越來越受到人們的重視,對(duì)基于結(jié)構(gòu)激勵(lì)和響應(yīng)識(shí)別結(jié)構(gòu)參數(shù)的方法進(jìn)行了深入研究。目前的結(jié)構(gòu)損傷識(shí)別很多都是基于結(jié)構(gòu)參數(shù)識(shí)別的基礎(chǔ)上進(jìn)行的,即工程結(jié)構(gòu)發(fā)生損傷時(shí),結(jié)構(gòu)的參數(shù)不可避免地要發(fā)生改變。此時(shí),如果能很好地識(shí)別結(jié)構(gòu)的參數(shù)變化,便可以發(fā)現(xiàn)結(jié)構(gòu)損傷的特征。貝葉斯模型修正法就是利用統(tǒng)計(jì)學(xué)里面的貝葉斯原理,將確定性的結(jié)構(gòu)模型嵌入到一組可能的概率性模型中,使結(jié)構(gòu)模型能夠量化模型預(yù)測(cè)和觀測(cè)的不確定性。貝葉斯識(shí)別方法的基本思路就是將所要估計(jì)的結(jié)構(gòu)參數(shù)看作隨機(jī)變量,通過觀測(cè)和分析與該參數(shù)相關(guān)的其他變量,以此來推斷這個(gè)參數(shù)值。對(duì)于線性高斯?fàn)顟B(tài)空間模型,卡爾曼濾波方法可以得到后驗(yàn)概率密度的解析表達(dá)式。而實(shí)際情況中的數(shù)據(jù)通常比較復(fù)雜,包含有非高斯和非線性的情況。對(duì)于這一問題的解決,學(xué)者們做了大量的研究,提出了擴(kuò)展卡爾曼濾波和高斯求和濾波的方法。但是這兩種方法均沒有考慮過程中的全部統(tǒng)計(jì)特性,而產(chǎn)生較差的識(shí)別結(jié)果。而對(duì)于非線性非高斯的情況,問題的解決將變得更加復(fù)雜。粒子濾波算法以其對(duì)線性和非線性結(jié)構(gòu)參數(shù)識(shí)別的適用性和有效性脫穎而出,得到人們的廣泛關(guān)注。本文對(duì)粒子濾波在結(jié)構(gòu)參數(shù)識(shí)別和結(jié)構(gòu)損傷識(shí)別方面進(jìn)行了分析研究,主要研究?jī)?nèi)容如下：(1)本文首先介紹了結(jié)構(gòu)損傷識(shí)別的發(fā)展歷程以及粒子濾波的主要應(yīng)用領(lǐng)域和研究現(xiàn)狀；其次,對(duì)粒子濾波在結(jié)構(gòu)參數(shù)識(shí)別領(lǐng)域的優(yōu)勢(shì)進(jìn)行了歸納性介紹,指出粒子濾波在結(jié)構(gòu)參數(shù)識(shí)別領(lǐng)域具有突出的優(yōu)勢(shì)。再則,對(duì)目前人們?cè)诹Ｗ訛V波應(yīng)用研究當(dāng)中碰到的一些難點(diǎn)問題進(jìn)行了總結(jié)歸納；最后闡明了本文的主要研究?jī)?nèi)容。(2)提出并介紹了一種基于最大似然值的結(jié)構(gòu)參數(shù)識(shí)別方法。在不同高斯和非高斯噪聲水平下,對(duì)單自由度結(jié)構(gòu)的參數(shù)識(shí)別進(jìn)行了數(shù)值仿真分析,并歸納總結(jié)了基于最大似然值的結(jié)構(gòu)參數(shù)識(shí)別方法的優(yōu)劣性。(3)介紹說明了粒子濾波算法在結(jié)構(gòu)參數(shù)和損傷識(shí)別中的基本原理。同時(shí),對(duì)粒子濾波算法在單自由度結(jié)構(gòu)參數(shù)和損傷識(shí)別中的過程進(jìn)行了詳細(xì)的說明和系統(tǒng)的討論。在不同高斯和非高斯噪聲水平下,基于粒子濾波算法對(duì)單自由度結(jié)構(gòu)的參數(shù)和損傷識(shí)別進(jìn)行了數(shù)值仿真分析,并歸納總結(jié)基于粒子濾波器的結(jié)構(gòu)參數(shù)和損傷識(shí)別方法的優(yōu)劣性。另外,基于單自由度結(jié)構(gòu)參數(shù)和損傷識(shí)別的數(shù)值仿真結(jié)果,對(duì)參數(shù)和損傷識(shí)別結(jié)果的可靠度進(jìn)行了簡(jiǎn)要的分析。(4)在單自由度結(jié)構(gòu)參數(shù)和損傷識(shí)別的研究基礎(chǔ)上,將基于粒子濾波器的結(jié)構(gòu)參數(shù)和損傷識(shí)別方法拓展至多自由度結(jié)構(gòu)。分別對(duì)在不同高斯和非高斯噪聲水平下,多自由度結(jié)構(gòu)參數(shù)和損傷識(shí)別進(jìn)行了數(shù)值仿真,分析結(jié)構(gòu)參數(shù)和損傷的識(shí)別結(jié)果,總結(jié)歸納基于粒子濾波算法的多自由度結(jié)構(gòu)參數(shù)和損傷識(shí)別的性能。并且通過一個(gè)4層鋁框架的實(shí)驗(yàn)結(jié)構(gòu)模型對(duì)該算法的參數(shù)和損傷識(shí)別性能進(jìn)行了實(shí)驗(yàn)驗(yàn)證分析,對(duì)比實(shí)驗(yàn)識(shí)別結(jié)果與結(jié)構(gòu)層間剛度的測(cè)量值,通過實(shí)驗(yàn)證實(shí)了粒子濾波算法在多自由度結(jié)構(gòu)剛度損傷識(shí)別中的有效性。(5)總結(jié)了粒子濾波算法在結(jié)構(gòu)參數(shù)和損傷識(shí)別領(lǐng)域的優(yōu)越性,并對(duì)粒子濾波算法的應(yīng)用前景進(jìn)行展望。粒子濾波算法作為結(jié)構(gòu)健康監(jiān)測(cè)領(lǐng)域比較新穎的方法,作者根據(jù)自身的研究工作對(duì)粒子濾波算法中存在的問題和不足進(jìn)行了歸納總結(jié)。最后,對(duì)粒子濾波算法下一步的研究工作進(jìn)行了一些討論和展望。
[Abstract]:As we all know, from the beginning of use, the structure is confronted with the coupling of environmental erosion, aging of materials, the long-term effect of load, catastrophe effect and fatigue effect, which will inevitably lead to the attenuation and damage accumulation of structural resistance and damage, and eventually lead to damage and damage to the structure. The structural investigation shows that, due to the design, construction and management of the architectural structure, there are different degrees of damage to most of the existing construction structures. In addition, the early industrial and civil buildings, office buildings and some bridges in our country are close to the design reference years. These structures are in complex service. In the environment, it has also been damaged in different degrees. The accurate judgment of the damage location and degree of the structure can not only ensure the safety and integrity of the structure, but also evaluate the reliability of the structure in real time, avoid the catastrophic tragedy, and make a scientific and rational maintenance and reinforcement scheme for the existing building structure, and reduce the dimension of the structure. To protect the cost, improve the efficiency of maintenance, to protect the society and the people's property from unnecessary losses, to deepen the understanding and research of the structural performance, and to promote the further development of the engineering structure, is of great practical significance. The method of excitation and response identification of structural parameters is studied deeply. Many of the current structural damage identification are based on structural parameter identification, that is, when the structure is damaged, the structural parameters will inevitably change. In this case, if the structure parameters can be identified well, the structure can be found. The Bias model correction method is to use the Bias principle in statistics to embed the deterministic structural model into a set of possible probabilistic models, so that the structural model can quantify the uncertainty of the model prediction and observation. The basic idea of the Bias recognition method is to consider the structural parameters to be estimated. Random variables, by observing and analyzing other variables related to the parameter, deduce the value of the parameter. For the linear Gauss state space model, the Calman filtering method can obtain the analytic expression of the posterior probability density. The data in the actual situation are usually complex, including non Gauss and nonlinear cases. The solution of this problem, scholars have done a lot of research, and put forward the method of expanding Calman filtering and Gauss sum filtering. However, these two methods do not consider all the statistical characteristics in the process, and produce poor identification results. For nonlinear non Gauss, the solution of the problem will become more complex. Particle filtering is more complex. The algorithm is widely paid attention to the applicability and effectiveness of linear and nonlinear structural parameters identification. This paper studies the structure parameter identification and structural damage identification of particle filter. The main research contents are as follows: (1) this paper first introduces the development process of structural damage identification. The main application fields and research status of particle filtering are introduced. Secondly, the advantages of particle filtering in the field of structural parameter identification are introduced. It is pointed out that particle filtering has a prominent advantage in the field of structural parameter identification. Then, some difficult problems that people have encountered in the research of particle filtering are summarized. Finally, the main contents of this paper are clarified. (2) a structural parameter identification method based on maximum likelihood is proposed and introduced. The parameter identification of the single degree of freedom structure is numerically simulated under different Gauss and non Gauss noise levels, and the structural parameter identification based on the maximum likelihood is summed up. The advantages and disadvantages of the method. (3) the basic principle of particle filter algorithm in structural parameters and damage identification is introduced. At the same time, the process of particle filter algorithm in single degree of freedom structure parameters and damage identification is explained in detail and the system is discussed. Under different Gauss and non Gauss noise level, particle filter algorithm pair The parameters of the degree of freedom structure and the damage identification are numerically simulated, and the advantages and disadvantages of the structural parameters based on the particle filter and the damage identification method are summarized. In addition, the reliability of the parameters and the damage identification results is briefly analyzed based on the single degree of freedom structure parameters and the numerical simulation results of damage identification. (4) On the basis of single degree of freedom structural parameters and damage identification, the structure parameters and damage identification methods based on particle filter are extended to the most free degree structure. The number of parameters and damage identification of multi degree of freedom are simulated under different Gauss and non Gauss noise levels, and the structural parameters and the identification of damage are analyzed. As a result, the parameters of the multi degree of freedom structure and the performance of damage identification based on the particle filter algorithm are summarized, and the experimental structure model of a 4 layer aluminum frame is used to verify the parameters and the damage identification performance of the algorithm. The results of the experimental identification and the measurement of the stiffness between the structure layers are compared and the particles are verified by the experiment. The effectiveness of filtering algorithm in multi degree of freedom structural stiffness damage identification. (5) the superiority of particle filter algorithm in the field of structural parameters and damage identification is summarized, and the prospect of application of particle filtering algorithm is prospected. The particle filter algorithm is a more novel method in the field of structural health monitoring. The author is based on the research work of the author. The problems and shortcomings in the particle filtering algorithm are summarized. Finally, the research work of the next step of the particle filter algorithm is discussed and prospected.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TU317

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