【摘要】：結(jié)構(gòu)在使用的過程中,由于各種原因可能會出現(xiàn)不同程度的損傷,當(dāng)這些損傷累積到一定的程度時,將會導(dǎo)致結(jié)構(gòu)的剛度和承載力的下降,進(jìn)而影響整個結(jié)構(gòu)的使用性和耐久性,嚴(yán)重時還可能會引發(fā)災(zāi)難性的事故,造成巨大的經(jīng)濟(jì)損失和人員傷亡。因此,如何快速有效地識別出結(jié)構(gòu)的損傷位置以及結(jié)構(gòu)的損傷程度,已經(jīng)成為當(dāng)前工程結(jié)構(gòu)損傷診斷研究領(lǐng)域的一項重要研究課題。小波分析作為一種時-頻兩域信號處理方法,能夠在時域和頻域較好的表征出信號的局部特性;神經(jīng)網(wǎng)絡(luò)算法擁有高度的非線性映射能力,對信號處理方面具有自組織、自學(xué)習(xí)、自適應(yīng)能力。結(jié)合兩者的優(yōu)點,本文建立了基于小波分析和神經(jīng)網(wǎng)絡(luò)相結(jié)合的理論方法,通過小波分析得出的小波系數(shù)圖判斷出結(jié)構(gòu)的損傷位置,并基于小波分析得出的小波系數(shù)模極大值,利用神經(jīng)網(wǎng)絡(luò)識別出結(jié)構(gòu)的損傷程度,因此,通過小波分析和神經(jīng)網(wǎng)絡(luò)兩種方法的結(jié)合,可以實現(xiàn)對結(jié)構(gòu)損傷位置和損傷程度的有效識別。本文以含有損傷的簡支梁為研究對象,建立了基于振型模態(tài)、轉(zhuǎn)角模態(tài)、曲率模態(tài)的損傷識別方法,對簡支梁含有一處損傷和多處損傷的裂縫位置進(jìn)行有效的識別,并對比了這三種模態(tài)下的損傷識別效果；然后對梁的模態(tài)參數(shù)進(jìn)行小波變換得出小波系數(shù)圖,利用神經(jīng)網(wǎng)絡(luò)去模擬小波系數(shù)模極大值與損傷程度之間的非線性關(guān)系來識別結(jié)構(gòu)的損傷程度。數(shù)值模擬分析表明,小波分析和神經(jīng)網(wǎng)絡(luò)的結(jié)合可以有效地識別出結(jié)構(gòu)的損傷位置和損傷程度。本文以含有損傷的連續(xù)梁為研究對象,建立了含有一處損傷、二處損傷和多處損傷的有限元模型,對各損傷工況分別基于振型模態(tài)、轉(zhuǎn)角模態(tài)、曲率模態(tài)下進(jìn)行小波變換,通過小波系數(shù)圖來識別出結(jié)構(gòu)的損傷位置；然后利用神經(jīng)網(wǎng)絡(luò)去模擬小波系數(shù)模極大值與損傷程度之間的非線性關(guān)系,由神經(jīng)網(wǎng)絡(luò)的輸出結(jié)果識別出結(jié)構(gòu)的損傷程度。分析結(jié)果表明,將小波分析和神經(jīng)網(wǎng)絡(luò)相結(jié)合,準(zhǔn)確的識別出了結(jié)構(gòu)的損傷位置和損傷程度。因此本文方法對結(jié)構(gòu)的損傷診斷具有重要的指導(dǎo)意義。
[Abstract]:During the use of the structure, there may be different degrees of damage due to various reasons. When the damage accumulates to a certain extent, it will lead to the decrease of the stiffness and bearing capacity of the structure. Furthermore, it will affect the durability and usability of the whole structure, and may lead to catastrophic accidents when serious, resulting in huge economic losses and casualties. Therefore, how to identify the damage location and damage degree of structures quickly and effectively has become an important research topic in the field of structural damage diagnosis. As a time-frequency two-domain signal processing method, wavelet analysis can better characterize the local characteristics of the signal in time domain and frequency domain. The neural network algorithm has high ability of nonlinear mapping, self-organizing, self-learning and adaptive in signal processing. Combining the advantages of the two methods, a theoretical method based on the combination of wavelet analysis and neural network is established in this paper. The damage location of the structure is judged by the wavelet coefficient graph obtained by wavelet analysis, and the modulus maximum of wavelet coefficient is obtained based on wavelet analysis. The damage degree of structure can be recognized by neural network, so the location and degree of damage can be effectively identified by combining wavelet analysis and neural network. In this paper, the damage identification method of simply supported beam with damage is established based on mode, rotation mode and curvature mode. The crack location of simply supported beam with one or more damage is effectively identified. The effects of damage identification in these three modes are compared. Then the wavelet transform of the modal parameters of the beam is carried out to obtain the wavelet coefficient graph and the nonlinear relationship between the modulus maximum of the wavelet coefficient and the degree of damage is simulated by using the neural network to identify the damage degree of the structure. Numerical simulation shows that the combination of wavelet analysis and neural network can effectively identify the damage location and damage degree of the structure. In this paper, a finite element model with one damage, two damage and multiple damage is established for continuous beam with damage. Wavelet transform is carried out for each damage condition based on mode, rotation mode, curvature mode, respectively. The damage location of the structure is identified by wavelet coefficient graph. Then the nonlinear relationship between the modulus maximum of wavelet coefficients and the degree of damage is simulated by the neural network, and the damage degree of the structure is identified by the output result of the neural network. The results show that the wavelet analysis and neural network are combined to identify the damage location and damage degree of the structure accurately. Therefore, this method has an important guiding significance for structural damage diagnosis.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU317

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本文編號：2364607