【摘要】：復(fù)合材料層合結(jié)構(gòu)的破壞往往起源于界面層。在長期荷載作用下,大型、復(fù)雜的結(jié)構(gòu),由于自然災(zāi)害、外部環(huán)境、結(jié)構(gòu)本身荷載等因素都會(huì)使界面產(chǎn)生損傷,且這種損傷是不易察覺的,從而使結(jié)構(gòu)的力學(xué)性能嚴(yán)重退化,甚至發(fā)生整體破壞。為了充分利用層合結(jié)構(gòu)的性能,提高其使用價(jià)值,對(duì)界面力學(xué)行為的研究十分必要。同時(shí),為保證結(jié)構(gòu)的安全性,需采取有效手段對(duì)其進(jìn)行實(shí)時(shí)健康監(jiān)測(cè)。壓電阻抗法(EMI)是一種基于智能材料的結(jié)構(gòu)健康檢測(cè)技術(shù),因其對(duì)結(jié)構(gòu)早期微小損傷比較敏感,常被用在土木工程等領(lǐng)域。通過建立壓電阻抗模型,提取包含結(jié)構(gòu)損傷信息的EMI信號(hào)并進(jìn)行分析,從而對(duì)損傷進(jìn)行判定。但在實(shí)際工程中,EMI信號(hào)數(shù)據(jù)龐大,怎樣對(duì)這些數(shù)據(jù)進(jìn)行快速有效的處理一直是研究的重點(diǎn)。壓縮感知(Compression sensing)理論與BP神經(jīng)網(wǎng)絡(luò)相結(jié)合為大量數(shù)據(jù)的處理提供了一種可行的方式。壓縮感知方法可以對(duì)信號(hào)進(jìn)行無失真壓縮,大大降低了它的維數(shù)。本文對(duì)復(fù)合材料層合板的弱界面問題進(jìn)行研究。引入粘彈性界面本構(gòu)關(guān)系模擬界面特性,利用狀態(tài)空間法求解彈性力學(xué)解析解。同時(shí),考察含壓電層層合板的機(jī)電效應(yīng),導(dǎo)出電導(dǎo)納關(guān)系式,驗(yàn)證EMI技術(shù)的可行性。然后,建立各向同性及各向異性層合板EMI有限元模型,通過賦予層間彈簧單元不同的剛度系數(shù)模擬結(jié)構(gòu)不同界面損傷程度,提取出相應(yīng)的EMI信號(hào)。利用壓縮感知理論對(duì)信號(hào)進(jìn)行壓縮,以提高信號(hào)傳輸和存儲(chǔ)能力。為了進(jìn)一步降低信號(hào)的維數(shù),再進(jìn)行主成分分析,將分析后的主成分特征值輸入BP神經(jīng)網(wǎng)絡(luò),實(shí)現(xiàn)損傷信號(hào)的鑒定與分類。結(jié)果表明EMI技術(shù)能夠?qū)雍辖Y(jié)構(gòu)界面損傷進(jìn)行檢測(cè),同時(shí)壓縮感知與BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的方式能夠有效降低數(shù)據(jù)的維數(shù),實(shí)現(xiàn)信號(hào)的快速傳輸與存儲(chǔ),并能對(duì)界面損傷進(jìn)行定量歸類,為工程健康監(jiān)測(cè)提供了一種可行的方式。
[Abstract]:The failure of composite laminated structure often originates from interfacial layer. Under the action of long-term load, large and complex structures, due to natural disasters, external environment, load of the structure itself and other factors will cause damage to the interface, and this damage is not easy to detect, thus making the mechanical properties of the structure seriously degraded. There is even total destruction. In order to make full use of the properties of laminated structure and improve its use value, it is necessary to study the mechanical behavior of interface. At the same time, in order to ensure the safety of the structure, it is necessary to take effective means to carry out real-time health monitoring. Piezoelectric impedance method (EMI) is a kind of smart material based structural health detection technology. It is often used in civil engineering because of its sensitivity to early minor damage of structures. The piezoelectric impedance model is established to extract and analyze the EMI signal which contains the damage information of the structure, so as to determine the damage. However, in practical engineering, EMI signal data is huge, how to deal with these data quickly and effectively has been the focus of research. The combination of compressed perceptual (Compression sensing) theory and BP neural network provides a feasible way to deal with a large number of data. The compression sensing method can compress the signal without distortion, which greatly reduces its dimension. In this paper, the weak interface of composite laminates is studied. The viscoelastic interface constitutive relation is introduced to simulate the interface properties and the state space method is used to solve the analytical solution of elastic mechanics. At the same time, the electromechanical effect of piezoelectric laminated plates is investigated, and the admittance relation is derived to verify the feasibility of EMI technology. Then, the EMI finite element model of isotropic and anisotropic laminated plates is established, and the corresponding EMI signals are extracted by simulating the different interface damage degree of the structure with different stiffness coefficients of the interlayer spring element. The compression sensing theory is used to compress the signal to improve the signal transmission and storage capacity. In order to further reduce the dimension of the signal, principal component analysis (PCA) is carried out, and the eigenvalue of the principal component is input into BP neural network to identify and classify the damage signal. The results show that EMI can detect the interface damage of laminated structure, and the combination of compression sensing and BP neural network can effectively reduce the dimension of data and realize the fast transmission and storage of signals. The interface damage can be classified quantitatively, which provides a feasible method for engineering health monitoring.
【學(xué)位授予單位】：寧波大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU317

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