压电智能结构损伤检测及其传感器优化配置的研究
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摘要
实现损伤自检测功能是智能结构研究的主要内容之一,而损伤检测方法和传感器的优化配置是关联损伤自检测功能的两个重要问题,这两个问题的研究,对智能结构的应用具有重要的理论意义和实用价值。论文针对压电智能复合材料层板,以有限元数值仿真为主要手段,对低速冲击加载方式下各压电传感器瞬态响应信号的特征进行提取,继而进行压电智能结构损伤检测方法及其传感器优化配置方面的研究。
论文采用最小二乘支持向量机(LS-SVM)的回归原理,对压电智能复合材料层板冲击损伤位置检测进行了研究,并与改进的BP网络进行了对比。结果表明,LS-SVM有比BP网络更快的训练速度、更强的泛化能力,并且LS-SVM具有不敏感于网络输入矢量次序的变换,表现出较强的适应性,适宜在结构损伤检测传感器优化配置问题中建立损伤检测目标函数。
论文提出了一种基于损伤检测的智能结构传感器优化配置的遗传神经网络方法。该方法采用最小二乘支持向量机(LS-SVM)网络建立损伤检测目标函数,运用改进的遗传算法对目标函数进行优化,从而实现不同数目传感器的优化布置,并综合考虑成本与效益的因素,确定传感器的最优配置数目。论文对该遗传神经网络方法的具体实现过程及其可行性进行了分析,结果表明,该方法是可行的,可用于实现传感器对应于其初始布置模式下的最优配置。对于更多传感器的初始布置模式,采用该方法可有效减少更多传感器的数量,从而降低成本。论文从试验角度,采用主动监测方案,对该遗传神经网络方法进行了一定的分析,对试验中采集的传感器响应信号采用功率谱密度最大值进行了特征提取。
论文对机翼盒段试验件进行了结构分析,采用有限单元方法,建立了其结构的有限元模型,并进行了冲击压电响应数值仿真。应用该遗传神经网络方法,基于冲击损伤位置检测问题,对机翼盒段试验件压电传感器进行了优化配置,得到了传感器对应于其初始布置模式下的最优配置,为该结构试验件的实际压电传感器的优化配置提供指导依据。
本文的研究得到了国家自然科学基金(90205031)的资助。
The research on realizing the self-detecting damage function is one of the main research contents of smart materials and structures. There are two important problems that are related to the self-detecting damage function. One is the method of damage detection; the other is the problem of optimal sensor placement. It has been of an important theoretical meaning and a great practical value for applications of smart materials and structures to research on these two problems. Based on the finite element numerical simulation, the piezoelectric smart composite laminated plates is simulated, and the transient responsive signals of piezoelectric sensors and their characteristics are obtained under the low-velocity impact load. On the basis of the above, the methods of damage detection and optimal sensor placement for smart materials and structures are researched in this dissertation.
Support Vector Machine (SVM) based on VC Theory and Structural Risk Minimization Principle is progressing rapidly in recent years, and has become a very young and useful component of Statistical Learning Theory. Nowadays SVM become an ideal network model for pattern recognition and nonlinear regression. In this dissertation, the regression theory of Least Square Support Vector Machine (LS-SVM) is applied to detect the impact damage locations for the piezoelectric smart composite laminated plates, and compared with the improved BP neural network. The results state clearly that, LS-SVM possesses the advantages such as the faster speed, better dissemination ability etc. And LS-SVM is not sensitive to the order transform of network imput vectors, especially meets the requirements of establishing the objective function based on damage detection for the problem of optimal sensor placement.
The method of genetic algorithm integrated neural network is proposed to optimize sensor placement based on damage detection for smart structures. In this method, LS-SVM is adopted as a kind of neural networks to establish the performance function based on damage detection, and genetic algorithm is applied to optimize the performance function. Considered the cost-effective factor roundly, the optimal number of sensors can be determined through the method. The implementation process and feasibility of the method of genetic algorithm integrated neural network are analyzed in this dissertation. The results show that the method is feasible, and can be applied to realize the optimal sensor placement corresponding to its primal sensor placement. For the more sensors primal placement, the number of sensors can be reduced effectively through the method. Moreover, this method is analyzed through the test based on the active monitoring scheme. In the test, the characteristics of sensors’responsive signals are extracted based on the method of Power Spectrum Density Maximum.
In this dissertation, based on the finite element method, the wing box specimen of a plane is simulated, and its piezoelectric responsive signals are obtained under the impact load firstly. Then the method of genetic algorithm integrated neural network is applied to determine the optimum piezoelectric sensor placement for the wing box specimen of a plane based on damage detection. The result of optimizing sensor placement corresponding to its primal sensor placement is obtained, and can give a certain of guidance for the practical piezoelectric sensor placement for the wing box specimen of a plane.
The research is partially supported by the grant from National Natural Science Foundation of China(90205031).
论文采用最小二乘支持向量机(LS-SVM)的回归原理,对压电智能复合材料层板冲击损伤位置检测进行了研究,并与改进的BP网络进行了对比。结果表明,LS-SVM有比BP网络更快的训练速度、更强的泛化能力,并且LS-SVM具有不敏感于网络输入矢量次序的变换,表现出较强的适应性,适宜在结构损伤检测传感器优化配置问题中建立损伤检测目标函数。
论文提出了一种基于损伤检测的智能结构传感器优化配置的遗传神经网络方法。该方法采用最小二乘支持向量机(LS-SVM)网络建立损伤检测目标函数,运用改进的遗传算法对目标函数进行优化,从而实现不同数目传感器的优化布置,并综合考虑成本与效益的因素,确定传感器的最优配置数目。论文对该遗传神经网络方法的具体实现过程及其可行性进行了分析,结果表明,该方法是可行的,可用于实现传感器对应于其初始布置模式下的最优配置。对于更多传感器的初始布置模式,采用该方法可有效减少更多传感器的数量,从而降低成本。论文从试验角度,采用主动监测方案,对该遗传神经网络方法进行了一定的分析,对试验中采集的传感器响应信号采用功率谱密度最大值进行了特征提取。
论文对机翼盒段试验件进行了结构分析,采用有限单元方法,建立了其结构的有限元模型,并进行了冲击压电响应数值仿真。应用该遗传神经网络方法,基于冲击损伤位置检测问题,对机翼盒段试验件压电传感器进行了优化配置,得到了传感器对应于其初始布置模式下的最优配置,为该结构试验件的实际压电传感器的优化配置提供指导依据。
本文的研究得到了国家自然科学基金(90205031)的资助。
The research on realizing the self-detecting damage function is one of the main research contents of smart materials and structures. There are two important problems that are related to the self-detecting damage function. One is the method of damage detection; the other is the problem of optimal sensor placement. It has been of an important theoretical meaning and a great practical value for applications of smart materials and structures to research on these two problems. Based on the finite element numerical simulation, the piezoelectric smart composite laminated plates is simulated, and the transient responsive signals of piezoelectric sensors and their characteristics are obtained under the low-velocity impact load. On the basis of the above, the methods of damage detection and optimal sensor placement for smart materials and structures are researched in this dissertation.
Support Vector Machine (SVM) based on VC Theory and Structural Risk Minimization Principle is progressing rapidly in recent years, and has become a very young and useful component of Statistical Learning Theory. Nowadays SVM become an ideal network model for pattern recognition and nonlinear regression. In this dissertation, the regression theory of Least Square Support Vector Machine (LS-SVM) is applied to detect the impact damage locations for the piezoelectric smart composite laminated plates, and compared with the improved BP neural network. The results state clearly that, LS-SVM possesses the advantages such as the faster speed, better dissemination ability etc. And LS-SVM is not sensitive to the order transform of network imput vectors, especially meets the requirements of establishing the objective function based on damage detection for the problem of optimal sensor placement.
The method of genetic algorithm integrated neural network is proposed to optimize sensor placement based on damage detection for smart structures. In this method, LS-SVM is adopted as a kind of neural networks to establish the performance function based on damage detection, and genetic algorithm is applied to optimize the performance function. Considered the cost-effective factor roundly, the optimal number of sensors can be determined through the method. The implementation process and feasibility of the method of genetic algorithm integrated neural network are analyzed in this dissertation. The results show that the method is feasible, and can be applied to realize the optimal sensor placement corresponding to its primal sensor placement. For the more sensors primal placement, the number of sensors can be reduced effectively through the method. Moreover, this method is analyzed through the test based on the active monitoring scheme. In the test, the characteristics of sensors’responsive signals are extracted based on the method of Power Spectrum Density Maximum.
In this dissertation, based on the finite element method, the wing box specimen of a plane is simulated, and its piezoelectric responsive signals are obtained under the impact load firstly. Then the method of genetic algorithm integrated neural network is applied to determine the optimum piezoelectric sensor placement for the wing box specimen of a plane based on damage detection. The result of optimizing sensor placement corresponding to its primal sensor placement is obtained, and can give a certain of guidance for the practical piezoelectric sensor placement for the wing box specimen of a plane.
The research is partially supported by the grant from National Natural Science Foundation of China(90205031).
引文
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