基于现代信号处理的结构模态参数识别与损伤识别研究
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摘要
随着社会的发展,各种土木结构、钢结构在城市建设、水利水电、电力输送,新能源等领域得到十分广泛的应用,并呈现出大型化、复杂化和多样化的发展趋势。这些大型结构与生活息息相关,其可靠性和安全性就备受关注,也因此结构的健康状况评估和健康监测成为一个不容忽视的问题。模态参数识别和损伤识别作为结构健康监测的核心技术,受到了工程界和学术界的重视,此前的研究取得了大量的成果。本论文以模态分析理论和结构动力学为基础,分析了部分现代信号处理方法在模态参数识别和损伤识别中的应用,提出了结合多种现代信号处理技术的结构模态参数识别方法和损伤识别方法。论文中完成的主要研究工作及成果如下：
1、经验模态分解方法(Empirical Mode Decomposition, EMD)的改进。通过研究结构模态响应的特性,对随机激励下结构不同阻尼比、不同固有频率的单模态响应进行分析,结果表明结构振动在近似线性的情况下,模态响应信号可以简单看作慢变包络的窄带调幅信号。以此为基础提出一种求解局部均值的新方法,该方法弱化了EMD方法的非线性处理能力,增强对调幅信号分量的处理能力,减少EMD方法的插值拟合次数,提高EMD的分解效率。并从极值点数量、密度这一角度分析了EMD方法的模态混叠问题。
2、基于时序分析方法和稳定图原理的结构模态参数识别方法。根据时间序列分析方法的特点,在稳定图中引入了模态能量比作为新的稳定点判据,代替原有的振型判据。通过两种方法的结合在一定程度上实现了时序模型的自适应定阶,进而避免了模态分析中的模态定阶问题,能有效的剔除时序模型分析中产生的大量虚稳定点,提高稳定图的识别效率
3、基于模态响应提取的结构模态参数识别方法。以模态分析原理为基础,提出一种综合运用时间序列模型谱分析技术、互相关检测技术、EMD筛分技术的模态响应提取方法。以一个五自由度线性振动系统的仿真模型为应用对象,应用该方法提取了模态响应,然后采用AR模型分析方法得到了结构模态参数。
4、基于模态能量特征变化量和BP神经网络的损伤识别方法。根据固有模态函数(Intrinsic mode function,IMF)定义了模态能量特征,由响应信号经EMD分解后得到IMF,利用IMF计算出模态能量特征。采用一种结合模态能量特征变化量与BP神经网络的损伤识别方法对一钢结构通信塔的有限元模型进行了单点损伤识别实验,证明了方法的有效性。
With the development of society, kinds of civil and steel structure has been widely applied in city construction, water conservancy and hydropower, power transmission, new energy and other areas, and showed a large, complex and diversified development trend. The large-scale structure has closely relationship with life, its reliability and safety should be paid more attention, so the structure health assessment and health monitoring can not be ignored. The modal parameter identification and damage recognition are seen as a core technology for structural health monitoring, which are concerned by engineering and academia. Based on modal analysis theory and structural dynamics, the thesis analyzed the feasibility of application of some the modem signal processing methods on the field of the modal parameters identification and the damage recognition, then presented damage identification method and structural modal parameter identification base on a complex modern signal processing technology. The primary work and achievements are as follows:
Method of empirical mode decomposition (EMD) is improved. In random excitation case, characteristics of single-mode response signal of different damping ratio and natural frequency indicate that the modal response signals can be simple as the slowly varying envelope of the narrowband AM signal, in the case that structural vibration is approximately linear. A new method for local mean solving is earned out based on this research. The new method weakened the ability of the EMD method to nonlinear processing, while enhancing the ability to decompose a component of the AM signal. The times of interpolation fitting of EMD is reduced by this new method, while improved the decomposition efficiency.And the mode mixing and limitation of EMD method were analyzed from density of maximum.
The method of modal parameters identification based on time series analysis and stabilization diagram is presented. According to the characteristic of time series analysis, mode energy ratio was introduced to stabilization diagram as a new criterion. Modal order determination of modal analysis can be avoided in a certain sense by both methods combined. It realized the adaptive modal order determination which considers the number of stable axis of stabilization diagram as the reference, then a lot of false points are rejected, and the computation efficiency of stabilization diagram is improved significantly.
According to modal analysis theory, a method of mode response extraction is introduced which based on time series model spectrum, cross-correlation detection and shifting process of EMD. A five degree of freedom linear simulation of vibration systems was used as application object for this mode response extraction method, and mode response was extracted successfully for this simulation model. The modal parameters of simulation model were identified by using analysis of AR model.
Damage identification based on model energy feature change and BP neural network is studied. Intrinsic mode functions (IMFs) are got from vibration response by using EMD method, and model energy feature of IMFs is computed. The damage identification method is applied to a element model of communications tower, which based on model energy feature change and BP neural network, and the method is proved effective.
1、经验模态分解方法(Empirical Mode Decomposition, EMD)的改进。通过研究结构模态响应的特性,对随机激励下结构不同阻尼比、不同固有频率的单模态响应进行分析,结果表明结构振动在近似线性的情况下,模态响应信号可以简单看作慢变包络的窄带调幅信号。以此为基础提出一种求解局部均值的新方法,该方法弱化了EMD方法的非线性处理能力,增强对调幅信号分量的处理能力,减少EMD方法的插值拟合次数,提高EMD的分解效率。并从极值点数量、密度这一角度分析了EMD方法的模态混叠问题。
2、基于时序分析方法和稳定图原理的结构模态参数识别方法。根据时间序列分析方法的特点,在稳定图中引入了模态能量比作为新的稳定点判据,代替原有的振型判据。通过两种方法的结合在一定程度上实现了时序模型的自适应定阶,进而避免了模态分析中的模态定阶问题,能有效的剔除时序模型分析中产生的大量虚稳定点,提高稳定图的识别效率
3、基于模态响应提取的结构模态参数识别方法。以模态分析原理为基础,提出一种综合运用时间序列模型谱分析技术、互相关检测技术、EMD筛分技术的模态响应提取方法。以一个五自由度线性振动系统的仿真模型为应用对象,应用该方法提取了模态响应,然后采用AR模型分析方法得到了结构模态参数。
4、基于模态能量特征变化量和BP神经网络的损伤识别方法。根据固有模态函数(Intrinsic mode function,IMF)定义了模态能量特征,由响应信号经EMD分解后得到IMF,利用IMF计算出模态能量特征。采用一种结合模态能量特征变化量与BP神经网络的损伤识别方法对一钢结构通信塔的有限元模型进行了单点损伤识别实验,证明了方法的有效性。
With the development of society, kinds of civil and steel structure has been widely applied in city construction, water conservancy and hydropower, power transmission, new energy and other areas, and showed a large, complex and diversified development trend. The large-scale structure has closely relationship with life, its reliability and safety should be paid more attention, so the structure health assessment and health monitoring can not be ignored. The modal parameter identification and damage recognition are seen as a core technology for structural health monitoring, which are concerned by engineering and academia. Based on modal analysis theory and structural dynamics, the thesis analyzed the feasibility of application of some the modem signal processing methods on the field of the modal parameters identification and the damage recognition, then presented damage identification method and structural modal parameter identification base on a complex modern signal processing technology. The primary work and achievements are as follows:
Method of empirical mode decomposition (EMD) is improved. In random excitation case, characteristics of single-mode response signal of different damping ratio and natural frequency indicate that the modal response signals can be simple as the slowly varying envelope of the narrowband AM signal, in the case that structural vibration is approximately linear. A new method for local mean solving is earned out based on this research. The new method weakened the ability of the EMD method to nonlinear processing, while enhancing the ability to decompose a component of the AM signal. The times of interpolation fitting of EMD is reduced by this new method, while improved the decomposition efficiency.And the mode mixing and limitation of EMD method were analyzed from density of maximum.
The method of modal parameters identification based on time series analysis and stabilization diagram is presented. According to the characteristic of time series analysis, mode energy ratio was introduced to stabilization diagram as a new criterion. Modal order determination of modal analysis can be avoided in a certain sense by both methods combined. It realized the adaptive modal order determination which considers the number of stable axis of stabilization diagram as the reference, then a lot of false points are rejected, and the computation efficiency of stabilization diagram is improved significantly.
According to modal analysis theory, a method of mode response extraction is introduced which based on time series model spectrum, cross-correlation detection and shifting process of EMD. A five degree of freedom linear simulation of vibration systems was used as application object for this mode response extraction method, and mode response was extracted successfully for this simulation model. The modal parameters of simulation model were identified by using analysis of AR model.
Damage identification based on model energy feature change and BP neural network is studied. Intrinsic mode functions (IMFs) are got from vibration response by using EMD method, and model energy feature of IMFs is computed. The damage identification method is applied to a element model of communications tower, which based on model energy feature change and BP neural network, and the method is proved effective.
引文
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