结构损伤特征提取及诊断方法研究
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摘要
土木工程设施在服役期内,由于受到荷载和其它各种突发因素的影响,使结构发生损伤,造成重大的经济损失和人员伤亡。因此,对结构健康状态做出及时有效的诊断、准确评估和预示,具有重要的科学理论意义和工程应用价值。本文研究了基于信号分析和神经网络的结构损伤特征提取及诊断方法。
论述了实施工程结构健康监测与损伤诊断的必要性与迫切性,介绍了结构健康监测与损伤诊断的基本概念、系统组成、损伤诊断的方法及研究现状。
从结构损伤特征提取的角度出发,研究了基于小波包分析的结构损伤特征提取方法。采用正交小波包对ASCE结构的响应信号进行分解,并计算每个频带上的相对能量来表征结构的状态。研究表明:对于相同的损伤,在不同节点处测量信号的小波包能量分布是不同的;不同类型的损伤小波包能量分布有显著的差异;小波包分析为信号处理和特征提取提供一种更加精细的分析方法。
为了实现结构状态的自动诊断,研究了一种基于小波包特征提取的BP神经网络结构损伤诊断方法。研究表明:和BP神经网络的其它学习算法相比,弹性学习算法收敛较快、耗时较少,识别正确率较高,适合于结构状态的模式分类;但是,对于同一损伤源,采用不同节点的信号分析时,网络的识别正确率和各项性能指标不同。
针对单一节点信号进行损伤诊断的不确定性和片面性,研究了基于多节点传感器特征融合的结构损伤诊断方法。多传感器特征融合,能够使不同传感器的信息相互补充,减小了损伤检测信息的不确定性,使诊断信息具有更高的精度和可靠性,提高了损伤诊断准确率。
土木结构的损伤在理论上是一个渐进过程,为了能够有效地监测这个损伤过程,研究了基于Hilbert-Huang变换的结构渐进损伤特征提取方法。模拟产生了单自由度模型和多自由度模型结构刚度渐进下降损伤的加速度振动信号,对该加速度振动信号进行Hilbert-Huang变换,并提取瞬时频率。损伤前后,瞬时频率会发生明显的变化,可以作为结构渐进损伤的特征。
Damage will come into existence in the civil engineering structures during their lifetime with the effect of loads and other unknown factors. As a result, it sometimes will bring about significant economic losses and personnel casualties. So it is necessary to make efficient diagnosis, evaluation and prognosis for the health condition of serving structures. In this paper the damage feature extraction and diagnosis techniques for engineering structure based on signal analysis and nerve network are studied, main research work and conclusions are demonstrated as following:
The necessity of civil engineering structural health monitoring (SHM) & damage detection is discussed firstly. Then the concept of SHM & damage detection and the architecture of SHM system are introduced. Moreover, damage detection techniques and their development are reviewed.
In order to extract damage feature, the wavelet packet analysis methods of damage feature extraction are developed. The response signals of the ASCE benchmark structure are processed by using orthogonal wavelet packet transform, then wavelet package energy (WPE) on decomposition frequency bands are calculated to represent the structure condition. Researches show that for a signal the WPE distribution can describe the energy variation of its components, for a special damage the distribution of WPE is different at the different detection nodes and for different kinds of damage their WPE distributions are different each other. The wavelet package transform can give us a finer analysis approach for signal processing and feature extraction.
To implement structure automatic diagnosis, a method of structure damage diagnosis is addressed based on wavelet packet analysis and BP neural network. Compared with other learning algorithm, the elasticity algorithm converges more quickly, and needs less learning time. It is suitable for pattern classification of structure condition. However, using signals from different detection nodes for the same damage,- the recognition correct rate and performances index of BP network are different.
To aim at fixing the uncertainty caused by only using signals from single detection node in structure damage diagnosis, another diagnosis method is presented by means of multi-sensor feature fusion theory. Through fusing feature extracted from several different detection nodes, it can make different information complementary, and reduce the uncertainty of damage detection information. So precision and reliability of the diagnosis information is much more modified and the diagnosis accuracy was improved.
The structure damage is a progressive process theoretically. In order to monitor the process efficiently, a feature extraction method of structure progressive damage is studied based on Hilbert-Huang transform (HHT). Acceleration vibration signals of a single-degree of freedom structure model and a multi-degree of freedom structure model are simulated by reducing the stiffness gradually. The signals are processed by using HHT to extract the instantaneous frequency. The extracted instantaneous frequency is obviously changed before and after damage coming into existence, which can be taken as a feature index to monitor the structure progressive damage.
论述了实施工程结构健康监测与损伤诊断的必要性与迫切性,介绍了结构健康监测与损伤诊断的基本概念、系统组成、损伤诊断的方法及研究现状。
从结构损伤特征提取的角度出发,研究了基于小波包分析的结构损伤特征提取方法。采用正交小波包对ASCE结构的响应信号进行分解,并计算每个频带上的相对能量来表征结构的状态。研究表明:对于相同的损伤,在不同节点处测量信号的小波包能量分布是不同的;不同类型的损伤小波包能量分布有显著的差异;小波包分析为信号处理和特征提取提供一种更加精细的分析方法。
为了实现结构状态的自动诊断,研究了一种基于小波包特征提取的BP神经网络结构损伤诊断方法。研究表明:和BP神经网络的其它学习算法相比,弹性学习算法收敛较快、耗时较少,识别正确率较高,适合于结构状态的模式分类;但是,对于同一损伤源,采用不同节点的信号分析时,网络的识别正确率和各项性能指标不同。
针对单一节点信号进行损伤诊断的不确定性和片面性,研究了基于多节点传感器特征融合的结构损伤诊断方法。多传感器特征融合,能够使不同传感器的信息相互补充,减小了损伤检测信息的不确定性,使诊断信息具有更高的精度和可靠性,提高了损伤诊断准确率。
土木结构的损伤在理论上是一个渐进过程,为了能够有效地监测这个损伤过程,研究了基于Hilbert-Huang变换的结构渐进损伤特征提取方法。模拟产生了单自由度模型和多自由度模型结构刚度渐进下降损伤的加速度振动信号,对该加速度振动信号进行Hilbert-Huang变换,并提取瞬时频率。损伤前后,瞬时频率会发生明显的变化,可以作为结构渐进损伤的特征。
Damage will come into existence in the civil engineering structures during their lifetime with the effect of loads and other unknown factors. As a result, it sometimes will bring about significant economic losses and personnel casualties. So it is necessary to make efficient diagnosis, evaluation and prognosis for the health condition of serving structures. In this paper the damage feature extraction and diagnosis techniques for engineering structure based on signal analysis and nerve network are studied, main research work and conclusions are demonstrated as following:
The necessity of civil engineering structural health monitoring (SHM) & damage detection is discussed firstly. Then the concept of SHM & damage detection and the architecture of SHM system are introduced. Moreover, damage detection techniques and their development are reviewed.
In order to extract damage feature, the wavelet packet analysis methods of damage feature extraction are developed. The response signals of the ASCE benchmark structure are processed by using orthogonal wavelet packet transform, then wavelet package energy (WPE) on decomposition frequency bands are calculated to represent the structure condition. Researches show that for a signal the WPE distribution can describe the energy variation of its components, for a special damage the distribution of WPE is different at the different detection nodes and for different kinds of damage their WPE distributions are different each other. The wavelet package transform can give us a finer analysis approach for signal processing and feature extraction.
To implement structure automatic diagnosis, a method of structure damage diagnosis is addressed based on wavelet packet analysis and BP neural network. Compared with other learning algorithm, the elasticity algorithm converges more quickly, and needs less learning time. It is suitable for pattern classification of structure condition. However, using signals from different detection nodes for the same damage,- the recognition correct rate and performances index of BP network are different.
To aim at fixing the uncertainty caused by only using signals from single detection node in structure damage diagnosis, another diagnosis method is presented by means of multi-sensor feature fusion theory. Through fusing feature extracted from several different detection nodes, it can make different information complementary, and reduce the uncertainty of damage detection information. So precision and reliability of the diagnosis information is much more modified and the diagnosis accuracy was improved.
The structure damage is a progressive process theoretically. In order to monitor the process efficiently, a feature extraction method of structure progressive damage is studied based on Hilbert-Huang transform (HHT). Acceleration vibration signals of a single-degree of freedom structure model and a multi-degree of freedom structure model are simulated by reducing the stiffness gradually. The signals are processed by using HHT to extract the instantaneous frequency. The extracted instantaneous frequency is obviously changed before and after damage coming into existence, which can be taken as a feature index to monitor the structure progressive damage.
引文
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