风力机叶片疲劳裂纹特征提取方法研究
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摘要
风力机叶片的故障已经成为现有风场中的隐患，本文旨在研究风力机叶片承受随机载荷和交变载荷共同作用时的裂纹萌生、生长和扩展信号特征，分析不同的初始裂纹的辨识方法，了解裂纹动态生存状态与叶片疲劳损伤程度之间的因果关系，识别风力机叶片的损伤程度和类型，由此研制具有自主知识产权的大型风力机叶片疲劳损伤辨识系统，从而解决难以实时监测大型风力机叶片的问题，在故障尚轻微时尽早地准确识别其位置和程度，提前对叶片故障预警，保障风力机高效安全地运行，大大降低风力机后期维修成本。
本文建立了初始裂纹及裂纹生长扩展的诊断模型，通过试验设计，搭建试验平台采集不同裂纹类型、不同裂纹阶段对应的故障信号，为有效地监测叶片状态优化声发射传感器安装位置，同时确定叶片裂纹故障信号的采样频率、采样长度、滤波频率等信号采集和检测的技术参数。分析风力机叶片承受循环载荷作用下的裂纹变化特征，明确瞬时声发射导波传递对裂纹生存状态关联机制，研究局部集中应力导致裂纹生长的叶片疲劳损伤特征，确定裂纹的形变特点、增长率以及叶片疲劳损坏程度之间的因果关系，由此及时准确地评估风力机叶片疲劳状态。
本文结合试验模拟的方法进一步分析风力机叶片裂纹萌生和扩展机理，了解动态应力对叶片疲劳破坏的影响，根据裂纹类型和状态判定风力机叶片的疲劳损伤程度，构建一个以声发射信号为监测参量、基于自适应小波分析提取微细裂纹故障特征的机制。首先结合Shannon熵方法实现小波基函数的自适应选取，实现消除背景噪声、分离有用信息，提取裂纹故障信号中的微细特征，在此算法基础上将采集到的风力机叶片裂纹声发射信号进行特征提取，再使用到小波尺度谱及重分配尺度谱中，通过对比得到不同类型裂纹的特征信号，完成对初始裂纹的萌生扩展状态的特征提取。风力机的玻璃钢叶片材料不存在明确的疲劳极限，当叶片出现裂纹导致叶片固有频率下降时，不同部位裂纹对固有频率的影响不同，裂纹深度扩展后振型将发生变化，而且产生裂纹的原因多样，由此引发的裂纹生存状态不同，因此提取叶片疲劳裂纹特征的分析机制是非常复杂的。从多分辨率角度入手来提取裂纹特征，分析采集数据中的敏感参数，挖掘叶片微细裂纹故障的特征参数，建立初始裂纹的诊断形式，展现叶片疲劳裂纹在不同频率段的特征，成为解决此问题的关键。本文使用多分辨率的奇异值分解并重构信号从而得到噪声干扰更少的信号；再进行重分配尺度谱的多分辨率计算使得在每一分辨率上的信号更加准确且更具有实际操作性，同时结合能量表达方法，得到可以指导实践的特征向量。
针对风力机叶片疲劳短裂纹从萌生，生长及扩展，到多裂纹以及长裂纹的出现直到叶片断裂的这一裂纹群体性行为。使用实时的声发射信号采集裂纹的特征就会出现时间跨度长，外界因素不好控制且损伤状态不好界定的问题。故采用分形理论来分析经过疲劳加速试验得到的叶片缩尺模型的不同裂纹阶段的裂纹几何特征。极大的排除了外界因素的限制和干扰，从而展现了从裂纹萌生到叶片断裂的全过程，并用分形维数这一参量表达了损伤变化程度。
Now, wind turbine blade failure has become the accident hidden trouble in wind field.In this paper, we explore the fatigue crack evolution of wind turbine blade under thecombination of random load and alternating load.The method of identification differentinitial cracks has been analyzed. We want to understand dynamic condition and causalrelationship of damage degree, and to identify the damage degree and type of fatigue crack.That is independent intellectual property rights of large-scale wind turbine blade fatiguedamage identification system, so as to solve the problem of real-time monitoring of largewind turbine blade. When fault is minor, the location and extent of damages is identified asearly as possible, and fault warning is demonstrated in advance, to ensure efficient windturbine running safely and reduce maintenance cost greatly.
This paper presented the diagnosis model which contains experiment of initial crackand the crack growth. Through the test-bed, different types of crack and fault signal ofcrack phase are collected. The installation location of acoustic emission sensor can beoptimized to effectively monitor state of blade, at the same time the sampling, filtering andfrequency are determined. Crack characteristics of wind turbine blades under cycle loadingis analyzed, The associated mechanism of transient AE guided wave transmission and thefatigue feature of the local stress concentration can be clear, characteristics of the crackdeformation, and the causal relationship between growth rate and fatigue damage havebeen determined to assess of wind turbine blade fatigue state timely.
This paper demonstrated the crack initiation and propagation mechanism through theexperimental simulation, and the influence of the dynamic stress. According to the cracktype and status to determine the degree of fatigue, it is the mechanism of adaptive waveletanalysis with AE signal that is built. Firstly, adaptive selection of wavelet basis functioncombined with Shannon entropy method, which eliminates background noise from usefulinformation, and then the wavelet scalogram and reassignment scalogram are applied.Finally the characteristic of different types of cracks is clear by comparing.The fatigue limit of FRP blade is not clear. When the blade cracks and the natural frequency decreases,the influence of the different parts of the crack on natural frequencies is different, and themode will change. Therefore the mechanism of blade fatigue characteristics is verycomplex. To extract crack characteristics with multiresolution is the key to solve thisproblem. This paper applied multi-resolution SVD and reconstruction to get less noisesignal, and multi-resolution reassignment scalogram to get the feature vector, combiningenergy expression method.
For the mass behavior from short crack initiation, growth and expansion, long cracks,to breakage, using real-time AE signal will appear a long time span, and control damagestate badly. The fractal theory is adopted to analyze the geometric characteristics of crack,through the fatigue acceleration experiment. That method can eliminate external factors. Itshows that the process of crack initiation to breakage, and the fractal dimension is the newfeature vector of state.
本文建立了初始裂纹及裂纹生长扩展的诊断模型，通过试验设计，搭建试验平台采集不同裂纹类型、不同裂纹阶段对应的故障信号，为有效地监测叶片状态优化声发射传感器安装位置，同时确定叶片裂纹故障信号的采样频率、采样长度、滤波频率等信号采集和检测的技术参数。分析风力机叶片承受循环载荷作用下的裂纹变化特征，明确瞬时声发射导波传递对裂纹生存状态关联机制，研究局部集中应力导致裂纹生长的叶片疲劳损伤特征，确定裂纹的形变特点、增长率以及叶片疲劳损坏程度之间的因果关系，由此及时准确地评估风力机叶片疲劳状态。
本文结合试验模拟的方法进一步分析风力机叶片裂纹萌生和扩展机理，了解动态应力对叶片疲劳破坏的影响，根据裂纹类型和状态判定风力机叶片的疲劳损伤程度，构建一个以声发射信号为监测参量、基于自适应小波分析提取微细裂纹故障特征的机制。首先结合Shannon熵方法实现小波基函数的自适应选取，实现消除背景噪声、分离有用信息，提取裂纹故障信号中的微细特征，在此算法基础上将采集到的风力机叶片裂纹声发射信号进行特征提取，再使用到小波尺度谱及重分配尺度谱中，通过对比得到不同类型裂纹的特征信号，完成对初始裂纹的萌生扩展状态的特征提取。风力机的玻璃钢叶片材料不存在明确的疲劳极限，当叶片出现裂纹导致叶片固有频率下降时，不同部位裂纹对固有频率的影响不同，裂纹深度扩展后振型将发生变化，而且产生裂纹的原因多样，由此引发的裂纹生存状态不同，因此提取叶片疲劳裂纹特征的分析机制是非常复杂的。从多分辨率角度入手来提取裂纹特征，分析采集数据中的敏感参数，挖掘叶片微细裂纹故障的特征参数，建立初始裂纹的诊断形式，展现叶片疲劳裂纹在不同频率段的特征，成为解决此问题的关键。本文使用多分辨率的奇异值分解并重构信号从而得到噪声干扰更少的信号；再进行重分配尺度谱的多分辨率计算使得在每一分辨率上的信号更加准确且更具有实际操作性，同时结合能量表达方法，得到可以指导实践的特征向量。
针对风力机叶片疲劳短裂纹从萌生，生长及扩展，到多裂纹以及长裂纹的出现直到叶片断裂的这一裂纹群体性行为。使用实时的声发射信号采集裂纹的特征就会出现时间跨度长，外界因素不好控制且损伤状态不好界定的问题。故采用分形理论来分析经过疲劳加速试验得到的叶片缩尺模型的不同裂纹阶段的裂纹几何特征。极大的排除了外界因素的限制和干扰，从而展现了从裂纹萌生到叶片断裂的全过程，并用分形维数这一参量表达了损伤变化程度。
Now, wind turbine blade failure has become the accident hidden trouble in wind field.In this paper, we explore the fatigue crack evolution of wind turbine blade under thecombination of random load and alternating load.The method of identification differentinitial cracks has been analyzed. We want to understand dynamic condition and causalrelationship of damage degree, and to identify the damage degree and type of fatigue crack.That is independent intellectual property rights of large-scale wind turbine blade fatiguedamage identification system, so as to solve the problem of real-time monitoring of largewind turbine blade. When fault is minor, the location and extent of damages is identified asearly as possible, and fault warning is demonstrated in advance, to ensure efficient windturbine running safely and reduce maintenance cost greatly.
This paper presented the diagnosis model which contains experiment of initial crackand the crack growth. Through the test-bed, different types of crack and fault signal ofcrack phase are collected. The installation location of acoustic emission sensor can beoptimized to effectively monitor state of blade, at the same time the sampling, filtering andfrequency are determined. Crack characteristics of wind turbine blades under cycle loadingis analyzed, The associated mechanism of transient AE guided wave transmission and thefatigue feature of the local stress concentration can be clear, characteristics of the crackdeformation, and the causal relationship between growth rate and fatigue damage havebeen determined to assess of wind turbine blade fatigue state timely.
This paper demonstrated the crack initiation and propagation mechanism through theexperimental simulation, and the influence of the dynamic stress. According to the cracktype and status to determine the degree of fatigue, it is the mechanism of adaptive waveletanalysis with AE signal that is built. Firstly, adaptive selection of wavelet basis functioncombined with Shannon entropy method, which eliminates background noise from usefulinformation, and then the wavelet scalogram and reassignment scalogram are applied.Finally the characteristic of different types of cracks is clear by comparing.The fatigue limit of FRP blade is not clear. When the blade cracks and the natural frequency decreases,the influence of the different parts of the crack on natural frequencies is different, and themode will change. Therefore the mechanism of blade fatigue characteristics is verycomplex. To extract crack characteristics with multiresolution is the key to solve thisproblem. This paper applied multi-resolution SVD and reconstruction to get less noisesignal, and multi-resolution reassignment scalogram to get the feature vector, combiningenergy expression method.
For the mass behavior from short crack initiation, growth and expansion, long cracks,to breakage, using real-time AE signal will appear a long time span, and control damagestate badly. The fractal theory is adopted to analyze the geometric characteristics of crack,through the fatigue acceleration experiment. That method can eliminate external factors. Itshows that the process of crack initiation to breakage, and the fractal dimension is the newfeature vector of state.
引文
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