基于稀疏双模传感器阵列的超声导波在役风力机叶片结构健康监测技术研究

The blades are the most essential and vital components, and meanwhile are one of the key components where defects are the most likely and frequent to occur in wind turbines.In this project, ultrasonic guided wave technique based on sparse dual-mode sensor array is proposed for structural health monitoring(SHM) of in-service wind turbine blades.Under the guidance of wave theory, propagation characteristics, including skew effect and wave structures, of ultrasonic guided wave modes will be investigated. Hybrid model of ultrasonic guided wave propagation in wind turbine blades will be developed based on the combination of experimental research and three-dimensional finite element simulation.Scattering of different ultrasonic guided wave modes at typical defects will be analysed using this model.The modes with high sensitivity to typical defects and large scattering angle, and their corresponding excitation frequencies will be selected. Dual-mode sensor unit for the excitation and reception of different ultrasonic guided wave modes will be developed and an experimental system will be established. Sparse dual-mode sensor array distribution based on defect scattering characteristics of ultrasonic guided waves will be optimized. In experiments, typical defects in wind turbine blades will be inspected by using different modes excited by chirp signals separately. Scattering signals containing defect information will be extracted from received inspection signals. Imaging parameters will be optimized and defects will be imaged based on ultrasonic guided wave scattering characteristics. Furthermore, data fusion technique using the signals of several modes at different frequencies will be optimized and developed to reduce and remove image artifacts for enhancing imaging quality and defect identification capabilities. As a result, structural health monitoring of in-service wind turbine blades will be finally achieved.

叶片是风力机中最基础和最关键的部件，也是缺陷易发、频发的重点部件之一。本项目提出一种基于稀疏双模传感器阵列的超声导波技术，用于在役风力机叶片结构健康监测。在波动理论指导下，研究超声导波模态的偏斜效应和波结构等传播特性。基于实验研究和三维有限元仿真，建立风力机叶片中超声导波传播的混合模型。利用此模型分析超声导波不同模态典型缺陷散射特性；选取对典型缺陷敏感、散射角较大的模态以及相应的激励频率。研制用于激励接收超声导波不同模态的双模传感器单元，建立实验系统。基于超声导波缺陷散射特性优化稀疏双模传感器阵列的分布。实验中采用chirp信号分别激励不同模态，检测风力机叶片中的典型缺陷。从获取的检测信号中提取包含缺陷信息的散射信号；优化成像参数，进行基于超声导波散射特性的缺陷成像；进而优化选取和发展多频多模态信号数据融合技术，减少和消除赝像，提高成像质量和缺陷可识别能力，实现在役风力机叶片结构健康监测。

近年来，风能作为一种清洁的可再生能源，在应对全球气候变暖、能源危机方面越来越受到世界各国的重视。大型风力发电机是将风能转化为电能的重要设备，而叶片是风力机捕获风能的最直接部件，也是风力机最基础和最关键的部件之一，造价占整机的20%左右。我国主流的风力机叶片均由刚性好、强度大、质量轻的纤维增强复合材料铺层制成，为典型的复合材料薄壳结构。针对风力机叶片的应用现状和工程检测需求，本课题开展了基于稀疏双模传感器阵列的超声导波在役风力机叶片结构健康监测技术研究。.首先，建立了单层正交各向异性板中Lamb波传播的理论解析模型。推导了Lamb波沿正交各向异性材料任意方向包括非主轴方向传播的波动方程。利用勒让德正交多项式展开法，对波动方程进行了数值求解并分析了Lamb波沿不同方向传播时，频散特性和波结构分布的影响。其次，构建了任意各向异性多层板中耦合Lamb波传播的理论解析模型，分析了纤维铺层角度的变化对耦合Lamb波频散特性、位移分布和应力分布的影响。采用Matlab和Abaqus混合编程的方法，分析了复合材料板超声导波的传播特性，分析Lamb波与缺陷的相互作用机理。研制了用于激励接收单模态A0、S0的双模传感器，通过有限元仿真优化传感器结构和尺寸，实验验证了该双模传感器激励接收单一A0、S0模态的可行性和有效性。建立了一套完整的实验系统，基于超声导波缺陷散射特性优化了双模传感器稀疏阵列分布。采用Chirp信号作为双模传感器的激励信号，提取各模态不同频率下包含缺陷信息的散射信号。结合椭圆成像技术和数据融合方法，实现了多模态、多频率缺陷成像，具有较高的成像质量和缺陷可识别能力，实现了在役复合材料板结构健康监测。.本课题的研究不仅具有较高的学术价值，而且具有重要的工程实用价值，可为我国风力机叶片在役健康状况监测提供科学对策和方法，促进和保障风电设备制造业和风能开发的可持续发展。