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Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades

机译：复合材料直升机旋翼在线结构健康监测的遗传模糊系统

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摘要

A structural health monitoring (SHM) methodology is developed for composite rotor blades. An aeroelastic analysis of composite rotor blades based on the finite element method in space and time and with implanted matrix cracking and debonding/delamination damage is used to obtain measurable system parameters such as blade response, loads and strains. A rotor blade with a two-cell airfoil section and [0/ ± 45/90]_s family of laminates is used for numerical simulations. The model based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems (GFS) are developed for global online damage detection using displacement and force-based measurement deviations between damaged and undamaged conditions and for local online damage detection using strains. It is observed that the success rate of the GFS depends on number of measurements, type of measurements and training and testing noise level. The GFS work quite well with noisy data and is recommended for online SHM of composite helicopter rotor blades.
机译：针对复合材料转子叶片开发了一种结构健康监测（SHM）方法。基于空间和时间的有限元方法，对复合材料转子叶片进行空气弹性分析，并注入基质裂纹和脱胶/分层损伤，以获取可测量的系统参数，例如叶片响应，载荷和应变。具有两格翼型截面和[0 /±45/90] _s叠片家族的转子叶片用于数值模拟。基于模型的测量结果被噪声污染，以模拟真实数据。遗传模糊系统（GFS）用于通过损坏和未损坏条件之间的位移和基于力的测量偏差来进行全局在线损伤检测，以及使用应变进行局部在线损伤检测。可以看出，GFS的成功率取决于测量次数，测量类型以及训练和测试噪声水平。 GFS可以在嘈杂的数据下很好地工作，建议将其用于复合直升机旋翼桨叶的在线SHM。

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