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Genetic fuzzy system for damage detection in beams and helicopter rotor blades

机译:用于梁和直升机旋翼桨叶损伤检测的遗传模糊系统

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摘要

Structural damage detection is an inverse problem of structural engineering having three main parts: finding the existence, location and extent of damage. In this study, a genetic fuzzy system is used to find the location and extent of damage. A finite element model of a cantilever beam is used to calculate the change in beam frequencies because of structural damage. Using these changes in frequencies, a fuzzy system is generated and the rule-base and membership functions optimized by genetic algorithm. The output faults of the fuzzy system are four levels of damage (undamaged, slight, moderate, and severe) at five locations along the beam (root, inboard, center, outboard and tip). The genetic fuzzy system developed for a noise level of 0.20 in the data gives a fault isolation success rate of 99.81% when the first eight natural frequencies are used. With noise level of 0.15 and 0.10, accuracy rates of 100% are obtained even when only the first four natural frequencies are used. The fuzzy system also shows excellent robustness with missing measurements and degrades gradually in the presence of faulty sensors/measurements. The genetic fuzzy system allows easy rule generation for different structures and when the number of inputs and outputs increase, thereby avoiding the 'curse of dimensionality' that plagues fuzzy systems. Results with a non-uniform beam and a finer output set of damage at 10 locations in the beam also show excellent results. The genetic fuzzy system also gives very good results for BO-105 hingeless helicopter rotor blade for frequency as well as mode shape-based data. The genetic fuzzy logic system in this study is proposed as a method for automatic rule generation in fuzzy systems for structural damage detection.
机译:结构损伤检测是结构工程的一个反问题,它具有三个主要部分:发现损伤的存在,位置和程度。在这项研究中,使用遗传模糊系统来查找损坏的位置和程度。悬臂梁的有限元模型用于计算由于结构损坏而引起的梁频率变化。利用这些频率变化,可以生成一个模糊系统,并通过遗传算法优化规则库和隶属函数。模糊系统的输出故障是沿梁的五个位置(根,内侧,中心,外侧和顶端)的四个损伤等级(损坏,轻微,中度和严重)。当使用前八个固有频率时,针对数据中的噪声水平为0.20的遗传模糊系统开发的故障隔离成功率为99.81%。在0.15和0.10的噪声水平下,即使仅使用前四个固有频率,也可获得100%的准确率。模糊系统还显示出出色的鲁棒性,缺少测量值,并且在存在错误的传感器/测量值的情况下逐渐退化。当输入和输出的数量增加时,遗传模糊系统允许轻松生成不同结构的规则,从而避免困扰模糊系统的“维数诅咒”。光束不均匀且光束中10个位置的输出损伤集更细的结果也显示了出色的结果。遗传模糊系统还为BO-105无铰链直升机旋翼桨叶提供了很好的频率和基于模态数据的结果。提出了本研究中的遗传模糊逻辑系统,作为一种用于结构损伤检测的模糊系统中的自动规则生成方法。

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