摘要
当布拉格光栅轴向存在大的应变梯度时,其反射光谱的形状会被扭曲,甚至出现多峰,发展非均匀应变分布重构方法对于结构健康监测技术具有重要意义。但采集反射光谱时的测试噪声会显著影响应变分布重构的精度。为此,提出了采用支持向量机对含噪的反射光谱进行回归预处理,并运用适应度排序改进的遗传优化算法结合传输矩阵反射光谱构建方法识别布拉格光栅轴向非均匀应变分布的方法。该方法将反射光谱视为时间序列,利用支持向量回归的全局优化和泛化能力进行噪声抑制,从而回归出有效的反射光谱;通过传输矩阵方法将光栅轴向应变分段均匀化,利用改进的遗传算法进行并行重构。对多种应变分布形式下的应变重构进行了仿真研究,结果表明,支持向量机方法可以有效地进行反射光谱回归,提高非均匀应变分布重构的精度。
The reflection spectrum shape of the Bragg grating sensor will be distorted even with multi-peaks when there exists great strain gradient in the axis direction of the FBG, so it＇s meaningful for developing strain profile reconstruction method in structure health monitoring field. With the problems that noised reflection spectrum will decline the precision of strain reconstruction, a novel method based on the support vector regressin （SVR） preprocessing of noised reflection spectrum is proposed for the non-uniformly distributed strain profile reconstruction along the axis of Bragg grating. This method combines fitness sorting improved genetic algorithm with transform- matrix reflection spectrum construction. The reflection spectrum is viewed as a time serial and SVR is developed to constrain the noise for effective regression of reflection spectrum due to its global optimization and well generalization. Subsequently, the strain distribution is treated as a piecewise constant function, the spectral response is calculated using the transform-matrix approach, and then improved genetic algorithm is employed for reconstruction simultaneously. The simulations for strain profile reconstruction are conducted in various kinds of strain distributions. Results show that the SVR reflection spectrum regression is very effective and the proposed algorithm can improve precision of non-uniform strain distribution reconstruction.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2008年第8期1513-1517,共5页
Acta Optica Sinica
基金
国家自然科学基金(10772077)
航空科学基金(2007ZD52047)资助课题
关键词
光纤光学
健康监测
非均匀应变分布重构
改进遗传算法
支持向量回归
传输矩阵法
fiber optics
health monitoring
non-uniform strain profile reconstruction
improved genetic algorithm
support vector regression
transform-matrix method
