【摘要】 为了提高管道损伤识别的准确率,提出了基于数据融合和单纯形遗传算法的两段式的管道损伤位置和程度的识别方法。首先将管道的柔度差曲率矩阵与广义残余力向量差两种信息源通过D-S证据理论融合算法初步判定管道损伤位置,然后通过单纯形遗传算法精确识别管道损伤位置与程度。考虑到基本遗传算法局部搜索不强、易发生早熟的缺点,提出了与局部搜索算法(单纯形搜索算法)相结合的改进策略。数值计算结果表明,考虑2%随机噪声影响情况,采用数据融合进行初步定位的方法大大缩小了可疑损伤区域范围,通过单纯形遗传算法能够进一步精确识别管道损伤位置及程度。本文提出的方法提高了管道损伤位置与程度识别的效率与准确率。更多还原

【Abstract】 In order to increase the precision of structural identification,a two-stage method based on data fusion and simplex genetic algorithm is proposed.Firstly,flexibility curvature matrix and generalized residual force vector difference are considered as two kinds of information sources,and the D-S evidence theory is utilized to integrate the two information sources and preliminarily detect structural damage locations.Then,simplex genetic algorithm is used to identify structural damage extents.Considering the premature convergence of basic GA,a method combined genetic algorithm with simplex algorithm is utilized as the improved strategy.It is shown that the two-stage method can precisely identify structural damage locations and extent under the condition of 2%random noise.The proposed method improves the efficiency and accuracy of pipeline damage identification.更多还原