EI、Scopus 收录
中文核心期刊
徐训 欧进萍. 基于独立分量分析的多源动态载荷识别方法[J]. 力学学报, 2012, 44(1): 158-166. DOI: 10.6052/0459-1879-2012-1-lxxb2011-107
引用本文: 徐训 欧进萍. 基于独立分量分析的多源动态载荷识别方法[J]. 力学学报, 2012, 44(1): 158-166. DOI: 10.6052/0459-1879-2012-1-lxxb2011-107
Xun Xu Jinping Ou. An identification method of multi-source dynamic loads based on independent component analysis[J]. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(1): 158-166. DOI: 10.6052/0459-1879-2012-1-lxxb2011-107
Citation: Xun Xu Jinping Ou. An identification method of multi-source dynamic loads based on independent component analysis[J]. Chinese Journal of Theoretical and Applied Mechanics, 2012, 44(1): 158-166. DOI: 10.6052/0459-1879-2012-1-lxxb2011-107

基于独立分量分析的多源动态载荷识别方法

An identification method of multi-source dynamic loads based on independent component analysis

  • 摘要: 提出了基于独立分量分析的多源动态载荷识别方法, 解决了在结构系统未知的情况下载荷波形的识别问题. 该方法基于结构在多源动态载荷作用下, 其响应是载荷与对应的结构脉冲响应卷积的原理, 并假设载荷源相互统计独立. 与既有的载荷识别方法相比,该识别方法特点表现在: 结构质量, 刚度及阻尼等信息可以完全未知, 但以实际载荷间的独立性为优化目标; 用互信息来度量识别载荷间的独立性, 通过梯度下降算法取消识别载荷间的各阶相关性, 使识别载荷间基本满足相互独立; 从波形的角度来进行载荷识别.通过数值仿真表明: 该方法对测点, 噪声, 不同载荷形式及不同结构有较好的鲁棒性; 识别载荷与实际载荷在归一化条件下, 识别载荷与实际载荷相关性系数约为1.

     

    Abstract: This study proposes an identification method ofmulti-source dynamic loads based on independent component analysis, in orderto detect the wave pattern of the load subjected to a structural systemwhose configurational information is totally unknown. The method is based onthe principle that the response of structure is a convolution of load andcorresponding structure impulse response; also, it assumes that individualload sources are statistically independent to each other. When compared withthe existing load identification methods, the features of the methoddeveloped in this study embody three merits: (1) The structuralconfiguration, such as distribution of structural mass, structural stiffnessand damping ratio, could be unknown, and the independence among the actualloads is taken as the goal of optimization; (2) The independence ofindividual identified loads is measured using their mutual information, andthe correlation at different orders is eliminated by the use of gradientdegressive algorithm, so that the independence among detected loads isguaranteed; (3) Loads could be identified from the standpoint of wavepatterns. Numerical simulation indicates that the algorithm is robust onmeasuring point, noise, structure and input load. By normalization, thevalue of the correlation coefficient between identified and actual loads isapproximately 1. Therefore, this method successfully integrates the existingknowledge on load into the load detection algorithm, and could make aconfident estimation of the actual value, which contributes in the practicalsignificance of the developed method.

     

/

返回文章
返回