Structural and Crack Parameter Identification on Structures Using Observer Kalman Filter Identification/Eigen System Realization Algorithm

P. Nandakumar, J. Jacob
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引用次数: 2

Abstract

Structural and crack parameters in a continuous mass model are identified using Observer Kalman filter Identification (OKID) and Eigen Realization Algorithm (ERA). Markov parameters are extracted from the input and out responses from which the state space model of the structural system is determined using Hankel matrix and singular value decomposition by Eigen Realization algorithm. The structural parameters are identified from the state space model. This method is applied to a lumped mass system and a cantilever which are excited with a harmonic excitation at its free end and the acceleration responses at all nodes are measured. The stiffness and damping parameters are identified from the extracted matrices using Newton-Raphson method on the structure. Later, cracks are introduced in the cantilever and all structural parameters are assumed as known priori, the unknown crack parameters such as normalized crack depth and its location are identified using OKID/ERA. The parameters extracted by using this algorithm are compared with other structural identification methods available in the literature. The main advantage of this algorithm is good accuracy of identified structural parameters.
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基于观测器卡尔曼滤波识别/特征系统实现算法的结构及裂纹参数识别
采用观测器卡尔曼滤波识别(OKID)和特征实现算法(ERA)对连续质量模型中的结构参数和裂纹参数进行了识别。从输入和输出响应中提取马尔可夫参数,利用Hankel矩阵和特征实现算法的奇异值分解确定结构系统的状态空间模型。从状态空间模型中识别结构参数。将该方法应用于一个集总质量系统及其自由端受简谐激励的悬臂梁,测量了其各节点的加速度响应。利用牛顿-拉夫逊法对结构进行了刚度和阻尼参数辨识。然后,在悬臂梁中引入裂缝,并假定所有结构参数先验已知,使用OKID/ERA识别归一化裂纹深度及其位置等未知裂纹参数。将该算法提取的参数与文献中其他结构识别方法进行了比较。该算法的主要优点是结构参数识别精度高。
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