基于加速度内积向量和灰云模型的结构损伤识别

为了解决测量噪声等引起的损伤识别不确定性问题,提出了基于加速度内积向量和灰云模型相结合的损伤识别方法。描述了云模型和云发生器的基本理论和公式,计算了结构在随机激励荷载下的加速度响应,并利用互相关函数和二阶差分法构造出加速度内积向量损伤指标,最后,基于灰云模型建立了内积向量和损伤区间的前件云和后件云。考虑随机测量噪声等引起的不确定性,利用多种模式下的加权和均化计算,建立了基于灰云模型的损伤识别方法。数值计算结果表明,所提出的基于灰云模型损伤识别方法,可以较好地进行含噪数据的损伤识别,其识别效果优于单纯的加速度内积向量损伤指标。

Structural damage identification based on acceleration inner product vector and gray cloud

In order to solve the uncertain damage problem caused by measurement noise, a damage identification method based on acceleration inner product vector and gray cloud model is presented. First, basic theory and formulas of cloud model and cloud generators are introduced. Then, the acceleration response under random excitation load is calculated, inner product vector is deduced from cross correlation functions and second-order difference method, and an inner product vector damage index is proposed. Finally, the grey cloud rules of inner product vector and damage intervals are built. Considering the uncertainties casused by stochastic measurement noise, a damage identification method based on gray cloud model is presented by using weighted summation and averaging in various modes. Simulation results show that the identification results of the proposed method are better than those of the inner product vector damage index, and the damage identification method based on inner product vector and gray cloud can solve the uncertain damage problem caused by measurement noise.