随机共振结合RobustICA的两阶段结构损伤定位方法

为实现强噪声背景低信噪比环境下的结构损伤识别,提出一种基于非线性随机共振降噪与鲁棒性独立分量分析(RobustICA)的两阶段损伤定位方法.第一阶段,运用非线性随机共振系统对强噪声低信噪比的测量响应进行预处理,以降低背景噪声的干扰并增强结构响应;第二阶段,结合RobustICA提取包含损伤信息的特征分量对结构响应异常进行识别,之后计算归一化的源分布向量(NSDV)的最大值对结构损伤异常进行定位.框架数值算例结果表明,所提出的算法能够较精确实现信噪比为5dB下的结构损伤异常识别与定位.

Two-stage damage localization method by integrating stochastic resonance theory and RobustICA algorithm

In order to realize the structural damage identification problem in the case of strong noise and low signal-to-noise ratio (SNR) environment, this paper presents an adaptive stochastic resonance-optimal algorithm that is able to adaptively determine the parameters in stochastic resonance (SR) system and to obtain the optimal output responses. On this basis, a two-stage damage location approach based on independent component analysis and nonlinear denoising derived from SR theory is proposed in this paper. In the first stage, structural dynamic responses and background noise are processed through the nonlinear SR system for enhancing the response signal. In the second stage, the feature components involving damage information are extracted via the robust independent component analysis(RobustICA) algorithm and then are used to detect the structural response anomalies. Afterwards, the maximum of normalized source distribution vector (NSDV) is computed. On the basis of determined the damage index, the maximum of NSDV is employed to locate the structural damage. Numerical results of a frame show that the proposed algorithm can be successfully implemented the instant and location of damage in the case of low SNR with 5dB.