基于EEMD与FastICA的损伤异常识别与定位

发布时间：2018-04-08 10:14

本文选题：总体平均经验模态分解　切入点：快速独立分量分析　出处：《振动与冲击》2016年01期

【摘要】：为了准确地提取结构损伤异常信息,消除小波奇异值分解时存在需要特定的小波基和分解层数以及经验模态分解(EMD)方法存在诸如虚假模态混叠等问题,提出一种基于改进的总体平均经验模态分解(EEMD)与快速独立分量分析(Fast ICA)相结合的提取结构损伤特征并进行识别与定位的新方法。首先,通过EEMD对结构动力响应信号进行预处理并用Fast ICA提取出包含损伤信息的特征分量对结构响应异常进行识别和初步定位;然后,计算归一化的源分布向量(NSDV)的最大值,并根据该最大值精确定位结构损伤。最后,通过框架数值算例和试验进行了所提方法的验证,结果表明该算法能够较好地进行结构损伤异常的识别与定位。
[Abstract]:In order to extract structural damage anomaly information accurately, there are some problems such as false mode aliasing and so on, which need special wavelet basis and decomposition layer number and empirical mode decomposition (EMD) method to eliminate wavelet singular value decomposition.A new method based on improved average empirical mode decomposition (EEMD) and fast independent component analysis (Fast ICA) is proposed to extract structural damage features and identify and locate them.Firstly, the structural dynamic response signal is preprocessed by EEMD and the characteristic component containing damage information is extracted by Fast ICA to identify and locate the structural response anomaly, and then the maximum value of normalized source distribution vector (NSDV) is calculated.According to the maximum value, the damage of the structure is accurately located.Finally, the proposed method is verified by numerical examples and experiments. The results show that the proposed method can identify and locate structural damage anomalies well.
【作者单位】：福州大学土木工程学院;
【基金】：国家自然科学基金(51278127,50878057) 国家十二五科技支撑计划(2012BAJ14B05)
【分类号】：TU317

【共引文献】