基于概率神经网络分类器的数据融合损伤检测方法

Data-fusion damage detection approach based on probabilistic neural network classifier

为了有效地利用来自大型结构健康监测系统大量冗余、互补的信息进行结构健康状况评估,本文从数据融合的基本原理入手,将小波分析、概率神经网络和数据融合等技术有机地结合起来,提出了一种5阶段的决策级数据融合损伤检测新方法,最后用2个数值算例验证了方法的有效性,并探讨了测量噪声对损伤识别的影响。研究结果表明,所提出的方法是可行、有效的。

In order to make full use of the redundant and complementary information and thus assess the structural health states from a large structural health monitoring system, the principle of data fusion was first introduced in this paper, then a 5-phase novel decision-level data fusion damage detection approach by integrating wavelet analysis, probabilistic neural network （PNN） and data fusion developed and implemented. Finally, two numerical examples validated the proposed method, the effect of measurement noise on identification accuracy was investigated as well. The result shows that the proposed method is feasible and effective for damage identification.