基于模糊神经网络的数据融合结构损伤识别方法

为了有效利用结构健康监测系统中的多源传感器数据信息,提高损伤检测与评估的识别正确率,该文通过构造模糊神经网络分类器,提出了一种基于模糊神经网络的数据融合损伤识别方法并将之应用于结构健康诊断中。它先通过数据预处理,提取原始响应信号中的特征参数,接着将之作为模糊神经网络的输入,构造模糊神经网络模型进行识别决策,最后运用数据融合算法,计算出数据融合后的决策结果。为了验证所提方法的有效性,通过一个7自由度的建筑模型,分别用单一模糊神经网络决策器和数据融合损伤识别方法进行了损伤识别和比较。研究结果表明:该文所提方法比单一决策结果更准确、可靠。

STRUCTURAL DAMAGE IDENTIFICATION METHOD WITH DATA FUSION BASED ON FUZZY NEURAL NETWORK

In order to make full use of the information collected by multi-source sensors and to increase the damage identification accuracy of a structural health monitoring system, a damage identification method with data-fusion based on fuzzy neural network is proposed in this paper. In this method, original structural response data is preprocessed and feature parameters are extracted. The parameters are used as the input of the fuzzy neural network model, and decision is obtained using this model. Finally, fusion decision results are analyzed by data fusion algorithms. A 7-degree-of-freedom building model is utilized to validate the proposed method, and a comparison is made between this method and a single fuzzy neural network model. The results show that the proposed damage identification method is more exact and reliable than that of a single fuzzy neural network model.