基于数据融合和“能量-损伤”的结构状态特征提取

Feature Extraction of Structure Status Based on Data Fusion and Wavelet Analysis

基于一致性数据融合算法和小波包分析,提出了结构状态特征提取方法。采用改进的一致性算法融合多传感器的测量数据,克服了一致性算法中两传感器在测量精度不同时置信距离不同的缺点,对支持矩阵进行模糊化处理,并采用"能量-损伤"特征提取技术对融合后的多传感器测量数据进行处理。利用5层框架结构的数值算例对比了结构各层加速度信号与融合后加速度信号构造的特征向量。结果表明,融合后加速度信号构造的特征向量包含了结构不同位置的不同状态信息,能够全面描述结构的工作状态。

A new structural status feature extraction method based on data fusion and wavelet analysis is presented.The data measured by multi-sensors are fused by using an improved consensus algorithm.It overcomes the shortcoming of the conventional consensus algorithm with two sensors,which has different confidence distances for different measurement precisions.The supporting matrix is fuzzified,which can avoid the subjective error in determining the threshold value.Structural eigenvectors extracted from the acceleration signals by data fusion and by no data fusion are compared through a numerical example.The result shows that the structural feature extracted by data fusion includes more structural status information in different sites and it can describe the structural status better.