基于数据融合和小波分析的结构损伤诊断

基于数据融合和小波分析理论，提出一种新的结构损伤诊断方法。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，避免了人为定义阈值而产生的主观误差。利用小波分析的降噪和多尺度分辨能力对多传感器的数据进行分析处理，从而对结构损伤作出诊断识别。通过数值算例，验证了该方法可以充分利用所有传感器的有效信息，能够在部分传感器性能降低（如受到噪声影响），甚至是完全失效的情况下，对结构损伤作出正确诊断。

Diagnosis of Structural Damage Based on Data Fusion and Wavelet Analysis Method

A new structural damage diagnosis method based on data fusion and wavelet analysis method is presented. The measuring data from multi-sensors are fused by improved consensus algorithm. It overcomes the shortcoming of the traditional consensus algorithm with two sensors, which has different confidence distance for different measuring precision. And the supporting matrix is fuzzified, which can avoid the subjective error in determining the threshold value. The data from multi-sensors are processed by wavelet analysis using its noise reduction ability and multi-scales resolving power, and then the damage of the structures can be diagnosed. The effectiveness of the method is confirmed through numerical examples. It can be concluded that the method could make full use of the data from multi-sensors and it could get correct diagnosing result even when part of sensors is affected or fully disabled.