In the long-term monitoring of the structural,the recorded signals usually contain thousands of data and various environment noise.This leads to the fact that it is impossible to efficiently and exactly identify the change happened in structure.To solve this problem,this paper proposed a structural damage identification method based on the wavelet de-noising technique with the signal converted into an order statistic.After that,the principal component analysis is adapted to extract the feature vectors of the order statistics containing the changes of structure and to reduce the data dimension.Finally,the damage index and control are established based on feature vectors using statistic knowledge.The change of structure could be observed by the damage index and the damages level of structure could be evaluated by the control line.The proposed method is verified by using the data obtained from a numerical simulation and the measurement for a real bridge.The result shows that the proposed damage identification method can efficiently extract the characteristic information of the vibration signal and that it can accurately the statechanges of structure.%对服役工程结构的状态进行长期监测的过程中,所测信号的数量往往非常巨大,而且信号包含有各种频率成分的环境噪声干扰,严重阻碍了准确识别工程结构状态的效率和准确率.针对这个问题,本文提出了基于小波降噪和主成分分析的结构损伤识别方法.首先采用小波降噪对测试信号进行处理并转换成顺序统计量,然后运用主成分分析对顺序统计量进行降维,提取有用特征矢量,最后利用统计方法构造损伤指标和控制线,通过控制线识别结构的状态变化.同时,论文使用某在役钢架桥的数值模拟及真实实验测量数据对该识别方法进行了验证,结果表明,该损伤识别方法能够有效提取振动信号的特征信息,能准确识别结构的状态变化.
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