摘要
基于结构加速度时间序列提出了一种新的损伤识别方法。首先,获取结构在无损伤状态下的加速度数据并进行分段,以各段数据的AR(auto-regressive)模型系数向量作为结构的参考状态样本,将未知状态的加速度AR模型系数向量分别加入参考状态样本中,构成多个原始数据矩阵;其次,对多个原始数据矩阵分别进行主成分分析得到前两阶主成分,并建立相应的椭圆控制图,以前两阶主成分在控制椭圆中的分布情况来判别结构是否存在损伤;最后,以一钢框架结构试验为例识别结构的两种损伤模式。结果显示,该方法能够准确、直观地识别结构是否存在损伤,相对于马氏距离判别法具有更强的稳定性。
A new method of damage identification is presented based on the time series of structure acceleration data. Firstly, the acceleration data of undamaged structure is sampled and partitioned into several streams, the auto regressive (AR) coefficients of all data streams are served as reference samples. Secondly, the AR coefficients of damaged structure acceleration data are added into the reference samples separately, and several original data matrixes are constructed. Then, the principal component analysis (PCA) is used to extract the first two principal components (PC) of these matrixes, and the corresponding control ellipses are constructed. It is observed that the first two PCs from damaged structure are all out of the corresponding control ellipse. At last, the experiment of a steel-made frame structure is used to test the validity of the method, the testing results show that the method can identify the two different damage modes correctly, and has more stability than the one based on Mahalanobis distance.
出处
《振动.测试与诊断》
EI
CSCD
北大核心
2012年第5期841-845,868,共5页
Journal of Vibration,Measurement & Diagnosis
关键词
AR模型
主成分分析
控制椭圆
马氏距离
损伤识别
AR model,principle component analysis,control ellipse,Mahalanobis distance,damage identification