基于时间序列模型的结构损伤识别方法
Structural Damage Identification Method Based on Time Series Model
投稿时间:2019-03-04  修订日期:2019-10-17
DOI:10.11908/j.issn.0253-374x.2019.12.001     稿件编号:    中图分类号:TU317;TU391
 
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中文摘要
      针对现有结构损伤识别方法中因模型参数物理意义不明确而导致的损伤信息遗漏等问题,提出一种基于时间序列模型的损伤识别方法.首先,推导了具有外部输入的自回归模型(ARX)的一般表达式,并通过联立多自由度体系运动方程建立了考虑结构动力特性的ARX模型.随后,运用该模型预测得到未损伤情况下的节点加速度时程序列,根据其与实测数据的差异程度构造表征结构损伤的参数,即损伤因子.最后,根据损伤因子数值大小与分布情况评估结构损伤状态.数值算例结果表明,该方法在较少的测量数据样本下,能够较好地识别单位置与多位置损伤,并可较为准确地判断损伤程度,同时识别结果受激振位置与测量噪声的影响较小.
英文摘要
      Aiming at the problem of missing damage information caused by the unclear physical meaning of model parameters in the existing structural damage identification method, a damage identification method based on time series model is proposed. Firstly, the general expression of auto-regressive model with eXogenous input (ARX) is derived. The ARX model considering structural dynamic characteristics is established by the simultaneous multi-degree-of-freedom system motion equation. Subsequently, the model is used to predict the nodal acceleration under undamaged conditions, and the parameters of the structural damage, i.e. the damage factor, are constructed according to the difference between the measured data and the predicted data. Finally, the structural damage state is evaluated based on the magnitude and distribution of the damage factor. The numerical results show that the proposed method can perfectly identify single-position and multi-position damage under less measurement dates, and can make a more accurate judgment on the damage degree. At the same time, the influence of the excitation position and measurement noise is small.
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