基于时间序列模型自回归系数灵敏度分析的结构损伤识别方法

STRUCTURAL DAMAGE IDENTIFICATION BASED ON SENSITIVITY ANALYSIS OF AUTOREGRESSIVE COEFFICIENTS OF TIME SERIES MODELS

  • 摘要: 该文在分析了时间序列模型的自回归系数对结构单元刚度灵敏度的基础上,提出了一种采用随机载荷作用下结构的时域响应数据进行损伤识别的新方法。该方法首先根据随机载荷作用下的结构响应拟合适用的时序模型;然后建立基于自回归系数的损伤灵敏度矩阵,通过该矩阵可以建立由单元损伤导致的自回归系数的变化与损伤系数变化之间的关系;最后通过求解损伤系数向量来识别损伤位置和损伤程度。对一悬臂梁结构损伤识别的数值结果表明:在计入1%和2%测量噪声的情况下,该方法仅利用单个传感器的时域测量数据,就能够较好地识别单个单元和多个单元损伤;如果对基于多个传感器的识别结果进行综合,识别结果则更加准确、可靠。

     

    Abstract: A new method is developed for identifying structural damages at the element level by using time-domain response data at a few points caused by random loadings. In the study, a time series model with a fitting order is first constructed using the time domain response data with measurement noise. A sensitivity matrix consisting of the first differential of the autoregressive coefficients of the time series models with respect to the stiffness of the structural elements is then obtained based on simulated time domain response data. Finally, the locations and severities of the damage can be easily obtained by solving for the damage vector whose components are the damage degrees of the structural elements. The efficiency and capability of the proposed method are demonstrated by applying the method to a cantilever beam with damage at one or two elements. Numerical simulations show that the use of one sensor acceleration history data with 1% and 2% measurement noises is capable of identifying damages at a single element and multiple elements efficiently and the increase in numbers of sensors is certainly helpful for improving the diagnosis correction ratio.

     

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