Zhang Xuan, Li Luyu, Qu Gaoqiang
School of Civil Engineering, Dalian University of Technology, Dalian 116024, China.
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2024 Jan 13;24(2):505. doi: 10.3390/s24020505.
In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method's advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.
在结构健康监测（SHM）中，当前大多数方法和技术都基于线性模型和线性损伤的假设。然而，实际工程结构中的损伤更多地表现为非线性行为，包括裂缝的出现和螺栓的松动。为了更有效地解决结构非线性损伤诊断问题，本研究将自回归（AR）模型与幅度感知排列熵（AAPE）相结合，提出了一种数据驱动的损伤检测方法。首先，针对基线状态下每个结构传感器的加速度数据建立AR模型，包括使用基于贝叶斯信息准则（BIC）的改进迭代方法确定模型阶数并计算模型系数。随后，在测试阶段，提取AR模型的残差作为损伤敏感特征（DSF），并计算AAPE作为损伤分类器来诊断非线性损伤。利用六层建筑模型的数值模拟和洛斯阿拉莫斯实验室三层框架结构的实验数据来说明所提方法的有效性。此外，为了证明本方法的优势，我们将AAPE与其他先进的单变量损伤分类器进行了比较分析。数值和实验结果证明了所提方法在检测和定位微小损伤方面的优势。而且，该方法适用于分布式传感器监测系统。
