Zhang Qing, Fu Xing, Jiang Wenqiang, Jin Hengdong
Yanzhao Electric Power Laboratory of North China Electric Power University, Baoding 071003, China.
Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, North China Electric Power University, Baoding 071003, China.
Sensors (Basel). 2025 Jul 27;25(15):4659. doi: 10.3390/s25154659.
Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation.
输电塔是关键的电力基础设施,但其结构损伤可能会在长期服役过程中不断累积,这凸显了损伤识别研究的至关重要性。本文提出了一种基于时域响应分析的互相关函数幅值向量(CorV)损伤定位方法。该方法包括利用动态响应数据计算损伤前后结构构件的CorV,采用CorV保证准则(CVAC)量化CorV的变化,并引入一阶差分进行损伤定位。以江门一座实际输电塔为工程背景,建立了有限元模型。通过降低特定构件的刚度来模拟损伤情况,并进行参数分析以验证所提方法。此外,还在实验室进行了实验验证以提供进一步的确认。结果表明,受损结构的CVAC值显著低于健康状态下的值。通过分析CorV各分量的相对变化,可以准确确定损伤位置。值得注意的是,该方法仅需获取输电塔在随机激励下的时域响应信号,即可检测损伤的存在和位置。因此,它非常适合在环境激励下对输电塔进行结构健康监测。