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Damage evaluation of fixed beams at both ends for bridge health monitoring using a combination of a vibration sensor and a surface acoustic wave device

机译:使用振动传感器和表面声波装置的组合对两端固定梁进行损坏评估,以进行桥梁健康监测

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摘要

In recent years, the aging of engineering structures such as bridges has become a serious problem, and the development of an inexpensive and mass-producible structural health monitoring (SHM) system is desired. In this study, we proposed an SHM method for bridges using an impedance-loaded surface acoustic wave (SAW) sensor, which is a combination of a SAW sensor and an impedance-changing sensor. The impedance-loaded part comprised a vibration sensor, that is a geophone, and variable-capacitance diode, and vibration experiments were conducted on fixed beams at both ends that simulated a bridge. The results from these experiments were subjected to frequency analysis under the continuous wavelet transform, and it was confirmed that unsupervised machine learning could be used to discriminate damage with high accuracy. Furthermore, it was found that a one-class support vector machine, used for anomaly detection in machine learning, was suitable for the SHM method of bridges.
机译:近年来,桥梁等工程结构的老化已成为一个严重的问题,需要开发一种廉价且可大规模生产的结构健康监测(SHM)系统。在这项研究中,我们提出了一种使用阻抗负载表面声波(SAW)传感器的桥梁SHM方法,该传感器是SAW传感器和阻抗变化传感器的组合。负载阻抗的部分由振动传感器(即地震检波器)和可变电容二极管组成,在模拟桥梁的两端固定梁上进行了振动实验。对连续小波变换下的实验结果进行了频率分析,证实了无监督机器学习可用于高精度判别损伤。此外,还发现用于机器学习异常检测的单类支持向量机适用于桥梁的SHM方法。

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