To monitor the health of large-scale structures, a wireless measurement system, such as a bridge, is required. One of the methods of monitoring the health of large-scale structures involves the application of an impedance-loaded wireless surface acoustic wave (SAW) sensor. Additionally, a pressure-sensor-loaded SAW sensor can detect the vibration of a cantilever. In this study, a continuous wavelet transform (CWT) is adopted to analyze the sensor responses. The CWT results obtained were classified into two categories based on the attenuation at each frequency, which include the exponential or linear type. Furthermore, machine learning was applied to evaluate cantilever damage. The results indicate that a high accuracy evaluation of damage is feasible with the proposed method.

为了监测大型结构的健康状况，需要无线测量系统，例如桥梁。监测大型结构健康状况的方法之一涉及应用阻抗加载的无线表面声波 (SAW) 传感器。此外，装有压力传感器的 SAW 传感器可以检测悬臂的振动。在这项研究中，采用连续小波变换（CWT）来分析传感器响应。获得的 CWT 结果根据每个频率的衰减分为两类，包括指数型或线性型。此外，机器学习被应用于评估悬臂梁损伤。结果表明，使用所提出的方法对损伤进行高精度评估是可行的。