Buckley Tadhg, Ghosh Bidisha, Pakrashi Vikram
UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Materials Engineering, University College Dublin, Dublin 4, Ireland.
QUANT Group, Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland.
Sensors (Basel). 2021 Oct 12;21(20):6760. doi: 10.3390/s21206760.
Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100-500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.
有效的结构健康监测（SHM）通常需要进行连续监测，以捕捉结构中感兴趣特征的变化，而这些结构往往远离电源。一个关键挑战在于传感器的低功耗数据持续传输。尽管在远程低功耗通信（如LoRa、窄带物联网）方面取得了重大进展，但针对低功耗结构健康监测仍缺乏足够的示范基准。损伤检测通常基于从加速度信号计算得出的监测特征，由于采样频率（约100 - 500赫兹），此类数据量很大。低功耗远程通信在数据包大小和频率方面都受到限制。然而，微控制器的效率越来越高，能够对损伤敏感特征进行本地计算。本文展示了通过低功耗、远程、无线、低成本且现成的组件实现边缘结构健康监测（Edge - SHM）框架。利用低功耗微机电系统（MEM）加速度计和微控制器开发了一个定制装置，在设定的时间间隔内计算频率和时域特征，然后将其发送到云平台。对由电动振动台激励的悬臂梁进行监测，通过在固定边界处控制螺栓松动来引入损伤，从而在其固定端引入旋转。结果表明了物联网驱动的边缘平台如何能从连续监测中受益。
