Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 20; p. 6760
• Main Authors: Buckley, Tadhg, Ghosh, Bidisha, Pakrashi, Vikram
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 12.10.2021; MDPI
• Subjects: Access control; Algorithms; Communication; damage sensitive feature; Data compression; Data processing; Data transmission; edge; Fault diagnosis; Internet of Things; IoT; long-range; microcontroller; Monitoring systems; Sensors; Signal processing; Spread spectrum; structural health monitoring; Systems design; Wireless networks
• ISSN: 1424-8220; 1424-8220
• 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.