Low-power IoT Structural Health Monitoring System with Optimized Data Acquisition
Creators: Liu,Andi
Publisher: name: University of Alberta Library
Year: 2022

<p>For large-scale metallic structures, such as aircraft, ships, and pipelines, their failure could be catastrophic. A structural health monitoring system (SHM) to detect failures in advance is essential. The Internet-of-Things (IoT) is a key technology applied for remote structural health monitoring. A challenging problem that arises in this domain is power consumption. Currently, most IoT systems are battery-powered and only have limited power capacity. Nevertheless, frequently replacing the battery is undesirable because some IoTs may be installed in a remote area. The objective of this research is to design and develop an IoT-based structural health monitoring system that includes an ultra-low-power miniaturized sensing node integrated with a three-dimensional (3D) stress and strain sensor. The system can monitor 3D stress and strain values and upload them to the cloud.<br>
The developed IoT system consisted of a sensing node, a router, and a gateway, while the sensing node was composed of an acquisition unit and a 3D Micro-Electro-Mechanical System (MEMS) sensor to monitor 3D stress and strain of a structure. The acquired data was transmitted from a node to a router, then sent to the gateway, and finally uploaded to the cloud, which could be viewed remotely. Bluetooth Low Energy (BLE) was the major communication between node – router and router – gateway. Wi-Fi was used by the gateway to upload data to the cloud.<br>
The acquisition unit contained a low-power microcontroller with a built-in BLE wireless communication function, a high precision 16-bit analog-to-digital converter (ADC) and a 32Mbytes flash memory. It interfaced with a compact 3D MEMS strain-gauge typed sensor (7mm x 7mm) developed by a team in mechanical engineering at the University of Alberta. The sensor has high accuracy and stability, low power consumption, and small size. In order to optimize the power consumption of the sensing node, intelligent dynamic sampling rate, low power sleep mode and optimized transmission power level were implemented into the design. The dynamic sampling rate was based on the thresholding concept. When a number of consecutive detected stress/strains exceeded a pre-set threshold range, the sampling rate was changed from 5Hz to 20 Hz. Data sampling was changed from every 200ms to 50ms. The acquisition unit was put into sleep mode while not collecting or transmitting data to decrease the power consumption. A transmit power optimization algorithm was implemented to find the optimal transmit power level to conserve power in transmission mode. <br>
Experiments were conducted to determine the sensitivity and crosstalk of the ADC, the accuracy of the sensing node, the communication distance range between sensing node-router-gateway, the current consumption of the sensing node at different operating modes. Experiments were also conducted to validate the entire system. The results showed good sensitivity (0.51Ω) and accuracy (0.58±0.34Ω) of the sensing node, allowing detecting a sub-ohm resistance change on a strain-gauge type sensor. The maximum transmission distance was over 35m. The physical dimensions and power consumption of the sensing node were 35mm x 38mm x 10 mm, 24mW in active mode, and 9.4mW in sleep mode, respectively. The experimental results also demonstrated that the average power consumption of the sensing node was 14.0mW per hour if an average of 10 triggered high-frequency samplings occurred over a day. The system could last for more than seven days with a 1200mAh battery.</p>