Automatic control of AC bridge-based capacitive strain sensor interface for wireless structural health monitoring
Taher, Sdiq Anwar
Is Version Of
Civil, Construction and Environmental EngineeringElectrical and Computer EngineeringCenter for Nondestructive Evaluation
Thin film-based flexible strain sensors have various advantages for structural health monitoring (SHM) because of their capability to sustain large deformations and cover large area of structural surfaces, making them ideal candidates for applications to complex geometries and structural crack monitoring. The authors previously developed a flexible strain sensor technology based on a soft elastomeric capacitor (SEC) for SHM and investigated an alternating current (AC) bridge-based method to transform the strain-induced dynamic capacitance changes in the SEC into analog voltage signals. Previous experiments have verified the capability of the SEC and the AC bridge-based signal converter for structural strain sensing applications. However, careful manipulation requirement for precise AC-bridge balancing, signal amplification control, and shunt calibration limits its practical use for full-scale SHM in field conditions. This study addressed such limitations with critical updates in both hardware and software with fully automated features for high-sensitive capacitive strain sensing. Newly developed hardware and software are fully controlled with a low-cost microcontroller ATmega328p in an automated way. A series of lab tests validated the prototype's performances in the capacitive strain sensing, outperforming an off-the-shelf wired capacitance measurement system (about 34% lower measurement noise), and confirmed that automatic control features worked as designed.
This is a manuscript of an article published as Jeong, Jong-Hyun, Hongki Jo, Simon Laflamme, Jian Li, Austin Downey, Caroline Bennett, William Collins, Sdiq Anwar Taher, Han Liu, and Hyung-Jo Jung. "Automatic control of AC bridge-based capacitive strain sensor interface for wireless structural health monitoring." Measurement (2022): 111789. DOI: 10.1016/j.measurement.2022.111789. Copyright 2022 Elsevier Ltd. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Posted with permission.