Modeling and Signal Processing of Bulk Acoustic Wave Passive Wireless Strain Sensors

Untethered, battery-less, and chip-less passive wireless strain sensors have been widely investigated to overcome the drawbacks of conventional sensors for structure health monitoring of large civil structures. Although the-state-of-the-art passive wireless sensors enable long-range, high-resolution measurements, the signal processing of these sensors is still a challenging task. Passive wireless sensors require an algorithm to capture their resonant frequencies from noisy signals. In this article, we propose an algorithm based on rational polynomial functions to fit the full waveform of bulk acoustic wave (BAW)-based passive wireless strain sensors. We establish an analytical expression for the signal and simplify it based on multiple constrains. Numerical simulations show that the simplified fitting functions can accurately extract the peak frequency of the resonant signal when these constraints are satisfied. The experimental demonstrations confirm that passive wireless sensors utilizing this algorithm achieve a resolution of 4 μ ɛ and a refresh rate of 7.5 Hz. In addition, we used the proposed algorithm to realize the vibration frequency measurement of a cantilever beam with a first mode around 4 Hz. The proposed method has high accuracy and moderate speed in extracting the resonance frequency of passive wireless sensors, thus making it possible to realize noncontact measurements of strain changes or vibrations in large civil structures.