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Daniel Coble

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Proceedings Article | 18 April 2023 Presentation + Paper

Non-linear vibration signal compensation technique for UAV-deployable sensor packages with edge computing

Joud Satme, Daniel Coble, Hung-Tien Huang, Austin R. Downey, Jason Bakos

Proceedings Volume 12486, 124861M (2023) https://doi.org/10.1117/12.2658563

KEYWORDS: Sensors, Education and training, Data modeling, Vibration, Structural health monitoring, Performance modeling, Accelerometers, Systems modeling, Signal to noise ratio, Signal attenuation

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For rapid assessment of infrastructure, the use of minimally invasive sensors that can be deployed remotely using autonomous vehicles is gaining popularity. Such systems are favorable for their ease of deployment and cost-effectiveness. Utilizing electropermanent magnets or adhesives to mount the sensors temporarily forms a barrier between the sensor and the structure being examined. This barrier creates undesirable nonlinearities and transmissibility losses that introduce errors into structural damage detection algorithms. Post-processing of signals using continuous modeling techniques from classical control theory can be applied to the collected signals to remove this error. However, post-processing creates additional analysis steps that require the signal to be taken off device. Processing the data at-the-edge prior to saving it to memory or transmitting it to a base station enables rapid assessment of infrastructure. With minimal time from signal detection to prognostics, such systems can be used in damage forecasting and infrastructure failure prevention. This preliminary work aims to develop a non-linear machine-based compensation technique that is resource and power efficient enough to be processed on-device. The proposed long short-term memory (LSTM) error-compensating network demonstrated potential by increasing the SNRdB by 9.3% and improving RMSE by approximately 20% while widening the usable lower limit of the sensor’s bandwidth from 2.78 to 1.34 Hz. The progress described in this report focuses on setting the framework for the proposed method and paves the way for a full-scale hardware implementation in the near future

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