Effect of sampling rates on the accuracy of acoustic-laser technique in defect detection and upsampling using machine learning

Renyuan Qin; Denvid Lau

doi:10.1117/12.2582919

22 March 2021 Effect of sampling rates on the accuracy of acoustic-laser technique in defect detection and upsampling using machine learning

Renyuan Qin, Denvid Lau

Author Affiliations +

Proceedings Volume 11592, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV; 115920G (2021) https://doi.org/10.1117/12.2582919
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Abstract

As a non-destructive testing (NDT) method, the acoustic-laser technique has demonstrated its effectiveness in defect detection of composite systems. The technique is particularly useful for the detection of near-surface defects in fiber reinforced polymer bonded concrete by vibrating the material with an acoustic excitation and measuring the vibration signals with a laser beam. However, same as the other vibration-based measurement methods, the accuracy of acoustic-laser technique is sensitive towards sampling rate during the measurements. The sampling rate of acoustic-laser measurement adopted in previous studies is as high as 50000 Hz to assure the accuracy of measurement. However, such high sampling rate cannot always be guaranteed due to the limitation of data acquisition system, or any missing data generated during the measurement. In this study, the effect of sampling rates on the accuracy of acoustic-laser technique is investigated through the experimental study. Six sampling rates, i.e. 10 Hz, 100 Hz, 1000 Hz, 10000 Hz, 25000 Hz, and 50000 Hz, are adopted to measure the FRP bonded system with an interfacial defect, in order to study the relationship between the sampling rate and measurement accuracy. Moreover, an upsampling method using machine learning is proposed in this study, in order to reconstruct the missing data to the target sampling rate, so that the accuracy of the detection can be improved from low sampling rate measurement with missing data.

Conference Presentation

Citation Download Citation

Renyuan Qin and Denvid Lau "Effect of sampling rates on the accuracy of acoustic-laser technique in defect detection and upsampling using machine learning", Proc. SPIE 11592, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, 115920G (22 March 2021); https://doi.org/10.1117/12.2582919

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