|
Structural Monitoring and Maintenance Volume 4, Number 4, December 2017 , pages 381-396 DOI: https://doi.org/10.12989/smm.2017.4.4.381 |
||
|
Harnessing sparsity in lamb wave-based damage detection for beams |
||
Debarshi Sen, Satish Nagarajaiah and S. Gopalakrishnan
|
||
| Abstract | ||
| Structural health monitoring (SHM) is a necessity for reliable and efficient functioning of engineering systems. Damage detection (DD) is a crucial component of any SHM system. Lamb waves are a popular means to DD owing to their sensitivity to small damages over a substantial length. This typically involves an active sensing paradigm in a pitch-catch setting, that involves two piezo-sensors, a transmitter and a receiver. In this paper, we propose a data-intensive DD approach for beam structures using high frequency signals acquired from beams in a pitch-catch setting. The key idea is to develop a statistical learning-based approach, that harnesses the inherent sparsity in the problem. The proposed approach performs damage detection, localization in beams. In addition, quantification is possible too with prior calibration. We demonstrate numerically that the proposed approach achieves 100% accuracy in detection and localization even with a signal to noise ratio of 25 dB. | ||
| Key Words | ||
| sparsity; lamb waves; damage detection; statistical learning | ||
| Address | ||
| Debarshi Sen: Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, MS 318, Houston, TX 77005, USA Debarshi Sen and Satish Nagarajaiah:Department of Mechanical Engineering, Rice University, 6100 Main Street, MS 318, Houston, TX 77005, USA S. Gopalakrishnan: Department of Aerospace Engineering, Indian Institute of Science, CV Raman Road, Bangalore 560012, India | ||
