Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines

In this paper, we propose and demonstrate a damage detection technique based on the automatic classification of the Lamb wave signals acquired on a metallic plate. In the reported experiments, Lamb waves are excited in an aluminum plate through a piezoelectric transducer glued onto the monitored structure. The response of the monitored structure is detected through a high-resolution phase-sensitive optical time-domain reflectometer (phi-OTDR). The presence and location of a small perturbation, induced by placing a lumped mass of 5 g on the plate, are determined by processing the optical fiber sensor data through support vector machine (SVM) classifiers trained with experimental data. The results show that the proposed method takes full advantage of the multipoint sensing nature of the phi-OTDR technology, resulting in accurate damage detection and localization.

Automated summary

In this paper, a damage detection technique based on automatic classification of Lamb wave signals is proposed and demonstrated. Lamb waves are excited in an aluminum plate using a piezoelectric transducer, and the response is detected through a high-resolution phase-sensitive optical time-domain reflectometer (phi-OTDR). The proposed method uses support vector machine (SVM) classifiers trained with experimental data to accurately determine the presence and location of a small perturbation induced on the plate. The results show that this method effectively utilizes the multipoint sensing nature of phi-OTDR technology for accurate damage detection and localization.