In this paper, the use of a phase-sensitive optical time-domain reflectometry (ϕ-OTDR) sensor for the detection of the Lamb waves excited by a piezoelectric transducer in an aluminum plate, is investigated. The system is shown to detect and resolve the Lamb wave in distinct regions of the plate, opening the possibility of realizing structural health monitoring (SHM) and damage detection using a single optical fiber attached to the structure. The system also reveals the variations in the Lamb wave resulting from a change in the load conditions of the plate. The same optical fiber used to detect the Lamb waves has also been employed to realize distributed strain measurements using a Brillouin scattering system. The method can be potentially used to replace conventional SHM sensors such as strain gauges and PZT transducers, with the advantage of offering several sensing points using a single fiber.

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 (ϕ-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 ϕ-OTDR technology, resulting in accurate damage detection and localization.

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In this paper, we make use of a phase-sensitive time domain reflectometry (phi-OTDR) sensor with 60-cm spatial resolution to detect the Lamb waves generated by a piezo-ceramic actuator in an aluminum plate. Furthermore, a machine learning algorithm based on Support Vector Machine (SVM) classifiers was employed for damage localization. We show that SVMs are able to identify the characteristics in Lamb wave signals that may be linked to damage location. This study makes full use of the rich information provided by the phi-OTDR sensor, extracting damaged data from diverse damage spots. The results indicate that the proposed technique has the potential to identify and locate damages in thin-plate structures.

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