Ultrasonic Guided Wave Localization and Classification of Damage-like Perturbations in a CFRP Plate Using a PZT Network

Abstract

Ultrasonic guided waves acquired using permanently bonded piezoelectric transducers enable large-area monitoring of composite structures. For practical structural health monitoring; however, both the location and the condition of a structural change are critical. This paper presents a data-driven multi-task framework for a carbon-fiber-reinforced polymer plate using a circular PZT array. To generate a large, precise, and repeatable experimental dataset without permanently damaging the specimen, paired magnets were placed on opposite surfaces of the plate and moved using a two-axis positioning system over a uniform grid. Four magnet-pair configurations were used to form four balanced experimental classes. For each case, ultrasonic guided-wave signals from all actuator–sensor paths were concatenated and processed by a shared convolutional neural network and long short-term memory architecture. The model simultaneously estimates the spatial coordinates through two regression branches and identifies the magnet-pair perturbation configuration through a classification branch. An automated hyperparameter search was used to select the principal architectural and training parameters. Performance was evaluated using five-fold training/validation cross-validation. The selected configuration achieved a classification accuracy of 0.9875±0.005 and a macro F1-score of 0.9876±0.0048. The mean normalized Euclidean localization error was 0.0228±0.0009, and 99.43% of the de-normalized localization errors were at or below 20mm. The shared architecture therefore performs simultaneous perturbation-configuration classification and two-dimensional localization from the same multi-path guided-wave measurements under the investigated controlled perturbation conditions.