Damage localization in plate-like structures using time-varying feature and one-dimensional convolutional neural network

•A new time-varying damage index that preserves temporal information was proposed.•A convolutional neural network was developed to further extract temporal features.•Damage localization was realized using four transducers and limited training data.•Good transferability and robustness of the CNN were demonstrated.•Once constructed, the CNN can predict damage location in milliseconds. Lamb wave-based SHM technology for damage detection and localization in plate-like structures has typically relied on post-processing of ultrasonic guided waves. Traditionally, the damage localization is realized using the time-of-flight (TOF) of damage-scattered waves. However, this method often requires the identification of a pure mode from the wave signal which is difficult in many cases. Damage index (DI) based methods offer another type of approaches that do not need such singal explanation. Since DI alone doesn’t contain temporal information, data fusion of signals from multiple actuator-sensor pairs must be performed for localization. As a result, a relatively dense actuator-sensor network is needed, and localization can only be realized within the region covered by the network. Realizing that temporal information contained in the wave signal is extremely important to damage localization, we propose a time-varying DI feature that preserves the temporal information to improve localization accuracy. In addition, we propose to use one-dimensional convolutional neural network (1-D CNN) to correlate the time-varying DI directly with the damage location. The equivariance property of CNN preserves the temporal information. The efficiency and feature extraction capability of the CNN help to build a neural network model with certain generalization capability, and thus the model trained on one plate can be applicable to a new plate. The performance of the proposed method was demonstrated in three cases: localization in the same plate with different damage locations, localization in a new plate with the same damage locations, and localization in a new plate but with different damage locations. Despite that only four transducers were used, and limited experimental data for training were available, good results have been obtained. Performance comparison with several other existing methods was also conducted.