Effective Combination of Modeling and Experimental Data with Deep Metric Learning for Guided Wave-Based Damage Localization in Plates

Data-driven methods have emerged as promising approaches for guided wave-based damage localization. The success of the methods highly depends on the amount and the quality of the training data. Modeling training data are easy to obtain and abundant but could be unrealistic with biased model parameters and experimental uncertainties unconsidered. Experimental training data are realistic but scanty due to the high cost and low accessibility. To tackle this problem, this work utilizes both types of data to collect sufficient training data and learn a mapping function invariant to the modeling errors and experimental noises in a supervised manner, and thus to improve localization accuracy. Specifically, deep metric learning is conducted on the hybrid data to identify a distance metric, based on which the discrepancy between two sets of data is minimized, i.e., the distance metric is insensitive to the superfluous variations caused by modeling errors and experimental noises. Then, a representative non-parametric regression algorithm that relies on the distance metric, kernel regression, is used to predict damage locations. The performance of the proposed method is demonstrated on damage localization in plates and compared with several other data-driven methods. Results show that that proposed method can serve as a general strategy to overcome the shortage of labelled training data in data-driven methods for damage localization.