Damage localization and robust diagnostics in guided-wave testing using multitask complex hierarchical sparse Bayesian learning

Abstract

The inversion of guided-wave data for accurate damage localization is a challenging problem when using guided waves for nondestructive testing and robust diagnostics. It is especially important to detect incorrect damage localization without knowing the original damage. In this paper, a new damage localization and robust diagnostics method is proposed. A Multi-task Complex Hierarchical Sparse Bayesian learning (MuCHSBL) algorithm is presented to solve the inverse problem for damage localization based on the data measured from a small number of sensors. The multi-task model improves the efficacy of damage localization by utilizing the consistency of damage locations for tasks with different signal frequencies. A Sparse Bayesian learning algorithm is also introduced to utilize the spatial sparsity of the damage, since structural damage typically occurs at only a few localized areas. The quantified posterior uncertainty of the model parameters gives a sense of confidence in the damage localization results. Utilizing the different levels of uncertainty in the optimal and suboptimal inversion models, diagnostic tools are proposed to detect whether the inversion for damage localization is accurate, without knowing the original damage. Numerical and experimental studies are carried out to verify the effectiveness of the proposed method. It is demonstrated that the damage localization efficacy of multi-task model is much higher than that of single-task model; moreover, the accuracy of damage localization results can be diagnosed effectively by the posterior uncertainty quantification of the model parameters.