A time-reversal assisted probabilistic fusion strategy for damage localization in plate-like structures using Lamb waves

Abstract

Precise damage identification and quantification is of great significance to ensure structural performance and provide early-warning for safety maintenance. In this research, a time-reversal assisted probabilistic strategy is developed to accurately localize damage via baseline-free manner in plate-like structures by fusing damage prediction data obtained from the elliptical trajectory location method (ETLM) and reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm. Damage features, including time difference for ETLM and correlation coefficient index (CCI) for RAPID algorithm, are extracted from the time-reversal focusing signal using the Hilbert transform (HT). To make full use of the damage-related information contained in the time-reversal focusing signals and improve the localizing accuracy, a decision-level data fusion based on Bayesian inference is proposed to fuse damage features and reconstruct damage information. A series of numerical and experimental studies, including different configurations of damage cases and transducer distributions, were conducted to investigate the performances of the proposed time-reversal assisted probabilistic method on damage localization. Results shows that the proposed method could successfully achieve accurate damage localization via baseline-free manner and significantly reduce the damage artifacts compared to traditional methods.

Bolt tightness monitoring using multiple reconstructed narrowband Lamb waves combined with piezoelectric ultrasonic transducer

“…To mitigate this issue, narrowband tone burst excitations with carefully selected frequencies are generally employed to obtain a pure wave mode and minimize the dispersion. Such excitations have been widely employed to evaluate and localize damages [24], while narrowband excitations confront two main challenges for applications in ultrasonicbased structural health monitoring: the determination of the optimal frequency for damage detection [25] and the nonmonotonic trend of damage index with the increasing of defects [26].…”