Robust multi-damage localization in plate-type structures via adaptive denoising and data fusion based on full-field vibration measurements

Structural damage localization by using full-field vibration measurements is inevitably contaminated by measurement noise and not robust for multi-damage cases. To overcome these problems, a novel robust multidamage localization method is proposed based on adaptive denoising and data fusion. The major contributions are in three aspects. Firstly, an evaluator of multi-damage localization performance is proposed, which converts the damage localization into an optimization problem. Secondly, a hierarchical clustering is adopted to evaluate the damage zones by examining spatial characteristics of the damage. Thirdly, a data fusion strategy is developed based on the assessment of damage localization performance, which guarantees providing robust multi-damage localization results. In addition, numerical and experimental studies of multi-damaged plates are conducted to validate the feasibility and effectiveness of the proposed method. It is found that the accuracy of the multi-damage localization is significantly enhanced by optimizing the process of damage feature extraction and data fusion.

Automated summary

A novel robust multidamage localization method is proposed based on adaptive denoising and data fusion, which significantly enhances the accuracy of multi-damage localization by optimizing the process of damage feature extraction and data fusion.