Pipeline Defect Assessment Method Based on Ultrasonic Guided Wave Sensor Array and GSA-CoSaMP Algorithm

Accurate characterization of pipeline defects is crucial for maintaining structural integrity and ensuring operational safety. This study introduces an innovative pipeline defect evaluation method integrating the gravitational search algorithm (GSA) with the compressed sampling matching pursuit (CoSaMP), aimed at improving the accuracy and robustness of ultrasonic guided wave (UGW) signal decomposition and reconstruction. GSA is applied to dynamically optimize signal sparsity, overcoming the limitations of traditional methods that rely on predefined sparsity levels. Moreover, an optimized waveform dictionary, which incorporates prior knowledge of guided wave reflection characteristics, is constructed to improve the accuracy of defect signal decomposition and reconstruction. The proposed method effectively separates overlapping reflection signals from the front and rear edges of pipeline defects, enabling precise characterization of defect axial dimensions. Finite element (FE) simulations and experimental validations using a piezoelectric (PZT) sensor array installed on the surface of a stainless steel pipeline illustrate the enhanced effectiveness of the proposed methodology, achieving average defect size evaluation errors of 0.68 and 2.20 mm, respectively, significantly outperforming conventional matching pursuit (MP), standard CoSaMP, orthogonal matching pursuit (OMP), and basis pursuit (BP) algorithms. This method addresses the limitations of existing approaches by adaptively optimizing signal sparsity, enhancing robustness against noise, and providing a reliable tool for pipeline integrity assessment. The findings contribute to the development of predictive maintenance strategies and advance real-time defect monitoring applications for complex pipeline networks.