{"title":"基于超声导波传感器阵列和GSA-CoSaMP算法的管道缺陷评估方法","authors":"Zhirong Lin;Yishou Wang;Linlin Fang;Xiaodie Hu;Xinlin Qing","doi":"10.1109/TIM.2025.3609325","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pipeline Defect Assessment Method Based on Ultrasonic Guided Wave Sensor Array and GSA-CoSaMP Algorithm\",\"authors\":\"Zhirong Lin;Yishou Wang;Linlin Fang;Xiaodie Hu;Xinlin Qing\",\"doi\":\"10.1109/TIM.2025.3609325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-13\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11162613/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11162613/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.