Multi-helical Lamb Wave Imaging for Pipe-like Structures Based on a Probabilistic Reconstruction Approach

Wang, Zhe; Huang, Songling; Wang, Shen; Wang, Qing; Zhao, Wei

Abstract

The special form of pipe-like structure provides the helical route for ultrasonic guided wave. Considering the pipe as a flattened plate but with periodical replications, the helical wave becomes intuitional and a corresponding imaging algorithm can be constructed. This work proposes the multihelical Lamb wave imaging method by utilizing the multiple arrival wavepackets which are denoted as different orders. The helical wave signal model is presented and the constant group velocity point is illustrated. The probabilistic reconstruction algorithm is combined with the separation and fusion of different helical routes. To verify the proposed scheme, finite element simulations and corresponding experiments are conducted. The cases of single-defect simulation and two-defect simulation indicate the successful and robust implementation of the imaging algorithm. The test on actual pipe damage is also investigated to show its capability in imaging an irregular defect. The comparison with imaging results from only first arrival demonstrates the advantage of multihelical wave imaging, including the better imaging resolution and higher localization accuracy.

Citation

Wang, Z., Huang, S., Wang, S., Wang, Q., & Zhao, W. (2020). Multi-helical Lamb Wave Imaging for Pipe-like Structures Based on a Probabilistic Reconstruction Approach. IEEE Transactions on Instrumentation and Measurement, 70, Article 6002510. https://doi.org/10.1109/tim.2020.3038474

• Journal Article Type: Article
• Acceptance Date: Nov 3, 2020
• Online Publication Date: Nov 17, 2020
• Publication Date: 2020
• Deposit Date: Jan 4, 2021
• Publicly Available Date: Jan 6, 2021
• Journal: IEEE Transactions on Instrumentation and Measurement
• Print ISSN: 0018-9456
• Electronic ISSN: 1557-9662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 70
• Article Number: 6002510
• DOI: https://doi.org/10.1109/tim.2020.3038474
• Public URL: https://durham-repository.worktribe.com/output/1254572