期刊
SENSORS
卷 23, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/s23125410
关键词
distributed optical fiber sensing system; defect detection; pipelines; physics-informed datasets; simulations; welding detection; sensing system; data classification performance and noise robustness
This study presents a framework for detecting mechanical damage in pipelines by generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The framework transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification. The investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system.
This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据