Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines

被引：0
|
作者：
Lee, Sun-Ho [1 ]
Park, Choon-Su [1 ]
Yoon, Dong-Jin [1 ]
机构：
[1] Korea Research Institute of Standards and Science, Daejeon, Korea, Republic of
来源：
e-Journal of Nondestructive Testing | 2024年 / 29卷 / 07期
关键词：
Recent increases in buried pipeline damage accidents due to third-party interference have significantly heightened attention towards buried pipeline monitoring. Especially, as the sudden damage can lead to large-scale leakage, there is a necessity for early response and maintenance. However, the application of a structural health monitoring approach is difficult, since the extensive network of buried pipelines, stretching over thousands of kilometres, exhibits diverse noise environments and propagation characteristics. In this study, introduces a deep learning-based pipeline damage monitoring algorithm, specifically designed to early detection of accidents caused by third-party interference. This algorithm integrates a CNN-based model, advanced signal processing for data pre-processing, and TDoA-based source localization. The training and test data set are the acquisition under completely independent conditions, which has been experimentally validate for applicability across various environments for buried pipelines. Moreover, both the training and test dataset acquisition were performed using accelerometers on in-service buried pipelines, each with diameters of 1,100 mm, 1,200 mm, and 2,200 mm, extending over lengths ranging from approximately 200 to 500 meters. Despite the independent conditions of the test datasets, our study yielded over 95% accuracy in early detection, with the results being in good agreement with the actual excavate locations. © 2024, NDT. net GmbH and Co. KG. All rights reserved;
D O I：
10.58286/29876
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学科分类号：
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