An FBG Strain Sensor-Based NPW Method for Natural Gas Pipeline Leakage Detection
Natural gas pipeline leaks can lead to serious and dangerous accidents that can cause great losses of life and property. Therefore, detecting natural gas pipeline leaks has always been an important subject. The negative pressure wave (NPW) method is currently the most widely used leakage detection m...
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Published in | Mathematical problems in engineering Vol. 2021; pp. 1 - 8 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
New York
Hindawi
15.03.2021
John Wiley & Sons, Inc |
Subjects | |
Online Access | Get full text |
ISSN | 1024-123X 1026-7077 1563-5147 1563-5147 |
DOI | 10.1155/2021/5548503 |
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Summary: | Natural gas pipeline leaks can lead to serious and dangerous accidents that can cause great losses of life and property. Therefore, detecting natural gas pipeline leaks has always been an important subject. The negative pressure wave (NPW) method is currently the most widely used leakage detection method. Generally, this method uses pressure sensors to detect NPW signals to assess the leak and determine the location of the leakage point. However, the installation of a pressure sensor requires penetrating the pipeline structure, so the sensor intervals are often distant, leading to large signal attenuations and the ineffective detection of small leaks. An NPW method based on fiber Bragg grating (FBG) strain sensors is proposed in this paper which detects NPWs by monitoring the annular strain of the pipeline. Moreover, due to the advantages of nondestructive installation FBG strain sensors can be arranged closer along the distance of the pipeline, the attenuation of the NPW is small and the detection of leaks is improved. This method is tested through experiments and compared with a pressure sensor-based method; the experimental results verify that the proposed method is more effective in detecting natural gas pipeline leaks. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 1024-123X 1026-7077 1563-5147 1563-5147 |
DOI: | 10.1155/2021/5548503 |