| Autor: |
Xiaohui Lin, Gang Li, Yilin Wang, Kehong Zeng, Wenming Yang, Fuyong Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Pipeline Science and Engineering, Vol 4, Iss 4, Pp 100184- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2667-1433 |
| DOI: |
10.1016/j.jpse.2024.100184 |
| Popis: |
Based on the principles and characteristics of distributed fiber optic monitoring technology, this paper introduces the current research progress in identifying fiber optic vibration signals in oil and gas pipelines and summarizes their applications. Fiber optic vibration signal recognition is classified into traditional and intelligent methods. Traditional recognition relies on feature extraction, analyzing intrusion signals in the time, frequency, and time-frequency domains, and employing thresholding for detection. In contrast, intelligent recognition employs big data and artificial intelligence techniques, training on intrusion signal samples to build fiber optic signal analysis models for event classification and threat level assessment over time. The intelligent method, renowned for its high accuracy and adaptability, has emerged as a focal point of research compared to traditional methods. This paper meticulously examines the limitations of intelligent fiber optic vibration signal identification in pipelines and outlines the trajectory of intelligent signal recognition technology. Accelerating the deployment of distributed optical fiber monitoring technology in oil and gas pipelines and enhancing pipeline intelligent monitoring are crucial objectives. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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