Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions

Journal Article · Tue Sep 01 00:00:00 UTC 2020 · Optics Express

DOI: https://doi.org/10.1364/OE.397509 · OSTI ID:1657278

Peng, Zhaoqiang; Jian, Jianan; Wen, Hongqiao; Gribok, Andrei; Wang, Mohan ; Liu, Hu; Huang, Sheng ; Mao, Zhi-Hong; Chen, Kevin P.

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

• Sponsoring Organization: USDOE Office of Fossil Energy and Carbon Management (FECM); USDOE Office of Nuclear Energy (NE)
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 1657278
• Journal Information: Optics Express, Journal Name: Optics Express Journal Issue: 19 Vol. 28; ISSN 1094-4087; ISSN OPEXFF
• Publisher: Optical Society of AmericaCopyright Statement
• Country of Publication: United States
• Language: English