TY - GEN
T1 - Pipeline Degradation Evaluation Based on Distributed Fiber Sensors and Convolutional Neural Networks (CNNs)
AU - Wu, Zekun
AU - Wang, Qirui
AU - Gribokb, Andrei V.
AU - Chen, Kevin P.
N1 - Funding Information:
This work was performed in support of Department of Energy grants DE-NE0008994 and the National Energy TechnologyLaboratory’songoingresearchundertheRSScontract89243318CFE000003.
Funding Information:
This work was performed in support of Department of Energy grants DE-NE0008994 and the National Energy Technology Laboratory's ongoing research under the RSS contract 89243318CFE000003.
Publisher Copyright:
© 2022 The Author(s).
PY - 2022
Y1 - 2022
N2 - We present a machine learning method to analyze data harnessed by distributed fiber sensors for pipeline monitoring. Convolutional neural networks are used to identify and classify pipeline internal defects with 99% and 94% accuracy, respectively.
AB - We present a machine learning method to analyze data harnessed by distributed fiber sensors for pipeline monitoring. Convolutional neural networks are used to identify and classify pipeline internal defects with 99% and 94% accuracy, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85146690784&partnerID=8YFLogxK
U2 - 10.1364/OFS.2022.W4.41
DO - 10.1364/OFS.2022.W4.41
M3 - Conference contribution
AN - SCOPUS:85146690784
T3 - Optics InfoBase Conference Papers
BT - Optical Fiber Sensors, OFS 2022
PB - Optica Publishing Group (formerly OSA)
T2 - 27th International Conference on Optical Fiber Sensors, OFS 2022
Y2 - 29 August 2022 through 2 September 2022
ER -