Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks

Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on th...

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Bibliographic Details
Main Authors: Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan, Nawal Aswan B. Abdul Jalil
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-03-01
Series:Journal of Pipeline Science and Engineering
Subjects:
Pipeline structural health monitoring (SHM)
Fault detection
Artificial neural networks (ANNS)
Frequency response function (FRF)
Principal component analysis (PCA)
Bolt looseness
Online Access:http://www.sciencedirect.com/science/article/pii/S2667143324000507
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