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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-03-01
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| Series: | Journal of Pipeline Science and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667143324000507 |
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