Data-driven remaining useful life estimation of subsea pipelines under effect of interacting corrosion defects

This research presents a method for analyzing the Remaining Useful Life (RUL) of pipelines impacted by corrosion defects through the integration of Latin Hypercube Sampling (LHS), Finite Element Analysis (FEA), and Machine Learning (ML). A dataset consisting of 200 samples and 8 random variables is...

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Veröffentlicht in:	Applied ocean research Jg. 155; S. 104438
Hauptverfasser:	Hosseinzadeh, Soheyl, Bahaari, Mohammadreza, Abyani, Mohsen, Taheri, Milad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2025
Schlagworte:	Latin hypercube sampling Machine learning Offshore pipeline engineering Pipeline integrity management Random sampling Remaining useful life Structural reliability ECDF ANN DTR Random sampling CDF LR Pipeline integrity management ILI Structural reliability GPR KNN SVM Remaining useful life K-S MLP Latin hypercube sampling PDF LHS MAOP ML FEA MIC POF IP AI RUL MFL-TFI Offshore pipeline engineering MVMS PSO FEM PCA SGD SCC Machine learning PIMS TBRA MTTF
ISSN:	0141-1187
Online-Zugang:	Volltext
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