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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Bibliographic Details
Published in:Applied ocean research Vol. 155; p. 104438
Main Authors: Hosseinzadeh, Soheyl, Bahaari, Mohammadreza, Abyani, Mohsen, Taheri, Milad
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2025
Subjects:
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 Access:Get full text
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Summary: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 generated, representing various pipeline and corrosion defect specifications. Finite element modeling is performed using ABAQUS software and Python scripting to calculate the Failure Pressure and failure Maximum Von-Mises Stress (MVMS) under varying conditions of longitudinal spacing (Sl) and Internal Pressure (IP). This model generates a dataset that includes internal pressure, longitudinal spacing, and other relevant variables for the training and evaluation of ML models. Model performance is assessed through grid search and overfitting checks. A corrosion growth algorithm is incorporated to update input data dynamically, allowing for the prediction of future MVMS values and associated failure probabilities. The Probability of Failure (POF) is calculated, and Probability Density Functions (PDFs) for failure pressure are analyzed using standard distributions and Kolmogorov-Smirnov tests to identify the most accurate model. This approach provides a robust framework for predicting RUL by evaluating pipeline failures and probabilistic failure pressure over time, contributing valuable insights into the reliability and safety of pipeline systems under various conditions and time intervals.
ISSN:0141-1187
DOI:10.1016/j.apor.2025.104438