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Integrating the root cause analysis to machine learning interpretation for predicting future failure.

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Summary
This summary is machine-generated.

This study introduces an AI model for offshore pipeline corrosion assessment, improving risk evaluation and mitigation strategies. It identifies CO2 corrosion and categorizes risks to enhance pipeline longevity and safety.

Keywords:
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Area of Science:

  • Materials Science and Engineering
  • Corrosion Science
  • Artificial Intelligence in Engineering

Background:

  • Existing offshore pipeline inspection methods lack efficient data utilization for predicting loss and mitigation.
  • Root cause analysis data is underutilized in forecasting potential pipeline failures.
  • There is a need for advanced methods to assess and mitigate pipeline corrosion risks.

Purpose of the Study:

  • To develop a novel Artificial Intelligence (AI) model for evaluating offshore pipeline corrosion.
  • To enhance the prediction of potential loss and corrosion mitigation using failure analysis knowledge.
  • To establish a feasible and actual inspection method by combining experimental and modeling approaches.

Main Methods:

  • Utilized elemental composition, hardness, and tensile tests to analyze metallic properties and corrosion products.
  • Employed Scanning Electron Microscopy with Energy Dispersive X-Ray (SEM-EDX) and X-Ray Diffractometry (XRD) to study corrosion mechanisms.
  • Implemented the Gaussian Mixture Model (GMM) with Pearson Multicollinear Matrix for risk assessment and damage prediction.

Main Results:

  • Identified wide and shallow pit corrosion and channelling as evident damage mechanisms.
  • Confirmed the material as API 5L X42 PSL 1 standard through tensile and hardness tests.
  • Determined CO2 corrosion as the primary cause of corrosion products using SEM-EDX and XRD analysis.
  • GMM analysis revealed three distinct risk levels: low, medium, and high, validated by silhouette scores and Bayesian information criterion.

Conclusions:

  • The AI-driven model effectively assesses offshore pipeline risks and predicts damage mechanisms.
  • Recommended mitigation strategies include chemical injection (parasol, biocide) and pigging for CO2 corrosion.
  • The study provides a guideline for risk-based inspection and clustering of offshore pipeline integrity.