REAL-TIME PIPELINE INTEGRITY MONITORING AND PREDICTIVE MAINTENANCE SCHEDULING: A HYBRID MACHINE LEARNING APPROACH

• Dr. Olivia J. Campbell

  Department of Industrial Engineering, University of Auckland, New Zealand

  Author

• Dr. Karim A. Nasser

  Department of Mechanical and Industrial Engineering, American University of Beirut, Lebanon

  Author

Pipeline networks are essential for resource transportation but are highly susceptible to anomalies like leaks, intrusions, and equipment failures, leading to significant environmental, economic, and safety risks. Traditional monitoring methods often suffer from delayed detection and high operational costs. This article proposes a novel machine learning framework for enhancing pipeline integrity management by combining real-time anomaly detection with predictive maintenance forecasting. The framework leverages a hybrid approach: the Random Forest algorithm is employed for immediate and accurate identification of anomalies from multi-sensor data, while the Prophet model is utilized for robust time-series forecasting of future maintenance requirements. Through extensive data preprocessing and an integrated architecture, the system is designed to provide rapid alerts for critical events and enable proactive maintenance scheduling. Expected results indicate high anomaly detection accuracy (over 95%) with low false positives, and precise maintenance forecasts (MAE < 7 days). This data-driven approach aims to improve pipeline security, minimize environmental damage, and optimize operational costs by transitioning from reactive to proactive intervention strategies.

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