Advanced sensor systems and machine learning for pipeline integrity management: a review of corrosion monitoring and prediction strategies

Abstract

The integrity of long-distance mild steel oil and gas pipelines is critically threatened by electrochemical corrosion, necessitating advanced integrity management strategies. This review analyzes the transition from periodic inspection to continuous, predictive monitoring, critically comparing cornerstone In-Line Inspection technologies like Magnetic Flux Leakage and Ultrasonic Testing against emerging long-range systems. We identify a significant “big data” bottleneck created by the extreme volume and complexity of sensor data, which impedes manual analysis. To resolve this, we propose a machine learning-driven integration framework. By leveraging deep learning for defect diagnosis and hybrid finite element analysis-neural networks for prognosis, we demonstrate how multimodal data fusion can enhance detection accuracy and reduce false positives. This work outlines a strategic pathway for implementing digital twin architectures to extend asset life and optimize maintenance schedules.