Adaptive Pipeline Monitoring Using Unsupervised Anomaly Detection

Abstract

Infrastructure The pipeline infrastructure plays a major role in the transportation of fluids in the industry safely and efficiently. Conventional monitoring techniques are in many cases based on fixed thresholds or monitored models which have the issue of being restricted by the requirement of labeled failure information as well as they are not easily able to adjust to changing operational environments. The paper discusses an adaptive pipeline monitoring scheme based on the unsupervised anomaly detection algorithm and applies the technique to multi-sensor data streams in order to detect the deviation of the normal operating schemes without prior information of fault events. The framework suggested involves adaptive learning, which will enable the framework to adapt to the changing pipeline dynamics and minimize false alarms to achieve real-time detection and strong operational insights. The metrics of evaluation prove the efficiency of the method in detecting the development of bypass on both the subtle and abrupt levels providing a proactive approach to integrity of the pipeline management and risk prevention.