This paper presents a pipeline monitoring system designed to detect, locate, and classify leakages and Third-Party Interference (TPI) incidents along a pipeline right-of-way using Vibroacoustic Technology. The system is ideal for retrofitting existing pipelines and focuses on real-time asset monitoring to detect activities such as excavation or impacts that could cause damage.

Vibroacoustic waves generated by anomalies, i.e., TPIs or leakages, propagate for several kilometers inside the pipeline. Strategically placed pressure sensors and accelerometers along the pipeline capture the data, that are then transmitted to computational units with advanced algorithms for noise reduction, event detection, and localization.

The system employs machine learning and deep learning techniques, including convolutional neural networks, to classify TPI events, enhancing detection accuracy and minimizing false alarms. This innovative AI-driven approach offers a robust solution for maintaining pipeline integrity with potential to become a key tool in pipeline asset protection.