Autonomous Inspection System for Anomaly Detection in Natural Gas Pipelines

Abstract

Abstract Oil and gas industry operates according to stringent standards that primarily strive to keep the pipeline transportation and other related operations efficiently while maintaining and practicing high standards of industrial safety. Due to which, the routine inspection and maintenance of underground/overground structures became a challenging process. Over the years, performance based integrity management programmes became popular and it have been implemented for managing the safety and environmental risks associated with hazardous oil or gas pipelines. Pipeline safety and integrity team must verify, repair and certify the integrity of pipelines that could leak or failure in the future. Internal inspection using pipeline inspection gauges (PIG) are considered expensive and the need for surface preparation procedures makes the inspection operations time consuming. Whereas, external survey operations are carried out by highly trained and certified inspection/survey team where they manually identify pipeline leakages or any visible damages. External manual inspection is conducted using various handheld devices and recorded data is later analyzed to identify and locate the possible anomalies. Relatively high surrounding temperatures and uneven terrain makes the manual survey a mundane mission. The shortcomings and the challenges of the conventional external inspection and survey mechanisms are tackled by introducing a mobile robotic platform equipped with non-invasive magnetometric diagnosis (NIMD) system which allows non-contact anomaly detection from a distance of up to 2-3m from the underground pipeline metal core without unearthing or any surface treatments such as coating removal that are performed in the conventional techniques. The proposed autonomous pipeline inspection system has various sensors that can precisely locate, navigate and detect the underground pipeline faults. A novel hierarchical controller which was developed in our previous studies was utilized and employed for the navigation of vehicle over the buried infrastructures. The effectiveness and the performance of autonomous system was tested on real field conditions and the true ability of the system was showcased to detect the underground pipeline anomalies even in extreme environmental conditions.