Leakage Detection on Pipelines using IIoT and Edge Computing: A Systematic Literature Review Using PRISMA

Abstract

Pipelines are principal components for storing and transporting petroleum products be it oil or gas. Automated monitoring and control systems for oil and gas operations that are operative, secure, inexpensive, and transparent are attainable through Edge computing and the Industrial Internet of Things. Pipelines need to span several kilometers to transport these petroleum products and some of such areas might not have internet coverage. Additionally, most Industrial Internet of Things systems are developed based on pressure readings obtained from the sensors mounted on pipelines. This study presents a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify the various pipeline leakage detection techniques in use. This model helps to improve the reporting of systematic reviews and aids the critical appraisal of published systematic reviews. The study also investigated what other data can be measured, using Industrial Internet of Things, that would indicate leakage in the pipeline. With the initial collection of 41 articles from prestigious databases such as Springer, Multidisciplinary Digital Publishing Institute, Sensors, and Science Direct, a final number of 16 articles were obtained after following the principles laid down in PRISMA. Amongst the publications by countries, China emerged the top publishing country followed by Iran. The parameters most frequently measured for the detection of leakage are pressure and other parameters are temperature, acoustic signals and vibrations around the pipeline. The results indicate that Negative Pressure Wave is one of the most promising methods for pipeline leakage detection.