AMPP Paper 16259-2021: Improving Cathodic Protection Monitoring Data in the Time of IIoT and Big Data

�� FREE PREVIEW (First 5 Pages) ✔ First 5 pages free to download · No email required · Full document available after purchase This paper explores the advances in remote monitoring technology and the enhancements that can be made to ensure that pipeline integrity is maximized while operational efficiencies are optimized. Specifically focusing on the data that is generated by cathodic protection and pipeline integrity monitoring devices (e.g. rectifier monitoring), this paper explores how data analytics techniques, such as artificial intelligence and machine learning algorithms, can shine a light on historically ‘dark’ data, improving pipeline integrity operations and the safety of workers and the broader public. Examples of artificial intelligence and machine learning work, such as applying intelligent algorithms to data analysis streams, will be presented as means of reducing data overload while providing automated predictive failure analysis and optimization of cathodic protection systems. The paper also addresses the challenges of alarm fatigue and proposes solutions using data analytics to isolate the effect of seasonality and improve alarm thresholds. It details experimental procedures involving impressed current cathodic protection (ICPP) rectifiers and remote monitoring units (RMUs).