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Improving Cathodic Protection Pipeline Integrity Monitoring Data in the Time of IIoT and Big Data

Picture for Improving Cathodic Protection Pipeline Integrity Monitoring Data in the Time of IIoT and Big Data
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Product Number: 51321-16259-SG
Author: Tony da Costa; Matt Barrett
Publication Date: 2021
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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.

Key words: Big data, data analytics, machine learning, cathodic protection, remote monitoring, pipeline integrity, dark data, intelligent algorithms, data overload, predictive failure, alarm optimization, seasonality, remote limits.

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.

Key words: Big data, data analytics, machine learning, cathodic protection, remote monitoring, pipeline integrity, dark data, intelligent algorithms, data overload, predictive failure, alarm optimization, seasonality, remote limits.

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