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Article

Predictive Maintenance Framework for Cathodic Protection Systems Using Data Analytics

1
Postgraduate School of Engineering Management, University of Johannesburg, Auckland Park 2006, South Africa
2
Institute for Intelligent Systems, University of Johannesburg, Auckland Park 2006, South Africa
*
Author to whom correspondence should be addressed.
Academic Editors: Mauro D’Arco and Francesco Bonavolontà
Energies 2021, 14(18), 5805; https://doi.org/10.3390/en14185805
Received: 6 July 2021 / Revised: 12 August 2021 / Accepted: 20 August 2021 / Published: 14 September 2021
(This article belongs to the Special Issue Measurement Applications in Industry 4.0)
In the quest to achieve sustainable pipeline operations and improve pipeline safety, effective corrosion control and improved maintenance paradigms are required. For underground pipelines, external corrosion prevention mechanisms include either a pipeline coating or impressed current cathodic protection (ICCP). For extensive pipeline networks, time-based preventative maintenance of ICCP units can degrade the CP system’s integrity between maintenance intervals since it can result in an undetected loss of CP (forced corrosion) or excessive supply of CP (pipeline wrapping disbondment). A conformance evaluation determines the CP system effectiveness to the CP pipe potentials criteria in the NACE SP0169-2013 CP standard for steel pipelines (as per intervals specified in the 49 CFR Part 192 statute). This paper presents a predictive maintenance framework based on the core function of the ICCP system (i.e., regulating the CP pipe potential according to the NACE SP0169-2013 operating window). The framework includes modeling and predicting the ICCP unit and the downstream test post (TP) state using historical CP data and machine learning techniques (regression and classification). The results are discussed for ICCP units operating either at steady state or with stray currents. This paper also presents a method to estimate the downstream TP’s CP pipe potential based on the multiple linear regression coefficients for the supplying ICCP unit. A maintenance matrix is presented to remedy the defined ICCP unit states, and the maintenance time suggestion is evaluated using survival analysis, cycle times, and time-series trend analysis. View Full-Text
Keywords: cathodic protection; corrosion monitoring; data analysis; machine learning algorithms; pipelines; predictive maintenance; predictive models cathodic protection; corrosion monitoring; data analysis; machine learning algorithms; pipelines; predictive maintenance; predictive models
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MDPI and ACS Style

Rossouw, E.; Doorsamy, W. Predictive Maintenance Framework for Cathodic Protection Systems Using Data Analytics. Energies 2021, 14, 5805. https://doi.org/10.3390/en14185805

AMA Style

Rossouw E, Doorsamy W. Predictive Maintenance Framework for Cathodic Protection Systems Using Data Analytics. Energies. 2021; 14(18):5805. https://doi.org/10.3390/en14185805

Chicago/Turabian Style

Rossouw, Estelle, and Wesley Doorsamy. 2021. "Predictive Maintenance Framework for Cathodic Protection Systems Using Data Analytics" Energies 14, no. 18: 5805. https://doi.org/10.3390/en14185805

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