A transfer-learning approach for corrosion prediction in pipeline infrastructures

Pipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due to potential leakages) and the human beings (due to accidents that may cause explosions in presence of gas leakages). Fo...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2022-05, Vol.52 (7), p.7622-7637
Main Authors: Canonaco, Giuseppe, Roveri, Manuel, Alippi, Cesare, Podenzani, Fabrizio, Bennardo, Antonio, Conti, Marco, Mancini, Nicola
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Corrosion
Corrosion tests
Explosions
Gas pipelines
Machine learning
Machines
Manufacturing
Mechanical Engineering
Pipelines
Prediction models
Processes
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Summary:Pipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due to potential leakages) and the human beings (due to accidents that may cause explosions in presence of gas leakages). For this reason, predictive mechanisms are needed to detect and address the corrosion phenomenon. Recently, we have seen a first attempt at leveraging Machine Learning (ML) techniques in this field thanks to their high ability in modeling highly complex phenomena. In order to rely on these techniques, we need a set of data, representing factors influencing the corrosion in a given pipeline, together with their related supervised information, measuring the corrosion level along the considered infrastructure profile. Unfortunately, it is not always possible to access supervised information for a given pipeline since measuring the corrosion is a costly and time-consuming operation. In this paper, we will address the problem of devising a ML-based predictive model for internal corrosion under the assumption that supervised information is unavailable for the pipeline of interest, while it is available for some other pipelines that can be leveraged through Transfer Learning (TL) to build the predictive model itself. We will cover all the methodological steps from data set creation to the usage of TL. The whole methodology will be experimentally validated on a set of real-world pipelines.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02771-y