Direct Assessment (DA) and In-Line Inspection (ILI) methods have served operators well for decades, yet published incident statistics consistently report corrosion as one of the main causes of pipeline failure. It is therefore clear that we must continually strive to improve our monitoring methods, as well as our decision making processes in relation to corrosion damage. As the pipeline industry continues its digital transformation, we should acknowledge the potential role of digital technologies for this purpose. In particular, we should look to predictive analytics techniques as a means to monitor the condition of corroded assets.

This paper explores the use of Bayesian networks for data-driven inspection planning in pipelines. As a simple example, we outline the development and validation of a prototype network for inspection prioritization of pipelines at a perceived risk of external corrosion. Despite its simplicity, the prototype achieves a high performance, and with further development it is anticipated that the network could be employed for even more sophisticated integrity management decisions.

Subject
Coating inspection, Materials, Piping, Trends, Structures, Pipelines, Inspection, Fusion bonded epoxy coating, Probability, Anomalies, Coating types, External corrosion, Training
Keywords:
data, analytics, Bayesian networks, machine learning, in-line inspection, external corrosion
© 2020 Association for Materials Protection and Performance (AMPP). All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise) without the prior written permission of AMPP. Positions and opinions advanced in this work are those of the author(s) and not necessarily those of AMPP. Responsibility for the content of the work lies solely with the author(s).
2020
Association for Materials Protection and Performance (AMPP)
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