Insulation provides an environment where corrosion can progress and remain undetected unless vigilantly monitored. Various approaches and inspection techniques are used to monitor the hidden losses. Current inspection methods often result in digital images – images that have improved corrosion under insulation (CUI) insights. Nevertheless, optimizing a CUI search remains challenging. Today’s digital world gives (almost) standardized inspection data more amenable to scrutiny than in the past. This paper examines piping CUI trends for thousands of piping circuits - after outlining steps followed during CUI surveys. The trends identified through data analytics are summarized through graphs evaluating multiple variables. Specifically, different insulation types and thicknesses, pipe diameters, and operating temperatures, among others, are compared. Finally, a predictive model to aid in selecting where to perform detailed inspections is outlined.

Subject
Circuits, Piping, Insulation, Glass, Operating temperatures, Inspection, Probability, Calcium silicate, Wool, Corrosion under insulation, Surveys, Normalizing, Damage
Keywords:
atmospheric corrosion, corrosion control, insulation, corrosion under insulation, CUI
© 2023 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).
2023
Association for Materials Protection and Performance (AMPP)
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