Predictive Maintenance in Dynamic Systems Advanced Methods, Decision
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction an
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redictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications
Predictive Maintenance in Dynamic Systems
Edwin Lughofer • Moamar Sayed-Mouchaweh Editors
Predictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications
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Editors Edwin Lughofer Fuzzy Logic Laboratorium Linz-Hagenberg Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz, Austria
Moamar Sayed-Mouchaweh Institute Mines-Telecom Lille Douai Douai, France
ISBN 978-3-030-05644-5 ISBN 978-3-030-05645-2 (eBook) https://doi.org/10.1007/978-3-030-05645-2 Library of Congress Control Number: 2019931901 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
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Preface
During recent years, rapid technological developments and breakthroughs in various industrial automatization processes with the support of modern machines, big data storages, and clouds of computers operating in parallel led to a significant increase in system complexity and dynamic processes. This makes a manual supervision and maintenance of machines, system components, and production chains more and more unaffordable and thus unrealistic to be conducted in a reasonable amount of time with reasonable efforts and costs for companies. Therefore, automated predictive maintenance (APdM) has more and more become a central cornerstone in today’s industrial applications and systems ranging from online manufacturing rails and production lines through (cyber) security problems and infrastructure management to energy fabrication, maritime systems, and exploitation facilities. This is because
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