Data-Driven Fault Detection for Industrial Processes Canonical Corre
Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the m
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Data-Driven Fault Detection for Industrial Processes Canonical Correlation Analysis and Projection Based Methods
Data-Driven Fault Detection for Industrial Processes
Zhiwen Chen
Data-Driven Fault Detection for Industrial Processes Canonical Correlation Analysis and Projection Based Methods
Zhiwen Chen Duisburg, Germany Dissertation, University of Duisburg-Essen, 2016
ISBN 978-3-658-16755-4 ISBN 978-3-658-16756-1 (eBook) DOI 10.1007/978-3-658-16756-1 Library of Congress Control Number: 2016961279 Springer Vieweg © Springer Fachmedien Wiesbaden GmbH 2017 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. Printed on acid-free paper This Springer Vieweg imprint is published by Springer Nature The registered company is Springer Fachmedien Wiesbaden GmbH The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany
To my parents, my love Caiwen and my son
Preface In order to maximize the customer satisfaction and profit as well as to obey government regulations, the complexity and automation degree of modern industrial processes are significantly growing. To ensure the safety and overall reliability of such complicated processes, automatized fault detection is of great importance. Although the model-based fault detection theory has been well studied in the past decades, its applications are still limited for large-scale industrial processes because it is difficult to establish accurate model by means of first principles. On the other hand, sufficient (real-time) data are collected and recorded during process operations, and high-speed computation is available due to the rapid improvement in sensor and computer technologies. Therefore, the main objective of this work is to develop advanced data-driven fault detection methods for different application scopes. This work is firstly dedicated to evaluate basic fault detection statistics w
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