Fault Diagnosis of Hybrid Dynamic and Complex Systems

Fault Diagnosis of Hybrid Dynamic and Complex Systems

Moamar Sayed-Mouchaweh Editor Fault Diagnosis of Hybrid Dynamic and Complex Systems 123

Editor Moamar Sayed-Mouchaweh Institute Mines-Telecom Lille Douai Douai, France ISBN 978-3-319-74013-3 ISBN 978-3-319-74014-0 (ebook) https://doi.org/10.1007/978-3-319-74014-0 Library of Congress Control Number: 2018933452 Springer International Publishing AG, part of Springer Nature 2018 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 imprint is published by the registered company Springer International Publishing AG part of Springer Nature. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economic losses caused by unexpected stops in production lines. Therefore, early detection and isolation of faults is the key to maintaining system performance, ensuring system safety, and increasing system life. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, model-based diagnostic approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. In addition, in HDS, two types of faults may occur: parametric and discrete faults. Parametric faults occur as abnormal changes in the value of parameters describing the continuous dynamics, while discrete faults are defined as unexpected, abnormal, changes in the system discrete mode. A key challenge of fault diagnosis of HDS is related to the state estimation and tracking because of the cohabitation of continuous and discrete dynamics. Therefore, the fault diagnosis requires distinguishing between healthy and faulty states during mode changes for all hybrid trajectories generated by the system. However, tracking all the possible trajectories of a hybrid system is computationally intractable, in particular in the presence of faults. This is due to multiple reasons. Firstly, faults cause unknown changes in the system model. Thus, it becomes challenging to differentiate the change in behavior due to a fault from change in behavior caused by a normal mode transition. Secondly, pre-enumerating all the operation modes of a system is computationally intractable, in particular in the presence of faults. Indeed computing the reachable set of states of HDS is an undecidable problem due to the infinite state space of continuous systems. Another challenge is related to the robustness of fault diagnosis and its time processing to issue the decision (fault detection and isolation). Indeed, the diagnosis engine must be able to manage out of order alarms and handle uncertainties and issue the diagnosis decision fast enough in order to give ample time to v

vi Preface human operators of supervision to implement corrective and maintenance actions. Finally, the diagnosis engine (inference) must scale well to large systems with multiple discrete modes. Indeed, a global model representing both the discrete and continuous dynamics can be too huge to be physically constructed for systems with large number of discrete modes. This edited Springer book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains (inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc.). These approaches cover the different aspects of performing single/multiple online/offline parametric/discrete abrupt/tear and wear fault diagnosis in incremental/nonincremental manner, using different modeling tools (hybrid automata, hybrid Petri nets, hybrid bond graphs, extended Kalman filter etc.) for different classes of hybrid dynamic and complex systems. Finally, the editor is very grateful to all authors and reviewers for their very valuable contribution allowing setting another corner stone in the research and publication history of fault diagnosis of hybrid dynamic and complex systems. I would like also to acknowledge Mrs. Mary E. James for establishing the contract with Springer and supporting the editor in any organizational aspects. I hope that this volume will be a useful basis for further fruitful investigations and fresh ideas for researcher and engineers as well as a motivation and inspiration for newcomers to address the problems related to this very important and promising field of research. Douai, France Moamar Sayed-Mouchaweh

Contents 1 Prologue... 1 Moamar Sayed-Mouchaweh 2 Motor Fault Detection and Diagnosis Based on a Meta-cognitive Random Vector Functional Link Network... 15 Choiru Za in, Mahardhika Pratama, Mukesh Prasad, Deepak Puthal, Chee Peng Lim, and Manjeevan Seera 3 Optimal Adaptive Threshold and Mode Fault Detection for Model-Based Fault Diagnosis of Hybrid Dynamical Systems... 45 Om Prakash, A. K. Samantaray, and R. Bhattacharyya 4 Diagnosing Hybrid Dynamical Systems Using Max-Plus Algebraic Methods... 79 Gregory Provan 5 Monitoring of Hybrid Dynamic Systems: Application to Chemical Process... 101 Nelly Olivier-Maget and Gilles Hetreux 6 Hybrid Bond-Graph Possible Conflicts for Hybrid Systems Fault Diagnosis... 123 Carlos J. Alonso-González, Belarmino Pulido, and Anibal Bregon 7 Hybrid System Model Based Fault Diagnosis of Automotive Engines... 153 E. P. Nadeer, S. Mukhopadhyay, and A. Patra 8 Diagnosis of Hybrid Systems Using Structural Model Decomposition... 179 Matthew J. Daigle, Anibal Bregon, and Indranil Roychoudhury vii

viii Contents 9 Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover... 209 Quentin Gaudel, Elodie Chanthery, Pauline Ribot, and Matthew J. Daigle 10 Diagnosis of Hybrid Dynamic Systems Based on the Behavior Automaton Abstraction... 243 Ramon Sarrate, Vicenç Puig, and Louise Travé-Massuyès Index... 279