Model-based Health Monitoring of Hybrid Systems

This book systematically presents a comprehensive framework and effective techniques for in-depth analysis, clear design procedure, and efficient implementation of diagnosis and prognosis algorithms for hybrid systems. It offers an overview of the fundamentals of diagnosis\prognosis and hybrid bond graph modeling. This book also describes hybrid bond graph-based quantitative fault detection, isolation and estimation. Moreover, it also presents strategies to track the system mode and predict the remaining useful life under multiple fault condition. A real world complex hybrid systema vehicle steering control systemis studied using the developed fault diagnosis methods to show practical significance. Readers of this book will benefit from easy-to-understand fundamentals of bond graph models, concepts of health monitoring, fault diagnosis and failure prognosis, as well as hybrid systems. The reader will gain knowledge of fault detection and isolation in complex systems including those with hybrid nature, and will learn state-of-the-art developments in theory and technologies of fault diagnosis and failure prognosis for complex systems.

[1]  Xinbo Huang,et al.  Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[2]  Mitsuo Gen,et al.  Genetic Algorithms & Engineering Optimization , 2000 .

[3]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[4]  P. Le Moigne,et al.  Energy gauge for lead-acid batteries in electric vehicles , 2000 .

[5]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Hassan Hammouri,et al.  Observer-based approach to fault detection and isolation for nonlinear systems , 1999, IEEE Trans. Autom. Control..

[7]  Andrew Ball,et al.  The development of an adaptive threshold for model-based fault detection of a nonlinear electro-hydraulic system , 2005 .

[8]  Belkacem Ould Bouamama,et al.  Robust Monitoring of an Electric Vehicle With Structured and Unstructured Uncertainties , 2009, IEEE Transactions on Vehicular Technology.

[9]  Hong Chen,et al.  A multi-objective control design for active suspensions with hard constraints , 2003, Proceedings of the 2003 American Control Conference, 2003..

[10]  C H Lo,et al.  Model-based fault diagnosis in continuous dynamic systems. , 2004, ISA transactions.

[11]  Chih-Chung Wang,et al.  Rotating machine fault detection based on HOS and artificial neural networks , 2002, J. Intell. Manuf..

[12]  H. Kaebernick,et al.  Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks , 2007 .

[13]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Dave Sept Scientific Computing: An Introductory Survey. By Michael T. Health. WCB/McGraw-Hill, 1997. 448 pp. Softcover, ISBN 0–07–027684–6. (A Solutions Manual is also available.) , 2000 .

[15]  Dan T. Horak Failure detection in dynamic systems with modeling errors , 1988 .

[16]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[17]  A. B. Rad,et al.  Intelligent system for process supervision and fault diagnosis in dynamic physical systems , 2006, IEEE Transactions on Industrial Electronics.

[18]  Qi Wu,et al.  Car assembly line fault diagnosis based on robust wavelet SVC and PSO , 2010, Expert Syst. Appl..

[19]  Nagi Gebraeel,et al.  A Neural Network Degradation Model for Computing and Updating Residual Life Distributions , 2008, IEEE Transactions on Automation Science and Engineering.

[20]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

[21]  C H Lo,et al.  Fusion of qualitative bond graph and genetic algorithms: a fault diagnosis application. , 2002, ISA transactions.

[22]  Danwei Wang,et al.  Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation , 2010, IEEE Transactions on Automation Science and Engineering.

[23]  K. Frohlich,et al.  Model-aided diagnosis: an inexpensive combination of model-based and case-based condition assessment , 2001 .

[24]  Junyan Wang,et al.  Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization , 2011, ICSI.

[25]  Furong Gao,et al.  Statistical Monitoring and Fault Diagnosis of Batch Processes Using Two-Dimensional Dynamic Information , 2010 .

[26]  T. G. Habetler,et al.  Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics , 2002 .

[27]  Michael J. Roemer,et al.  Predicting remaining life by fusing the physics of failure modeling with diagnostics , 2004 .

[28]  H. G. Natke,et al.  A PASSIVE DIAGNOSTIC EXPERIMENT WITH ERGODIC PROPERTIES , 1997 .

[29]  R. Rosenberg,et al.  System Dynamics: Modeling and Simulation of Mechatronic Systems , 2006 .

[30]  Jie Chen,et al.  Design of unknown input observers and robust fault detection filters , 1996 .

[31]  Danwei Wang,et al.  Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives , 2010, IEEE Transactions on Automation Science and Engineering.

[32]  Angelo Alessandri,et al.  Fault detection of actuator faults in unmanned underwater vehicles , 1999 .

[33]  J. H. Lee,et al.  Fault diagnosis and fault tolerant control of linear stochastic systems with unknown inputs. , 2001 .

[34]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[35]  Jin Wang,et al.  Large-Scale Semiconductor Process Fault Detection Using a Fast Pattern Recognition-Based Method , 2010, IEEE Transactions on Semiconductor Manufacturing.

[36]  J. Wang,et al.  Identification of pneumatic cylinder friction parameters using genetic algorithms , 2004, IEEE/ASME Transactions on Mechatronics.

[37]  K. Khorasani,et al.  Satellite fault diagnosis using a bank of interacting Kalman filters , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[38]  Christopher Edwards,et al.  New results in robust actuator fault reconstruction for linear uncertain systems using sliding mode observers , 2007 .

[39]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[40]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[41]  Suk Joo Bae,et al.  Dual Features Functional Support Vector Machines for Fault Detection of Rechargeable Batteries , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[42]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[43]  Bo-Suk Yang,et al.  Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis , 2004, Expert Syst. Appl..

[44]  Chee Pin Tan,et al.  Sliding mode observers for fault detection and isolation , 2002 .

[45]  J. K. Spoerre Application of the cascade correlation algorithms (CCA) to bearing fault classification problems , 1997 .

[46]  D. A. Linkens,et al.  Automatic modelling and analysis of dynamic physical systems using qualitative reasoning and bond graphs , 1993 .

[47]  Marcel Staroswiecki,et al.  Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems , 2001, Autom..

[48]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[49]  Asok Ray,et al.  Stochastic modeling of fatigue crack dynamics for on-line failure prognostics , 1996, IEEE Trans. Control. Syst. Technol..

[50]  Antoine Grall,et al.  Sequential condition-based maintenance scheduling for a deteriorating system , 2003, Eur. J. Oper. Res..

[51]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[52]  L. Chambers Practical methods of optimization (2nd edn) , by R. Fletcher. Pp. 436. £34.95. 2000. ISBN 0 471 49463 1 (Wiley). , 2001, The Mathematical Gazette.

[53]  Steven Y. Liang,et al.  Damage mechanics approach for bearing lifetime prognostics , 2002 .

[54]  Steven Y. Liang,et al.  STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS , 2000 .

[55]  Douglas E. Adams,et al.  A nonlinear dynamical systems framework for structural diagnosis and prognosis , 2002 .

[56]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[57]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[58]  Christopher Edwards,et al.  Sliding mode observers for robust detection and reconstruction of actuator and sensor faults , 2003 .

[59]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[60]  Peng Wang,et al.  Fault prognostics using dynamic wavelet neural networks , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[61]  P. Baruah,et al.  HMMs for diagnostics and prognostics in machining processes , 2005 .

[62]  Stephen Ogaji,et al.  Engine-fault diagnostics:an optimisation procedure , 2002 .

[63]  G. Dauphin-Tanguy,et al.  Robust Fault Diagnosis by Using Bond Graph Approach , 2007, IEEE/ASME Transactions on Mechatronics.