Fault diagnosis of hybrid dynamic and complex systems / Moamar Sayed-Mouchaweh, editor.
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 economical losses...
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Full Text (via Springer) |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing,
2018.
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| Subjects: |
MARC
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| 245 | 0 | 0 | |a Fault diagnosis of hybrid dynamic and complex systems / |c Moamar Sayed-Mouchaweh, editor. |
| 264 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 264 | 4 | |c ©2018. | |
| 300 | |a 1 online resource (viii, 286 pages : 97 illustrations, 59 illustrations in color.) | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 347 | |a text file |b PDF |2 rda. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Intro; Preface; Contents; 1 Prologue; 1.1 Hybrid Dynamic Systems: Definition, Classes, and Modeling Tools; 1.2 Fault Diagnosis of Hybrid Dynamic Systems: Problem Formulation, Methods, and Challenges; 1.3 Contents of the Book; 1.3.1 Chapter 2; 1.3.2 Chapter 3; 1.3.3 Chapter 4; 1.3.4 Chapter 5; 1.3.5 Chapter 6; 1.3.6 Chapter 7; 1.3.7 Chapter 8; 1.3.8 Chapter 9; 1.3.9 Chapter 10; References; 2 Motor Fault Detection and Diagnosis Based on a Meta-cognitive Random Vector Functional Link Network; 2.1 Introduction; 2.1.1 Induction Motor; 2.1.2 Hybrid Dynamic System; 2.1.3 Our Approach. | |
| 505 | 8 | |a 2.2 Fault Detection and Diagnosis in Induction Motors2.2.1 Fault Detection and Diagnosis Features in an Induction Motor; 2.2.2 Fault Detection Methods from Single and Multiple Sources; 2.3 eT2RVFLN Architecture; 2.3.1 Cognitive Architecture of an eT2RVFLN; 2.3.2 Meta-cognitive Learning Policy of the eT2RVFLN; 2.3.2.1 What to Learn; 2.3.2.2 How to Learn; 2.3.2.3 When to Learn; 2.4 Experimental Design; 2.5 Numerical Results; 2.6 Conclusion; References; 3 Optimal Adaptive Threshold and Mode Fault Detection for Model-Based Fault Diagnosis of Hybrid Dynamical Systems; 3.1 Introduction. | |
| 505 | 8 | |a 3.2 Bond Graph3.2.1 Hybrid Bond Graph (HBG) Model; 3.3 Diagnostic HBG Model for Uncertain System; 3.3.1 Modelling Parameter Uncertainty; 3.3.2 Modelling Measurement Uncertainty; 3.3.3 ARR/GARR and Adaptive Threshold; 3.3.4 Fault Signature Matrix and Coherence Vector; 3.3.5 Proposed Method for Optimal Threshold and Mode Fault Detection; 3.4 Case Study: Bench Mark Hybrid Two-Tank System; 3.4.1 ARRs/GARRs for Hybrid Two-Tank System; 3.4.2 Optimum Adaptive Threshold for Hybrid Two-Tank System; 3.4.3 FDI Study for Hybrid Two-Tank System Using Proposed Technique; 3.5 Conclusions; References. | |
| 505 | 8 | |a 4 Diagnosing Hybrid Dynamical Systems Using Max-Plus Algebraic Methods4.1 Introduction; 4.2 Problem Statement; 4.2.1 Hybrid Systems Model; 4.2.2 Objective; 4.2.3 System Architecture; 4.3 Related Work; 4.3.1 Algebraic Descriptions of Hybrid Systems; 4.3.2 Petri Net Models; 4.3.3 Diagnosing Hybrid Systems; 4.4 Behaviour Modeling: Switching Max-Plus Linear Systems; 4.4.1 Max-Plus Algebra; 4.4.2 Continuous Dynamics: Max-Plus Linear Systems; 4.4.3 Switching Max-Plus Linear Systems; 4.4.4 Stochastic SMPL Systems; 4.4.5 Generality of Approach; 4.5 Running Example; 4.5.1 Nominal Model. | |
| 505 | 8 | |a 4.5.2 Fault Model4.5.3 Max-Plus Model; 4.6 Diagnosing Hybrid Systems Using SMPL Automata; 4.6.1 Observers; 4.6.2 Isolating Faults; 4.7 Computational Complexity; 4.7.1 Fault Detection; 4.7.2 Fault Isolation; 4.7.3 Approximation Algorithm; 4.8 Diagnosis Scenarios; 4.8.1 Scenario 1: T3 Leak; 4.8.2 Scenario 2: V3 Blockage; 4.8.3 Scenario 3: V2 Blockage; 4.9 Types of Hybrid Systems Covered; 4.10 Summary; References; 5 Monitoring of Hybrid Dynamic Systems: Application to Chemical Process; 5.1 Introduction; 5.2 Residual Generation by the Extended Kalman Filter. | |
| 520 | |a 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 economical losses caused by unexpected stops in production lines. 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, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. This 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/non-incremental 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. Synthesizes the state of the art in the domain of fault diagnosis of hybrid dynamic systems; Studies the complementarities and the links between the different methods and techniques of fault diagnosis of hybrid dynamic systems; Includes the required notions, definitions and background to understand the problem of fault diagnosis of hybrid dynamic systems and how to solve it; Uses multiple examples in order to facilitate the understanding of the presented methods. | ||
| 650 | 0 | |a Fault location (Engineering) | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Automatic control. | |
| 650 | 0 | |a Quality control. | |
| 650 | 0 | |a Reliability. | |
| 650 | 0 | |a Industrial safety. | |
| 650 | 0 | |a Electrical engineering. | |
| 650 | 7 | |a Reliability. |2 fast |0 (OCoLC)fst01093641. | |
| 650 | 7 | |a Quality control. |2 fast |0 (OCoLC)fst01084966. | |
| 650 | 7 | |a Industrial safety. |2 fast |0 (OCoLC)fst00971664. | |
| 650 | 7 | |a Electrical engineering. |2 fast |0 (OCoLC)fst01728596. | |
| 650 | 7 | |a Computational intelligence. |2 fast |0 (OCoLC)fst00871995. | |
| 650 | 7 | |a Automatic control. |2 fast |0 (OCoLC)fst00822702. | |
| 650 | 7 | |a Fault location (Engineering) |2 fast |0 (OCoLC)fst00921982. | |
| 700 | 1 | |a Sayed-Mouchaweh, Moamar, |e editor. | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319740133. |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-319-74014-0 |z Full Text (via Springer) |
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