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E-book: Model-based Process Supervision: A Bond Graph Approach

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Model-based fault detection and isolation requires a mathematical model of the system behaviour. Modelling is important and can be difficult because of the complexity of the monitored system and its control architecture. The authors use bond-graph modelling, a unified multi-energy domain modelling method, to build dynamic models of process engineering systems by composing hierarchically arranged sub-models of various commonly encountered process engineering devices. The structural and causal properties of bond-graph models are exploited for supervisory systems design. The structural properties of a system, necessary for process control, are elegantly derived from bond-graph models by following the simple algorithms presented here. Additionally, structural analysis of the model augmented with available instrumentation indicates directly whether it is possible to detect and/or isolate faults in some specific sub-space of the process. Such analysis aids in the design and resource optimization of new supervision platforms. Static and dynamic constraints, which link the time evolution of the known variables under normal operation, are evaluated in real time to determine faults in the system. Various decision or post-processing steps integral to the supervisory environment are discussed in this monograph; they are required to extract meaningful data from process state knowledge because of unavoidable process uncertainties. Process state knowledge has been further used to take active and passive fault accommodation measures. Several applications to academic and small-scale-industrial processes are interwoven throughout. Finally, an application concerning development of a supervision platform for an industrial plant is presented with experimental validation. Model-based Process Supervision provides control engineers and workers in industrial and academic research establishments interested in process engineering with a means to build up a practical and functional supervisory control environment and to use sophisticated models to get the best use out of their process data.

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This book is written for students and researchers interested in bond graph model-based control and supervision. The book presents a detailed up-to-date view of how bond graph modeling can be applied to fault diagnosis and control of engineering processes. (IEEE Control Systems Magazine, Vol. 30, February, 2010)

Abbreviations xix
Introduction to Process Supervision
1(12)
Process Supervision
1(6)
Basic Diagnosis Tasks
3(1)
Fault, Failure and Safety
4(3)
Diagnostic System
7(4)
Specification of Diagnostic Systems
7(1)
Classification of Diagnostic Systems
8(3)
Organization of the Book
11(2)
Bond Graph Modeling in Process Engineering
13(68)
The Bond Graph Methodology
13(6)
Introduction
13(1)
Concepts and Definitions
13(5)
Why Use Bond Graphs?
18(1)
Generalized Variables in Bond Graph Models
19(3)
Power Variables
19(1)
Energy Variables
20(1)
Word Bond Graph and Block Diagram
21(1)
Pseudo Bond Graph
22(4)
Why Pseudo Bond Graph?
22(2)
Pseudo Power Variables
24(1)
Pseudo Energy Variables
25(1)
Basic Bond Graph Elements
26(17)
One Port Passive Elements
26(11)
Active Elements
37(1)
Junctions
38(3)
Transformers and Gyrators
41(2)
Information Bonds
43(1)
Causality
43(9)
Introduction
43(2)
Sequential Causality Assignment Procedure (SCAP)
45(2)
Bicausal Bond Graphs
47(1)
State-space Equations
48(2)
Model Structure Knowledge
50(2)
Single Energy Bond Graph
52(7)
Bond Graphs for Mechanical Systems
52(1)
Bond Graphs for Thermal Processes
52(7)
Formal Generation of Dynamic Models
59(3)
Bond Graph Software
59(1)
Application
59(3)
Coupled Energy Bond Graph
62(19)
Representation
62(1)
Thermofluid Sources
63(1)
Thermofluid Multiport R
63(3)
Thermofluid Multiport C
66(2)
Application: Bond Graph Model of a Thermofluid Process
68(13)
Model-based Control
81(60)
Introduction
81(3)
Classical Model-based Control
84(16)
Conversion of Bond Graph Models to Signal Flow Graph Models
84(7)
Transfer Function from State-space Models
91(2)
Conversion of Bond Graph Models to Block Diagram Models
93(1)
Example I: Physical Model-based Control
93(2)
Example II: Physical Model-based System Design
95(5)
Causal Paths
100(4)
Transfer Functions from Bond Graph Models
101(2)
Delay and Attenuation Dynamics
103(1)
Augmented Controller and observer Design
104(9)
Pole Placement
104(3)
Example: Active Flow-induced Vibration Isolation
107(2)
Pole Placement Architecture in Bond Graph Models
109(2)
Discrete-time Augmented Controller and Observer
111(1)
Current Estimator
112(1)
Structural Analysis of Control Properties
113(28)
Structural Rank
113(1)
Structural Controllability
114(2)
Structural Observability
116(2)
Example I: Two Spools in a Cylinder
118(3)
Example II: A Hybrid Two-tank System
121(3)
Example III: A Biomechanics Problem
124(4)
Infinite Zeroes and Relative Degree
128(5)
Zero Dynamics
133(8)
Bond Graph Model-based Qualitative FDI
141(36)
Model Order Reduction
141(13)
FDI Using Bond Graphs and Qualitative Reasoning
154(5)
Determination of Initial Fault Set
155(3)
Fault Disambiguation
158(1)
Qualitative Analysis Using Tree Graphs
159(4)
Qualitative FDI Using Temporal Causal Graphs
163(6)
Fault Hypothesis Generation
164(2)
Fault Hypothesis Validation
166(3)
Hybrid Diagnosis with Temporal Causal Graphs
169(1)
Remarks on Model Linearization
170(7)
Bond Graph Model-based Quantitative FDI
177(52)
Introduction
177(3)
Classical Quantitative FDI and Residual Generation
180(15)
Observer-based Methods
181(2)
Observer-based Residuals
183(2)
Unknown Input Observers
185(6)
Parity Space Residuals
191(4)
Analytical Redundancy Relations and Fault Signature
195(3)
Residual and Decision Procedure
195(1)
The Fault Signature Matrix
196(2)
Structured Approach to ARR Derivation
198(6)
Behavior Model
198(3)
Constraints and Variables
201(1)
Derivation of ARRs
202(2)
ARR Generation from Bond Graph Models
204(10)
Constraints and Variables
204(3)
Algorithm for Generation of ARRs
207(2)
Example
209(5)
Causality Inversion Approach for ARR Derivation
214(4)
Example I: A Mechanical System
215(2)
Example II: A Two-tank System
217(1)
An FDI Application
218(11)
Residual Evaluation and Fault Signature Matrix
218(2)
Single Fault Hypothesis and Fault Isolation
220(1)
Simulation Results
221(8)
Application to a Steam Generator Process
229(42)
Introduction
229(5)
Process Description
229(2)
Nomenclature
231(2)
Word Bond Graph Model of the Process
233(1)
Bond Graph Models of Steam Generator's Components
234(10)
Bond Graph Model of the Storage Tank
234(1)
Bond Graph Model of the Supply System
235(1)
Bond Graph Model of the Boiler
236(2)
Bond Graph Model of the Steam Expansion System
238(1)
Bond Graph Model of the Condenser
239(4)
Bond Graph Model of the Condensate Discharge Valves
243(1)
Model Validation
244(4)
Design of the Supervision System
248(9)
Determination of Hardware Redundancies
249(1)
Derivation of ARRs
250(3)
Practical Fault Signature Matrix and Residual Sensitivity
253(1)
Effect of Hybrid Components
254(2)
Selection of Decision Procedure
256(1)
Online Implementation
257(5)
Data Acquisition and Toolbox Integration
257(4)
Native Interface
261(1)
Experimental Validation of Fault Scenarios
262(6)
Process Faults
262(3)
Sensor Faults
265(1)
Actuator Faults
266(1)
Controller Faults
267(1)
Reconfiguration
268(3)
Diagnostic and Bicausal Bond Graphs for FDI
271(44)
Diagnostic Bond Graph
271(10)
Derivation of ARR
274(2)
Example of a Non-resolvable System
276(4)
Fault Signature Matrix from Causal Paths
280(1)
Simulation and Real Time Implementation of the Residuals
281(8)
Integrated System Simulation: Coupling the Models
282(3)
Simulation Results
285(4)
The Initial Conditions Problem
289(5)
Order of Extra Derivatives
292(2)
Fault Scenario Simulation
294(1)
Matching Problems in Classical Bond Graph Modeling
294(6)
Notion of Bicausality
298(2)
Algorithm for ARR Generation and Construction of FSM
300(1)
Example I: A Two-tank Process
300(6)
Sensor Placement by Using Bicausal Bond Graphs
300(4)
Residual Generation: Symbolic Method
304(1)
Residual Evaluation and Fault Scenario Simulation
305(1)
Example II: A Servo-valve Controlled Motor Transmission System
306(5)
System Description and Bond Graph Model
306(2)
ARRs and FSM
308(2)
Validation Through Simulation
310(1)
The Fault Isolation Problem
311(4)
Actuator and Sensor Placement for Reconfiguration
315(32)
Introduction
315(1)
Minimal Sensor and Actuator Placement
315(1)
Sensor Placement for FDI and FTC
316(1)
External Model
316(4)
External Model in a Bond Graph Sense
317(1)
Services
317(1)
User Selected Operating Mode (USOM)
318(1)
Operating Mode Management
319(1)
Application to a Smart Pneumatic Valve
320(9)
Description of the System
321(1)
Bond Graph Model of the Smart Actuator
322(3)
Missions and Versions
325(1)
Operating Mode Management of the Smart Actuator
325(3)
Monitoring of the Smart Actuator
328(1)
Reconfiguration of a Thermo-fluid System
329(10)
Minimal Sensor and Actuator Placement
329(3)
Determination of Direct and Deduced Redundancies
332(1)
Analytical Redundancy Relations and FSM
333(2)
Sensor and Actuator Loss
335(1)
Automaton Representation of Equipment Availability
336(2)
Operating Modes of the Thermo-fluid System
338(1)
Application to a Steam Generator Process
339(8)
Operating Modes of the Steam Generator Process
340(2)
Experimental Results
342(5)
Isolation of Structurally Non-isolatable Faults
347(26)
Introduction
347(1)
Residuals and Robustness
348(2)
Localization of Fault Subspace
350(2)
Methodology for Single Fault Isolation
352(3)
Parameter Estimation
352(1)
Parallel Simulation of Bank of Fault Models
353(2)
Application to a Controlled Two-tank System
355(18)
ARRs and FSM
356(3)
Parameter Estimation
359(2)
Improvement of Isolability Using Bank of Fault Models
361(2)
Validation Through Simulation
363(2)
Qualitative Trend Analysis
365(8)
Multiple Fault Isolation Through Parameter Estimation
373(50)
Introduction
373(7)
Adaptive Thresholds for Robust Diagnosis
374(5)
Localization of Fault Subspace
379(1)
Fault Isolation by Parameter Estimation
380(3)
A Linear Two-tank System
383(10)
Output Error Minimization
384(3)
Optimization of Least Squares of ARRs
387(4)
Optimization by Using Diagnostic Bond Graph
391(2)
A Refrigerator Subsystem
393(9)
Bond Graph Model and the ARRs
395(2)
Fault Isolation Through Parameter Estimation
397(5)
A Non-linear Two-tank System
402(7)
The System and Its Bond Graph Model
402(2)
Residual Generation and Fault Detection
404(1)
Fault Isolation Through Parameter Estimation
405(4)
Optimization by Using Residual Sensitivity
409(5)
Gauss-Newton Optimization
411(1)
Example
411(3)
Sensitivity Bond Graphs
414(9)
Diagnostic Sensitivity Bond Graphs
415(2)
Example of the Use of Sensitivity Bond Graphs for FDI
417(6)
Fault Tolerant Control
423(30)
Introduction
423(2)
Classical System Inversion Algorithms
425(10)
Linear Time-Invariant (LTI) System Inversion
426(1)
Implicit Inversion of Strictly Proper Systems
427(1)
Examples of System Inversion
428(1)
Example of Input Reconstruction
429(2)
Example of Bond Graph Model Based Implicit System Inversion
431(1)
Bond Graph Model Based Explicit System Inversion
432(2)
Example of Bond Graph Model Based Explicit System Inversion
434(1)
Parameter Estimation
435(2)
Benchmark Problem: Active FTC of a Two-tank System
437(10)
Fault Quantification with Single Fault Hypotheses
437(3)
Fault Quantification with Multiple Fault Hypotheses
440(2)
Fault Accommodation Through Fault Tolerant Control
442(1)
System Inversion
443(1)
Actuator Sizing
443(4)
Passive FTC: Robust Overwhelming Control
447(6)
Overwhelming Controller Design
447(3)
Example: A Robust Level Controller
450(3)
References 453(14)
Index 467
Belkacem Ould Bouamama graduated in 1982 from the Institut National des Hydrocarbures et de la Chimie Boumerdes (INHC) in Process Control. He received his Ph.D. degree in 1987 from Goubkine Institute of Petroleum and Gas of Moscow. From 1988 to 1994, he was researcher and head of department of automatic control at INHC. From 1994 to 2000, he was an associate professor in control engineering at the Université des Sciences et Technolgies de Lille (France) and since then he has been a full professor at Ecole Polytechnique de Lille. Currently he heads the inter-disciplinary group on Fault Detection and Isolation using Bond Graph models at the Laboratoire d'Automatique, Génie Informatique & Signal, Lille, France. The main thrust of the research concerns modelling and monitoring of process engineering using a bond graph approach. Their application domains are mainly nuclear power plants, chemical and petrochemical processes. . He is the author of several international publications in this area and the co-author of three books in bond graph modelling and monitoring. He has written a book Modeling and Simulation in Thermal and Chemical Engineering published by Springer Verlag (3-540-66388-6).

Arun Kumar Samantaray graduated in 1989 from the College of Engineering and Technology (CET) in Mechanical Engineering. He received the masters degree in Dynamics and Contol and PhD degree in Mehanical Engineering (Rotor Dynamics) from the Indian Institute of Technology-Kharagpur, in 1991 and 1996, respectively. From 1996 to 2001, he worked as the Project Manager at the HighTech Consultants. From 2001 to 2004, he was a research scientist at Université des Sciences et Technologies de Lille (France) and thereafter; he has been an assistant professor in the Department of Mechanical Engineering at the Indian Institute of Technology, Kharagpur. He is an author of bond graph modelling software SYMBOLS and also the editor-in-chief of the bond graph forum atwww.bondgraphs.com. He is the new co-author in the second edition of the book Modelling and Simulation of Engineering Systems through Bond Graphs. He is also a consultant to various industries requiring help in modelling, simulation, design, fault detection, and automation.
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