Model-based process supervision : a bond graph approach

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. All evolving engineering disciplines first create a body of fundamental knowledge and then move on to new problem areas. Control engineering has now reached this level of maturity and is tackling new theoretical and applications areas. The field of nonlinear systems is receiving much research attention as are the problems of industrial supervisory control. The twin drivers of research into supervisory control are the use of new technology (computer networks and distributed sensor networks, for example) and the search for theoretical techniques to describe and solve supervisory control application problems.

Author Notes

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.

Abbreviations	p. xix
1 Introduction to Process Supervision	p. 1
1.1 Process Supervision	p. 1
1.1.1 Basic Diagnosis Tasks	p. 3
1.1.2 Fault, Failure and Safety	p. 4
1.2 Diagnostic System	p. 7
1.2.1 Specification of Diagnostic Systems	p. 7
1.2.2 Classification of Diagnostic Systems	p. 8
1.3 Organization of the Book	p. 11
2 Bond Graph Modeling in Process Engineering	p. 13
2.1 The Bond Graph Methodology	p. 13
2.1.1 Introduction	p. 13
2.1.2 Concepts and Definitions	p. 13
2.1.3 Why Use Bond Graphs?	p. 18
2.2 Generalized Variables in Bond Graph Models	p. 19
2.2.1 Power Variables	p. 19
2.2.2 Energy Variables	p. 20
2.2.3 Word Bond Graph and Block Diagram	p. 21
2.3 Pseudo Bond Graph	p. 22
2.3.1 Why Pseudo Bond Graph?	p. 22
2.3.2 Pseudo Power Variables	p. 24
2.3.3 Pseudo Energy Variables	p. 25
2.4 Basic Bond Graph Elements	p. 26
2.4.1 One Port Passive Elements	p. 26
2.4.2 Active Elements	p. 37
2.4.3 Junctions	p. 38
2.4.4 Transformers and Gyrators	p. 41
2.4.5 Information Bonds	p. 43
2.5 Causality	p. 43
2.5.1 Introduction	p. 43
2.5.2 Sequential Causality Assignment Procedure (SCAP)	p. 45
2.5.3 Bicausal Bond Graphs	p. 47
2.5.4 State-space Equations	p. 48
2.5.5 Model Structure Knowledge	p. 50
2.6 Single Energy Bond Graph	p. 52
2.6.1 Bond Graphs for Mechanical Systems	p. 52
2.6.2 Bond Graphs for Thermal Processes	p. 52
2.7 Formal Generation of Dynamic Models	p. 59
2.7.1 Bond Graph Software	p. 59
2.7.2 Application	p. 59
2.8 Coupled Energy Bond Graph	p. 62
2.8.1 Representation	p. 62
2.8.2 Thermofluid Sources	p. 63
2.8.3 Thermofluid Multiport R	p. 63
2.8.4 Thermofluid Multiport C	p. 66
2.8.5 Application: Bond Graph Model of a Thermofluid Process	p. 68
3 Model-based Control	p. 81
3.1 Introduction	p. 81
3.2 Classical Model-based Control	p. 84
3.2.1 Conversion of Bond Graph Models to Signal Flow Graph Models	p. 84
3.2.2 Transfer Function from State-space Models	p. 91
3.2.3 Conversion of Bond Graph Models to Block Diagram Models	p. 93
3.2.4 Example I: Physical Model-based Control	p. 93
3.2.5 Example II: Physical Model-based System Design	p. 95
3.3 Causal Paths	p. 100
3.3.1 Transfer Functions from Bond Graph Models	p. 101
3.3.2 Delay and Attenuation Dynamics	p. 103
3.4 Augmented Controller and Observer Design	p. 104
3.4.1 Pole Placement	p. 104
3.4.2 Example: Active Flow-induced Vibration Isolation	p. 107
3.4.3 Pole Placement Architecture in Bond Graph Models	p. 109
3.4.4 Discrete-time Augmented Controller and Observer	p. 111
3.4.5 Current Estimator	p. 112
3.5 Structural Analysis of Control Properties	p. 113
3.5.1 Structural Rank	p. 113
3.5.2 Structural Controllability	p. 114
3.5.3 Structural Observability	p. 116
3.5.4 Example I: Two Spools in a Cylinder	p. 118
3.5.5 Example II: A Hybrid Two-tank System	p. 121
3.5.6 Example III: A Biomechanics Problem	p. 124
3.5.7 Infinite Zeroes and Relative Degree	p. 128
3.5.8 Zero Dynamics	p. 133
4 Bond Graph Model-based Qualitative FDI	p. 141
4.1 Model Order Reduction	p. 141
4.2 FDI Using Bond Graphs and Qualitative Reasoning	p. 154
4.2.1 Determination of Initial Fault Set	p. 155
4.2.2 Fault Disambiguation	p. 158
4.3 Qualitative Analysis Using Tree Graphs	p. 159
4.4 Qualitative FDI Using Temporal Causal Graphs	p. 163
4.4.1 Fault Hypothesis Generation	p. 164
4.4.2 Fault Hypothesis Validation	p. 166
4.5 Hybrid Diagnosis with Temporal Causal Graphs	p. 169
4.6 Remarks on Model Linearization	p. 170
5 Bond Graph Model-based Quantitative FDI	p. 177
5.1 Introduction	p. 177
5.2 Classical Quantitative FDI and Residual Generation	p. 180
5.2.1 Observer-based Methods	p. 181
5.2.2 Observer-based Residuals	p. 183
5.2.3 Unknown Input Observers	p. 185
5.2.4 Parity Space Residuals	p. 191
5.3 Analytical Redundancy Relations and Fault Signature	p. 195
5.3.1 Residual and Decision Procedure	p. 195
5.3.2 The Fault Signature Matrix	p. 196
5.4 Structured Approach to ARR Derivation	p. 198
5.4.1 Behavior Model	p. 198
5.4.2 Constraints and Variables	p. 201
5.4.3 Derivation of ARRs	p. 202
5.5 ARR Generation from Bond Graph Models	p. 204
5.5.1 Constraints and Variables	p. 204
5.5.2 Algorithm for Generation of ARRs	p. 207
5.5.3 Example	p. 209
5.6 Causality Inversion Approach for ARR Derivation	p. 214
5.6.1 Example I: A Mechanical System	p. 215
5.6.2 Example II: A Two-tank System	p. 217
5.7 An FDI Application	p. 218
5.7.1 Residual Evaluation and Fault Signature Matrix	p. 218
5.7.2 Single Fault Hypothesis and Fault Isolation	p. 220
5.7.3 Simulation Results	p. 221
6 Application to a Steam Generator Process	p. 229
6.1 Introduction	p. 229
6.1.1 Process Description	p. 229
6.1.2 Nomenclature	p. 231
6.1.3 Word Bond Graph Model of the Process	p. 233
6.2 Bond Graph Models of Steam Generator's Components	p. 234
6.2.1 Bond Graph Model of the Storage Tank	p. 234
6.2.2 Bond Graph Model of the Supply System	p. 235
6.2.3 Bond Graph Model of the Boiler	p. 236
6.2.4 Bond Graph Model of the Steam Expansion System	p. 238
6.2.5 Bond Graph Model of the Condenser	p. 239
6.2.6 Bond Graph Model of the Condensate Discharge Valves	p. 243
6.3 Model Validation	p. 244
6.4 Design of the Supervision System	p. 248
6.4.1 Determination of Hardware Redundancies	p. 249
6.4.2 Derivation of ARRs	p. 250
6.4.3 Practical Fault Signature Matrix and Residual Sensitivity	p. 253
6.4.4 Effect of Hybrid Components	p. 254
6.4.5 Selection of Decision Procedure	p. 256
6.5 Online Implementation	p. 257
6.5.1 Data Acquisition and Toolbox Integration	p. 257
6.5.2 Native Interface	p. 261
6.6 Experimental Validation of Fault Scenarios	p. 262
6.6.1 Process Faults	p. 262
6.6.2 Sensor Faults	p. 265
6.6.3 Actuator Faults	p. 266
6.6.4 Controller Faults	p. 267
6.7 Reconfiguration	p. 268
7 Diagnostic and Bicausal Bond Graphs for FDI	p. 271
7.1 Diagnostic Bond Graph	p. 271
7.1.1 Derivation of ARR	p. 274
7.1.2 Example of a Non-resolvable System	p. 276
7.1.3 Fault Signature Matrix from Causal Paths	p. 280
7.2 Simulation and Real Time Implementation of the Residuals	p. 281
7.2.1 Integrated System Simulation: Coupling the Models	p. 282
7.2.2 Simulation Results	p. 285
7.3 The Initial Conditions Problem	p. 289
7.3.1 Order of Extra Derivatives	p. 292
7.3.2 Fault Scenario Simulation	p. 294
7.4 Matching Problems in Classical Bond Graph Modeling	p. 294
7.4.1 Notion of Bicausality	p. 298
7.4.2 Algorithm for ARR Generation and Construction of FSM	p. 300
7.5 Example I: A Two-tank Process	p. 300
7.5.1 Sensor Placement by Using Bicausal Bond Graphs	p. 300
7.5.2 Residual Generation: Symbolic Method	p. 304
7.5.3 Residual Evaluation and Fault Scenario Simulation	p. 305
7.6 Example II: A Servo-valve Controlled Motor Transmission System	p. 306
7.6.1 System Description and Bond Graph Model	p. 306
7.6.2 ARRs and FSM	p. 308
7.6.3 Validation Through Simulation	p. 310
7.7 The Fault Isolation Problem	p. 311
8 Actuator and Sensor Placement for Reconfiguration	p. 315
8.1 Introduction	p. 315
8.1.1 Minimal Sensor and Actuator Placement	p. 315
8.1.2 Sensor Placement for FDI and FTC	p. 316
8.2 External Model	p. 316
8.2.1 External Model in a Bond Graph Sense	p. 317
8.2.2 Services	p. 317
8.2.3 User Selected Operating Mode (USOM)	p. 318
8.2.4 Operating Mode Management	p. 319
8.3 Application to a Smart Pneumatic Valve	p. 320
8.3.1 Description of the System	p. 321
8.3.2 Bond Graph Model of the Smart Actuator	p. 322
8.3.3 Missions and Versions	p. 325
8.3.4 Operating Mode Management of the Smart Actuator	p. 325
8.3.5 Monitoring of the Smart Actuator	p. 328
8.4 Reconfiguration of a Thermo-fluid System	p. 329
8.4.1 Minimal Sensor and Actuator Placement	p. 329
8.4.2 Determination of Direct and Deduced Redundancies	p. 332
8.4.3 Analytical Redundancy Relations and FSM	p. 333
8.4.4 Sensor and Actuator Loss	p. 335
8.4.5 Automaton Representation of Equipment Availability	p. 336
8.4.6 Operating Modes of the Thermo-fluid System	p. 338
8.5 Application to a Steam Generator Process	p. 339
8.5.1 Operating Modes of the Steam Generator Process	p. 340
8.5.2 Experimental Results	p. 342
9 Isolation of Structurally Non-isolatable Faults	p. 347
9.1 Introduction	p. 347
9.2 Residuals and Robustness	p. 348
9.3 Localization of Fault Subspace	p. 350
9.4 Methodology for Single Fault Isolation	p. 352
9.4.1 Parameter Estimation	p. 352
9.4.2 Parallel Simulation of Bank of Fault Models	p. 353
9.5 Application to a Controlled Two-tank System	p. 355
9.5.1 ARRs and FSM	p. 356
9.5.2 Parameter Estimation	p. 359
9.5.3 Improvement of Isolability Using Bank of Fault Models	p. 361
9.5.4 Validation Through Simulation	p. 363
9.5.5 Qualitative Trend Analysis	p. 365
10 Multiple Fault Isolation Through Parameter Estimation	p. 373
10.1 Introduction	p. 373
10.1.1 Adaptive Thresholds for Robust Diagnosis	p. 374
10.1.2 Localization of Fault Subspace	p. 379
10.2 Fault Isolation by Parameter Estimation	p. 380
10.3 Example I: A Linear Two-tank System	p. 383
10.3.1 Output Error Minimization	p. 384
10.3.2 Optimization of Least Squares of ARRs	p. 387
10.3.3 Optimization by Using Diagnostic Bond Graph	p. 391
10.4 Example II: A Refrigerator Subsystem	p. 393
10.4.1 Bond Graph Model and the ARRs	p. 395
10.4.2 Fault Isolation Through Parameter Estimation	p. 397
10.5 Example III: A Non-linear Two-tank System	p. 402
10.5.1 The System and Its Bond Graph Model	p. 402
10.5.2 Residual Generation and Fault Detection	p. 404
10.5.3 Fault Isolation Through Parameter Estimation	p. 405
10.6 Optimization by Using Residual Sensitivity	p. 409
10.6.1 Gauss-Newton Optimization	p. 411
10.6.2 Example	p. 411
10.7 Sensitivity Bond Graphs	p. 414
10.7.1 Diagnostic Sensitivity Bond Graphs	p. 415
10.7.2 Example of the Use of Sensitivity Bond Graphs for FDI	p. 417
11 Fault Tolerant Control	p. 423
11.1 Introduction	p. 423
11.2 Classical System Inversion Algorithms	p. 425
11.2.1 Linear Time-Invariant (LTI) System Inversion	p. 426
11.2.2 Implicit Inversion of Strictly Proper Systems	p. 427
11.2.3 Examples of System Inversion	p. 428
11.2.4 Example of Input Reconstruction	p. 429
11.2.5 Example of Bond Graph Model Based Implicit System Inversion	p. 431
11.2.6 Bond Graph Model Based Explicit System Inversion	p. 432
11.2.7 Example of Bond Graph Model Based Explicit System Inversion	p. 434
11.3 Parameter Estimation	p. 435
11.4 Benchmark Problem: Active FTC of a Two-tank System	p. 437
11.4.1 Fault Quantification with Single Fault Hypothesis	p. 437
11.4.2 Fault Quantification with Multiple Fault Hypotheses	p. 440
11.4.3 Fault Accommodation Through Fault Tolerant Control	p. 442
11.4.4 System Inversion	p. 443
11.4.5 Actuator Sizing	p. 443
11.5 Passive FTC: Robust Overwhelming Control	p. 447
11.5.1 Overwhelming Controller Design	p. 447
11.5.2 Example: A Robust Level Controller	p. 450
References	p. 453
Index	p. 467

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