Well-structured and interdisciplinary overview of MBSE, covering both theoretical foundations and practical applications
Taking an interdisciplinary approach, Model Based System Engineering provides a comprehensive introduction to understanding and applying model-based system engineering (MBSE) principles and practices in the design, development, and management of complex systems. Throughout the book, readers will find case studies, practical examples and exercises, and multiple-choice questions that reinforce key concepts and promote active learning.
The book begins by exploring the historical context of MBSE, highlighting its emergence as a response to the limitations of traditional document-centric approaches. It emphasizes the crucial role of abstraction in MBSE and introduces key concepts, definitions, and taxonomies that form the bedrock of this discipline. Subsequent chapters delve into the core principles of modeling, examining the intricate relationships between systems, languages, and models.
Sample topics covered in Model Based System Engineering include:
- Prefaced by Bran Selic, a world authority on MBSE and software engineering
- Model verification and validation, exploring various techniques, such as model checking, simulation, and testing that enable the early detection and resolution of design errors and inconsistencies
- Model-based system architecting, methodological considerations, and application in real-world contexts
- Various modeling paradigms, including structural and behavioral models
- The pivotal role of languages in enabling effective modeling practices
- Benefits of formalization in enhancing the precision, consistency, and analyzability of system models
Model Based System Engineering is an essential resource for systems engineers, researchers, and students seeking to understand and harness the power of MBSE in tackling the complexities of modern systems.
Table of Contents:
Preface ix
Foreword xi
Acronyms xiii
Introduction xv
1 The Start of the Journey 1
1.1 A Difficult Start 1
1.2 The Power of Abstraction 2
1.3 The Rise of MBSE 6
1.4 The Pillars of MBSE 9
1.5 The Ingredients of MBSE 12
1.6 Comparison with a Traditional Approach 14
1.7 Some Success Stories 16
1.8 Key Takeaways and Considerations 17
References 20
2 System Engineering Principles 23
2.1 Preliminary Definitions 23
2.2 System Definitions and Taxonomy 24
2.3 Systems of Systems 28
2.4 Ensuring System Consistency 31
2.5 Managing System Complexity 33
2.6 System Lifecycle 35
2.7 Historical Perspective 37
2.8 For a System Thinking 39
2.9 System Engineering Definition 42
2.10 System Engineering Issues 43
2.11 The Role of a Systems Engineer 45
2.12 Relationship with Requirements Engineering 49
2.13 Key Considerations in SE 53
2.14 Transitioning to MBSE 63
2.15 Conclusion 64
References 67
3 Modeling Principles 73
3.1 Systems, Languages, and Models 73
3.2 Models 79
3.3 Languages 86
3.4 Formalization Principles 90
3.5 The Power of Modeling 94
3.6 Models and Semantics 97
3.7 The Different Types of Model 99
3.8 Models and Diagrams 102
3.9 System and Viewpoints 105
3.10 Interface Design and Management 109
3.11 Modeling Standards and Tools 115
3.12 Modeling Requirements 118
3.13 Conclusion 120
References 123
4 Modeling Paradigms 127
4.1 Structural Models 127
4.2 Behavioral Models 137
4.3 Agent-based Modeling 161
4.4 Beyond Structure and Behavior 166
4.5 Modeling the System Environment 171
4.6 Conclusion 175
References 178
5 Model Verification and Validation 183
5.1 Verification 183
5.2 Validation 188
5.3 The Evolution of V&V 193
5.4 V&V Concerns 197
5.5 Theoretical Bases 200
5.6 Motivations for V&V Models 203
5.7 Techniques of V&V in MBSE 205
5.8 Standards and Good Practices 212
5.9 Closing Design Space Exploration 215
5.10 Reducing Costs and Delays 216
5.11 Conclusion 219
References 221
6 System Architecting Principles 225
6.1 Fundamental Principles 225
6.2 Types of System Architectures 228
6.3 Open Architectures 235
6.4 Architectural Patterns and Tactics 239
6.5 Evaluating and Updating System Architectures 241
6.6 Linking MBSE to Architectural Concerns 245
6.7 Conclusion 251
References 253
7 Methodological Bases 257
7.1 Preliminary Considerations 257
7.2 Methodological Foundations 262
7.3 Formalizing Processes 267
7.4 Modeling Processes 270
7.5 Process Orchestration 272
7.6 Model Transformations 274
7.7 MBSE and Project Management 279
7.8 Evaluating the Effectiveness of MBSE 282
7.9 MBSE Adoption: Challenges and Limitations 285
7.10 A Software Development Process Example 291
7.11 Conclusion 295
References 297
8 Case Study 301
8.1 Overview 301
8.2 Project Overview and Motivation 302
8.3 Blending “Meet-in-the-middle” and AI 304
8.4 The Arcadia Methodology 305
8.5 AI-powered Scenario Management 307
8.6 Formal Checking and Simulations 311
8.7 Decision-making and Optimization 314
8.8 Evaluation and the Path Forward 317
8.9 Conclusion 319
9 Conclusion 321
Index 323
About the Author :
Ali Koudri is a seasoned systems and software engineering expert with over 20 years of experience in designing and developing innovative solutions for complex systems. His extensive background spans diverse domains, including aeronautics, rail, defense, and healthcare, where he has consistently demonstrated his ability to tackle ambitious projects that require out-of-the-box thinking. Ali has held key roles in prominent organizations such as Thales Research and Technology and the Institute for Technological Research SystemX.
