Multi-Disciplinary Engineering for Cyber-Physical Production Systems (eBook)

Foreword 5
Preface 7
Contents 9
List of Contributors 11
1 Introduction to the Multi-Disciplinary Engineeringfor Cyber-Physical Production Systems 13
1.1 Motivation 14
1.2 Background 18
1.3 Research Questions 24
1.4 Book Structure 27
1.4.1 Part I: Product Design 27
1.4.2 Part II: Production System Engineering 28
1.4.3 Part III: Information Modeling and Integration 30
1.5 Who Shall Read This Book? 32
References 35
Part I Product and Systems Design 37
2 Product and Systems Engineering/CA* Tool Chains 38
2.1 Introduction 38
2.2 Generic Procedures for the Development of Interdisciplinary Products 41
2.2.1 Micro-logic in Development 42
2.2.2 Process Models as Macro-logic in Development 45
2.2.3 Process Models for CPS as an Interdisciplinary Technical System 48
2.2.4 Systems Engineering as an Interdisciplinary Approach for Development of CPS 51
2.3 Concretisation of Process Descriptions in the Sense of a Workflow 53
2.3.1 Adaptation of Development Processes to the Context 55
2.3.2 Identification of Context Factors for Adaption of Development Processes 57
2.3.3 An Approach for Systematic Analysis of Determining Factors for the Development Process 58
2.3.3.1 Goal System 60
2.3.3.2 Object System 61
2.3.3.3 Process System 62
2.3.3.4 Action System 62
2.3.3.5 Control of Development Tasks via the ZOPH Approach 64
2.4 Model-Based Engineering for Mastering Complexity 65
2.4.1 Model Based Systems Engineering 67
References 71
3 Cyber-Physical Product-Service Systems 74
3.1 Introduction 75
3.2 Research Methodology and Objectives 77
3.3 Elements and Definition of Cyber-Physical Product-Service Systems 78
3.3.1 Cyber-Physical Systems 78
3.3.2 Product-Service Systems 79
3.3.3 Cyber-Physical Product-Service Systems 81
3.4 Challenges for Integrating CPPS and PSS LifeCycles 83
3.4.1 Product LifeCycle Management 83
3.4.2 Service LifeCycle Management 84
3.4.3 Integration of PLM and SLM 85
3.4.4 Engineering Challenges 86
3.5 Implications for the Engineering Process 88
3.5.1 Cross-Domain Requirements Engineering and Design 89
3.5.2 Servitized Business Models Enabled by CPS 91
3.6 Industrial Use Case 93
3.7 Summary and Conclusions 95
3.7.1 Research Questions Answered 95
3.7.2 Strengths and Limitations 96
References 96
4 Product Lifecycle Management Challenges of CPPS 100
4.1 Introduction 100
4.2 State of the Art and Challenges of PLM in the CPPS Context 107
4.2.1 Processes and Methods 108
4.2.2 Model Representation 110
4.2.3 Information Management and Integration 111
4.3 PLM Forward and Backward Information Flows in CPPS 113
4.4 Summary and Outlook 118
References 119
Part II Production System Engineering 122
5 Fundamentals of Artifact Reuse in CPPS 123
5.1 Introduction 124
5.2 Approach 127
5.3 Generic Production System Architecture 128
5.3.1 Literature Review 128
5.3.2 Hierarchy Layers 129
5.4 Production System Life Cycle 135
5.4.1 Characteristics of Engineering Phase 137
5.4.2 Characteristics of Operation and Maintenance Phase 140
5.4.3 Characteristics of End-of-Life Phase 142
5.5 Summary and Outlook 144
References 145
6 Identification of Artifacts in Life Cycle Phases of CPPS 149
6.1 Introduction 150
6.2 Engineering Phase 151
6.2.1 Approach for the Identification of Artifactsin the Engineering Phase 151
6.2.2 Identification Criteria for Artifacts in Engineering Phase 152
6.2.2.1 Requirements 152
6.2.2.2 Layouts and Visualizations 152
6.2.2.3 Basic Specifications 152
6.2.2.4 Behavior Models 152
6.2.2.5 CAD Construction 153
6.2.3 Usage of Engineering Phase Artifacts 154
6.3 Operation and Maintenance Phase 157
6.3.1 Approach for the Identification of Artifactsin the Operation and Maintenance Phase 157
6.3.2 Identification Criteria for Artifacts in Operation and Maintenance Phase 158
6.3.2.1 Construction Element 160
6.3.2.2 Component 161
6.3.2.3 Function Group 162
6.3.2.4 Work Station 162
6.3.2.5 Work Unit 163
6.3.2.6 Production Line Segment 163
6.3.2.7 Production Line 164
6.3.2.8 Factory 164
6.3.2.9 Production Network 165
6.3.3 Usage of Operation and Maintenance Phase Artifacts 165
6.4 End-of-Life Phase 169
6.4.1 Approach for the Identification of Artifactsin the Engineering Phase 170
6.4.2 Identification Criteria for Artifacts in End-of-Life Phase 170
6.4.3 Usage of End-of-Life Phase Artifacts 173
6.5 Summary and Outlook 174
References 175
7 Description Means for Information Artifacts Throughout the Life Cycle of CPPS 178
7.1 Introduction 179
7.2 Disambiguation: Description Means, Information Handling Methods, and Tools 180
7.3 Description Means for Artifacts 181
7.3.1 Description Means During Engineering Phase 181
7.3.2 Description Means During Operation and Maintenance Phase 183
7.3.3 Description Means During End-of-Life Phase 185
7.4 Artifact Classification 187
7.5 Summary and Outlook 187
References 192
8 Engineering of Next Generation Cyber-Physical Automation System Architectures 193
8.1 Introduction 194
8.2 The Evolution of Automation System Architectures 195
8.2.1 Classical Automation System Architectures 196
8.2.2 Emerging Automation System Architectures 197
8.3 The Transformation of Automation System Architectures 202
8.3.1 Towards Information-Driven Automation Systems 202
8.3.2 Migration Strategies 204
8.4 Considerations on Future Automation System Architectures 206
8.4.1 Rethinking of Automation Systems Engineering 206
8.4.2 Directions and Challenges 207
8.5 Conclusion and Outlook 209
References 211
9 Engineering Workflow and Software Tool Chains of Automated Production Systems 215
List of Abbreviations 215
9.1 Introduction 216
9.2 Engineering Workflow of Production System 217
9.3 Established Tool Chains in Practice 220
9.3.1 Tool Chain for Mechanical Design 222
9.3.2 Tool Chains of Electrical Design 229
9.3.3 Tool Chain of PLC/Software Design 231
9.3.4 Tool Chain of Virtual Engineering 235
9.3.5 Tool Chain of Virtual Commissioning 237
9.4 Summary and Outlook 240
References 241
10 Standardized Information Exchange Within Production System Engineering 243
10.1 Introduction 243
10.2 Use Cases for Information Exchange 246
10.2.1 Use Case 1: Production System Hierarchies 246
10.2.2 Use Case 2: Integration of Pre-developed Production System Units 248
10.2.3 Use Case 3: Exchange of Control System Engineering Information 248
10.2.4 Use Case 4: Consistent and Up-To-Date Documentation 249
10.2.5 Use Case 5: Combination of Engineering and Runtime Information 249
10.2.6 Current Activities Related to Solution of the Use Cases 250
10.2.6.1 Development of Modular and Hierarchical Production System Architectures 250
10.2.6.2 Standardization of Data Exchange Formats 250
10.2.6.3 Integration of Engineering Data Representations and Runtime Communication Systems 251
10.3 Information Exchange Technologies 251
10.4 AutomationML 254
10.5 Challenges Within Standardization of Information Exchange 259
10.6 Summary 263
References 263
Part III Information Modeling and Integration 266
11 Model-Driven Systems Engineering: Principles and Application in the CPPS Domain 267
11.1 Introduction 267
11.2 Model-Driven Engineering in a Nutshell 271
11.2.1 Metamodeling 272
11.2.2 Model Transformations 273
11.3 Selected MDSE Standards for CPPS Engineering 275
11.3.1 Systems Modeling Language (SysML) 275
11.3.2 Modeling and Analysis of Real-Time Embedded System Profile (MARTE) 277
11.3.3 Performance Modeling Interchange Format (PMIF) 279
11.3.4 AutomationML 280
11.3.5 Synopsis 282
11.4 MDSE of CPPS in Action 282
11.4.1 Case Study 283
11.4.2 CPPS Modeling 284
11.4.2.1 Modeling in SysML 285
11.4.2.2 Profiling SysML Models with MARTE 288
11.4.2.3 Modeling in PMIF 289
11.4.2.4 Modeling in AML 292
11.4.3 CPPS Engineering Chain Automation 294
11.4.3.1 Integrating SysML and AML 294
11.4.3.2 Integrating AML and PMIF 296
11.4.4 Synopsis 298
11.4.5 Critical Discussion 299
11.5 Conclusion and Future Challenges 300
References 302
12 Semantic Web Technologies for Data Integration in Multi-Disciplinary Engineering 306
12.1 Introduction 306
12.2 Industry Needs for Semantic Web Technologies 308
12.3 Semantic Web Technologies: Key Concepts and Capabilities 315
12.3.1 Key Elements of Semantic Web Technologies 316
12.3.2 Data Integration with Semantic Web Technologies 318
12.3.3 Semantic Web Capabilities 319
12.4 Adoption of Semantic Web Technologies in Multi-Disciplinary Engineering Settings 322
12.5 Use Case: Engineering Data Integration in a Multi-Disciplinary Engineering Setting 324
12.6 A SWT-Based Solution for Data Integration 325
12.6.1 Ontologies Used for Data Integration 326
12.6.2 Mappings Across Local and Common Ontologies 327
12.6.3 Implementation Details and Functionality 329
12.7 Summary 331
References 332
13 Patterns for Self-Adaptation in Cyber-Physical Systems 335
13.1 Introduction 336
13.2 Background 337
13.2.1 Uncertainties 337
13.2.2 Adaptation 339
13.2.2.1 Architecture-Based Adaptation 339
13.2.2.2 Multi-Agent Based Approaches 340
13.2.2.3 Self-Organizing Based Approaches 340
13.2.3 Collective Intelligence Systems 341
13.3 Research Questions 343
13.4 Systematic Mapping Study Method 344
13.4.1 Search and Selection Strategy 345
13.4.2 Data Extraction 346
13.4.3 Data Analysis and Reporting 346
13.5 Adaptation in Cyber-Physical Systems 347
13.6 Threats to Validity 354
13.7 Reflection of the Systematic Mapping Study Results 355
13.8 Patterns for Self-Adaptation 355
13.8.1 Synthesize-Utilize Pattern 356
13.8.2 Synthesize-Command Pattern 358
13.8.3 Collect-Organize Pattern 359
13.9 Potential of Collective Intelligence Systems for Cyber-Physical Systems and Cyber-Physical Production Systems 361
13.9.1 Collective Intelligence Systems for Capability Augmentation 361
13.9.2 Collective Intelligence Systems as Enabler for Emergent Machine-To-Machine Interactions 362
13.9.3 Collective Intelligence Systems as Coordinators and Knowledge Integrators Across Heterogeneous, Multi-Disciplinary Domains 364
13.10 Related Work 365
13.11 Conclusion and Future Work 367
References 368
14 Service-Oriented Architectures for Interoperability in Industrial Enterprises 373
14.1 Introduction 373
14.2 Technical Features of the Industrial Enterprise 374
14.3 Service-Oriented Architectures and the Industrial Enterprise 377
14.3.1 IoT@Work 378
14.3.2 PLANTCockpit 378
14.3.3 IMC-AESOP 380
14.3.4 eScop 381
14.3.5 Arrowhead Framework 384
14.4 Realizations of the Reference Architectures 385
14.4.1 Service Discovery 385
14.4.2 Service Description 387
14.4.3 Data Representation and Access 389
14.4.4 Information and Message Encoding 391
14.4.5 Message Exchange 392
14.4.6 Networking, Data Link and Media 394
14.4.7 Security 395
14.5 Discussion 397
References 398
15 A Deterministic Product Ramp-up Process: How to Integrate a Multi-Disciplinary Knowledge Base 403
15.1 Introduction 404
15.2 Strategy-Dependent Relevance 406
15.3 Structure of a Production Process 408
15.4 Qualification of a Production Process 409
15.5 Product Ramp-up and the Agility of Production Systems 413
15.6 Invoking an Effective Multi-disciplinary Knowledge Base 419
15.7 Information Model and Matchmaking Scenarios 421
15.8 Needs for Standardization Across Enterprises 431
15.9 Outlook: Deterministic Product Ramp-up for Supply Chains 432
References 433
16 Towards Model Quality Assurance for Multi-Disciplinary Engineering 436
16.1 Introduction 437
16.2 Background 439
16.2.1 Stakeholder Needs for Model Quality Assurance 439
16.2.2 Model-Driven Engineering 440
16.2.3 AutomationML 441
16.2.4 Quality Assurance and Model Review 441
16.3 Research Questions 443
16.4 Model Quality Assurance Concept 445
16.4.1 Adapted Review Process for MDE and AutomationML MQA 445
16.4.2 A Generic Reviewing Language 447
16.4.3 Utilizing the Generic Reviewing Language for AutomationML 449
16.5 Conceptual Evaluation 451
16.5.1 Illustrative Use Case: Round-Trip-Engineering 452
16.5.2 MQA-Review Needs and Expected Tool Capabilities 454
16.5.3 Evaluation of MQA-Review with Tool Support 455
16.6 Summary, Limitations, and Outlook 457
References 459
17 Conclusions and Outlook on Research for Multi-Disciplinary Engineering for Cyber-Physical Production Systems 461
Index 471