Industrial Internet of Things (eBook)

Houbing Song is Assistant Professor in the Department of Electrical and Computer Engineering at the West Virginia University. He currently serves as the founding director of both the West Virginia Center of Excellence for Cyber-Physical Systems (WVCECPS) sponsored by the West Virginia Higher Education Policy Commission and the Security and Optimization for Networked Globe Laboratory (SONG Lab). His research interests lie in the areas of cyber-physical systems, internet of things, connected vehicles, wireless communications and networking, optical communications and networking, smart grid communications and networking, and body sensor networks. Danda B. Rawat is Assistant Professor in the Department of Electrical Engineering at the Georgia Southern University. He currently serves as the Founding Director of the Cyber-security, Wireless Systems and Networking Innovations (CWiNs) Laboratory. His research interests include design, analysis, and evaluation of cognitive radio networks, software defined networks, cyber physical systems, vehicular/wireless ad hoc networks, wireless sensor networks, wireless mesh networks, and cyber-security and wireless communication for smart grid. Sabina Jeschke is the Full Professor and Head of Institute Cluster IMA/ZLW & IfU (Institute of Computer Sciences in Mechanical Engineering (IMA), Center for Learning and Knowledge Management (ZLW), Associated Institute for Management Cybernetics e.V. (IfU)), Faculty of Mechanical Engineering, RWTH Aachen University. She is vice dean of the Faculty of Mechanical Engineering of the RWTH Aachen University, chairwoman of the board of management of the VDI Aachen and member of the supervisory board of the Körber AG. Her research interests include distributed artificial intelligence and complex IT-systems (e.g. cloud computing, Internet of Things, cyber-physical-systems, green IT & ET, semantic web services), robotics and automation (e.g. heterogeneous and cooperative robotics, cooperative agents, web services for robotics, artificial intelligence production technology), traffic and mobility (autonomous and semi-autonomous traffic systems, international logistics, car2car & car2X models), innovation & future research (e.g. empirical and IT-based analysis via Data Mining and Text Mining) and virtual worlds for scientific and R&D cooperations (e.g. virtual and remote laboratories, intelligent assistants, semantic coding of specialized information). Christian Brecher is currently the Ordinary Professor for Machine Tools at the Laboratory for Machine Tools and Production Engineering (WZL) of the RWTH Aachen as well as the Director of the Department for Production Machines at the Fraunhofer Institute for Production Technology IPT. He is CEO of the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” that is funded by the German Research Foundation (DFG).

Foreword 7
Cyber-Physical Systems for Production Technology 7
Contents 11
About the Editors 15
Introduction and Overview 18
1 Industrial Internet of Things and Cyber Manufacturing Systems 19
1 Introduction 19
2 Foundations of the Industrial Internet of Things and Cyber Manufacturing Systems 20
3 Potentials and Challenges 24
4 Major Research Initiatives 26
5 Approaches and Solutions 27
5.1 Modeling for CPS and CMS 27
5.2 Architectural Design Patterns for CMS and IIoT 29
5.3 Communication and Networking 30
5.4 Artificial Intelligence and Analytics 30
5.5 Evolution of Workforce and Human-Machine-Interaction 32
6 A Glance into the Future: Towards Autonomous Networked Manufacturing Systems 33
Acknowledgments 34
References 34
2 An Application Map for Industrial Cyber-Physical Systems 36
1 An Introduction to Cyber-Physical Systems 36
2 Foundations of Industrial Cyber-Physical Systems 37
2.1 Technical Dimension of Cyber-Physical Systems 38
2.2 Human Dimension of Cyber-Physical Systems 39
2.3 Organizational Dimension of Cyber-Physical Systems 40
3 Categories of Potential Improvement for Industrial Cyber-Physical Systems 42
3.1 Automatization 44
3.2 Autonomization 45
3.3 Human-Machine Interaction 46
3.4 Decentralization 47
3.5 Digitization for Process Alignment 47
3.6 Big Data 48
3.7 Cyber Security 49
3.8 Knowledge Management 49
3.9 Qualification 50
4 Elaboration of an Application Map for Industrial Cyber-Physical Systems 50
4.1 Smart Factory 51
4.2 Industrial Smart Data 52
4.3 Industrial Smart Services 54
4.4 Smart Products 55
4.5 Product-Related Smart Data 56
4.6 Product-Related Smart Services 57
4.7 Utilization of the Application Map 57
5 Summary and Outlook 59
References 60
3 Cyber-Physical Electronics Production 62
1 Trends and Requirements in Modern Electronics Production 62
1.1 Miniaturization and Function Integration 63
1.2 Flexibility and Complexity 65
1.3 Logistics and Production Concepts 67
2 Enabling E-CPS Technologies 71
2.1 Sensor Integration, Printing Technologies and Communications 71
2.2 Software Systems 74
2.3 Autonomous and Smart One-Piece-Flow 79
3 Concept of a Cyber-Physical Electronics Production System 83
3.1 Self-Learning Electronics Production Processes 84
3.2 Assistance Systems 86
3.3 Integrated Cyber-Physical Electronics Production 89
References 91
Modeling for CPS and CMS 94
4 Cyber-Physical Systems Engineering for Manufacturing 95
1 Introduction 95
2 Cyber-Physical Systems 96
3 Systems Engineering 97
4 Manufacturing Innovation 98
5 Smart Manufacturing Systems Programs at NIST 99
5.1 Smart Manufacturing Systems Design and Analysis 100
5.1.1 Modeling Methodologies for Manufacturing System Analysis 101
5.1.2 Predictive Analytics for Manufacturing Systems 102
5.1.3 Performance Measurement for Smart Manufacturing 104
5.1.4 Service-Based Manufacturing and Service Composition 105
5.2 Smart Manufacturing Operations Planning and Control 107
5.2.1 Digital Thread for Smart Manufacturing 108
5.2.2 Systems Analysis Integration for Smart Manufacturing Operations 112
5.2.3 Wireless Systems for Industrial Environments 112
5.2.4 Cybersecurity for Smart Manufacturing Systems 115
5.2.5 Prognostics, Health Management and Control 116
5.2.6 Smart Manufacturing Systems Test Bed 118
6 Summary and Concluding Remarks 119
References 120
5 Model-Based Engineering of Supervisory Controllers for Cyber-Physical Systems 125
1 Introduction 125
1.1 Model-Based Systems Engineering 125
1.2 Structure of This Chapter 128
2 Synthesis-Based Development of Coordination Control 128
3 Description of an AGV System 131
3.1 Components of the Multi Mover 132
3.2 Interaction of the Components 133
4 Hybrid Models of the Uncontrolled System 134
4.1 Multi Mover High-Level Modes and Movement 135
4.2 Drive Motor 136
4.3 Steer Motor 137
4.4 Ride Control 137
4.5 Battery Sensor 137
4.6 Proximity Sensor 138
4.7 Bumper Switch 138
4.8 LEDs and Buttons 139
4.9 Abstraction Form Hybrid Automata to Discrete-Event Models 139
5 Requirements of the System 140
5.1 Emergency and Error Handling 140
5.2 LED Actuation 141
5.3 Motor Actuation 141
5.4 Button Handling 143
5.5 Proximity Sensors and Ride Control Handling 144
6 Synthesis of Supervisory Controller 145
7 Simulation-Based Visualization 146
8 Concluding Remarks 148
References 148
6 Formal Verification of SystemC-based Cyber Components 151
1 Introduction 151
2 Related Work 153
3 Preliminaries 155
3.1 Bounded Model Checking and Induction 155
3.2 SystemC Basics 156
4 TLM Property Checking 156
4.1 Simplified Model of the SystemC Kernel 157
4.2 Model Generation 161
4.2.1 SystemC to SCTLMD 161
4.2.2 Kernel Integration 162
4.2.3 Limitations 163
4.3 Property Language and Monitor Generation 165
4.3.1 Simple Safety Properties 166
4.3.2 Transaction Properties 166
4.3.3 System-Level Properties 167
4.4 BMC-Based Verification 167
5 Induction-Based TLM Property Checking 169
6 Experiments 172
6.1 BMC-Based Verification 173
6.1.1 FIFO Design 173
6.1.2 TLM-2.0 Design 174
6.2 Induction-Based Verification 176
6.2.1 FIFO Design and TLM-2.0 Design 176
6.2.2 JPEG Encoder 177
6.2.3 Chain Benchmark 178
6.3 Summary of Experimental Results 179
7 Conclusions 179
References 179
7 Evaluation Model for Assessment of Cyber-Physical Production Systems 182
1 Introduction 182
1.1 Motivation 183
2 A First Analysis of Value-Adds 185
3 State-of-the-Art 187
4 Approach and Methodology 191
5 Abilities of Cyber-Physical Systems 193
5.1 Abilities of Capturing and Processing Data and Information 194
5.2 Abilities of High-Quality Analysis with Artificial Intelligence 195
6 Advanced Abilities 196
6.1 The “Industrie 4.0” Component 197
6.2 Artificial Intelligence 198
7 Performance Indicators 200
7.1 Identified Performance Indicators 201
7.2 Performance Indicators of the Overall Systems Architecture 202
7.2.1 Modularity 202
7.2.2 Complexity 203
7.3 Performance Indicators of Production Operation 203
7.3.1 Maintainability 203
7.3.2 Production Efficiency 204
7.4 Performance Indicators of Changing Production Systems 204
7.4.1 Re-configurability 204
7.4.2 Automatic Planning 204
7.4.3 Automatic Adaptation 205
7.5 Performance Indicators of Cyber Support 205
7.5.1 Social Interaction 205
7.5.2 Support of Decisions 205
7.5.3 Further Characteristics: Usability 206
7.6 Relation Between Performance Indicators and Abilities 206
8 Validation 206
9 Conclusion 208
10 Further Work 210
References 210
Architectural Design Patterns for CMS and IIoT 213
8 CPS-Based Manufacturing with Semantic Object Memories and Service Orchestration for Industrie 4.0 Applications 214
1 Introduction 214
2 Use Cases 216
2.1 Use Case 1: Orchestrated Production 219
2.2 Use Case 2: Automated Maintenance 220
2.3 Use Case 3: Priority Management 221
3 Related Work 222
3.1 Semantic Technologies 222
3.2 Hardware Requirements 223
3.3 M2M Communication 224
3.4 Digital Object Representations 225
3.5 Conclusion 227
4 Digital Object Memories 227
4.1 Data Model 228
4.2 Storage Infrastructure 230
4.3 Communication Interfaces 232
5 Semantic Service Orchestration 233
5.1 Service Discovery 234
5.2 Semantic Orchestration 235
6 Conclusion 237
References 238
9 Integration of a Knowledge Database and Machine Vision Within a Robot-Based CPS 241
1 Introduction 241
2 Terms and Conditions 242
3 Research Efforts 243
4 Handling Dangerous Goods Using Industrial Machine Vision 244
4.1 Analysis of the Process Flow and the Boundary Conditions 245
4.2 Solution 246
4.3 Detection the Orientation of the Manhole Cover 248
4.4 Examination of Manhole Cover Seal 249
4.5 Operator Interface of the System 249
4.6 Control and Communication Concept 250
4.7 Conclusion 251
5 Laundry Logistics in Conjunction with RFID Systems 251
5.1 Prospective Applications of Industry 4.0 in the Textile Industry 253
5.2 Implementation of Industry 4.0 Within Industrial Laundries 254
5.3 Conclusion 256
6 Production, in Particular Assembly with the Help of Physical Human-Robot Interaction (PHRI) 257
6.1 Challenges 257
6.1.1 Objectives and Approach 257
6.1.2 Description of Process 258
6.2 Planning 259
6.2.1 Analysis 259
6.2.2 Sub Problems 259
6.2.3 Requirements 259
6.3 Applied Technologies 260
6.3.1 Robotics 260
6.3.2 Machine Vision and Data Bases 260
6.3.3 Pre-separation 260
6.3.4 Robotics and Cell 261
6.3.5 Tools Used 261
6.3.6 Human Machine Interface 262
6.3.7 Search Strategy 264
6.3.8 Image Processing and Database 264
6.4 Procedure for Developing the Solution 265
6.5 Summary 266
7 Research Challenges and Conclusions 267
References 268
10 Interoperability in Smart Automation of Cyber Physical Systems 271
1 Introduction 271
2 Related Work 273
2.1 Machine-Machine Interaction 273
2.1.1 Field Bus Systems and the Industrial Ethernet 274
2.1.2 Data Distribution Service (DDS) 275
2.1.3 OPC Unified Architecture 276
2.2 Human-Robot-Interaction 279
3 Holistic Interoperability in Production Networks 282
3.1 Interoperability on Machine-Machine-Level Using OPC UA and Semantic Technologies 282
3.2 Using Artificial Intelligence to Learn from Data 283
4 Towards Interoperability in Human-Robot Interaction 284
4.1 System Design 285
4.2 Gathering 285
4.3 Recognition and Evaluation 286
4.4 Respond 286
5 Use Cases 287
5.1 Adaptable Demonstrator for Flexible Production Organization 287
5.2 Virtual Production Intelligence Platform 290
5.3 Canoe 292
6 Conclusion 293
References 294
11 Enhancing Resiliency in Production Facilities Through Cyber Physical Systems 297
1 Introduction 297
2 The Need for Resilient Factories in the Context of Industry 4.0 298
2.1 Cyber-Physical Systems, Self-Optimization and the Internet of Things 298
2.1.1 Cyber-Physical Systems (CPS) 298
2.1.2 Cyber-Physical Production System (CPPS) 299
2.1.3 Self-Optimization 300
2.1.4 Internet of Things 300
2.1.5 Industry 4.0 301
2.2 Resilient Production Systems 301
2.2.1 Market Trend Customized Products 301
2.2.2 Technology Trend Cyber-Physical Production Systems 302
2.2.3 Global Trend of Energy and Resource Efficiency 303
2.2.4 The Resilient Factory as a Response to the Given Trends 303
3 Objectives of the Resilient Factory 304
4 Two Example Cases for Cyber-Physical Systems in Production 306
4.1 Energy-Oriented Manufacturing Planning and Control System 306
4.1.1 Requirements and Conditions 306
4.1.2 Concept and Procedure 308
4.1.3 Applications and Potentials 311
4.2 Smart Glasses in Industrial Assembly 311
4.2.1 Overview of Smart Devices Established on the Market 312
4.2.2 Current Applications and Suitability of Smart Devices in Production 313
4.2.3 Potentials for Increased Productivity and New Applications 314
5 Prerequisites and Requirements for Cyber-Physical Production Systems in the Resilient Factory 315
5.1 Technical Requirements 316
5.1.1 Decentralized IT Structures 316
5.1.2 Data Security 317
5.1.3 Modularity of Technical Systems 317
5.1.4 Fusion of Shopfloor- and Office-IT 317
5.1.5 Integration of People: Socio-Technical Systems 318
5.2 Scientific Requirements 319
5.2.1 Reliability of Adaptive, Learning Systems 319
5.2.2 Integration of Risk Management for Non-deterministic Systems 319
5.2.3 Model-Based Cooperation: Contradictions, Incompleteness, Failures 320
5.2.4 Emergent Patters 320
5.2.5 Conflicting Data and Information 320
6 Conclusion 321
References 322
Communication and Networking 324
12 Communication and Networking for the Industrial Internet of Things 325
1 Introduction 325
2 Communication in Industrial Automation 326
2.1 Definitions 326
2.2 Current Trends in Industrial Communications 328
2.3 Challenges for the Industrial Internet of Things 329
3 Communication Within a Local Automation Cell 330
3.1 Diversity Schemes 331
3.1.1 Frequency Diversity 331
3.1.2 Time Diversity 332
3.1.3 Spatial Diversity 332
3.2 Medium Access Control 333
3.2.1 Performance Metrics with Regard to the IIoT 333
3.2.2 Example: IEEE 802.15.4 334
4 Communication Within the Factory Hall and Beyond 335
4.1 Routing and Addressing Background 336
4.2 Challenges for Routing and Addressing in the IIoT 338
4.2.1 IPv6 and 6LoWPAN 339
4.3 Addressing and Routing in Standardized Protocol Stacks 340
5 Application Layer Communication 344
5.1 The Constrained Application Protocol 345
5.1.1 The Block Mode 346
5.1.2 The Observer Mode 348
5.1.3 CoAP and Proxies 348
5.2 Cloud and Distributed Processing 350
6 Conclusion and Outlook 350
References 351
13 Communications for Cyber-Physical Systems 355
1 Introduction 355
2 Data Communication Networks 357
3 Types of Communication Networks for CPSs 360
4 Impact of Communication Network Deficiencies 363
5 Reliable Communications Within CPSs 365
6 Approaches to Improve Communications Reliability 366
6.1 Network Provided QoS Provisioning 367
6.2 Redundancy 367
6.3 New Network Generations 368
7 CPS Communications Using the Internet 369
8 Communication Standards for CPSs 371
9 Communication Patterns for CPSs 374
9.1 Request-Response 375
9.2 Discovery 375
9.3 Publish-Subscribe 376
10 Conclusion 376
References 377
Artificial Intelligence and Data Analytics for Manufacturing 381
14 Application of CPS in Machine Tools 382
1 Motivation for CPS in the Manufacturing Environment 382
2 State of the Art—Literature Review 384
2.1 Characteristics of Cyber-Physical Systems in Manufacturing 384
2.2 Classification of Intelligent Objects in the Machining Process 385
2.3 Definition of “Real Time” in the Context of Manufacturing 388
2.4 Derivation of Requirements 389
3 Approach to Information Distribution 390
4 Solutions in the Area of Machining 392
4.1 Intelligent Chuck for Turning Machine 392
4.1.1 Phase I: Company Management Level—Analysis of the Turning Process 393
4.1.2 Phase II: Production Management Level—Scheduling and Situational Production Control 393
4.1.3 Phase III: Shop Floor—Development of a Chuck Control 394
4.2 Intelligent Milling Tool 395
4.2.1 Phase I: Company Management Level—Smart Analytics 396
4.2.2 Phase II: Production Management Level—Automatic Configuration 397
4.2.3 Phase III: Shop Floor—Development of the Intelligent Tool 399
5 Evaluation and Classification 401
5.1 Validation of the Developed CPS 401
5.2 Evaluation of the Defined Requirements 403
5.3 Classification of the Developed Solutions 404
6 Summary 405
References 405
15 Going Smart—CPPS for Digital Production 408
1 Introduction 408
2 Technology Knowledge for Digital Production 409
2.1 Sensors—Perception Organs of CPPS 410
2.1.1 Concepts of Sensor Integration and Fusion 411
2.1.2 Applications in Production Environments 413
2.2 CPPS—The Architecture for Smart Applications 420
2.2.1 Technology Models—Knowledge Carriers of Production Entities 421
2.2.2 Tech Apps—The CPPS Human Machine Interface 423
3 Summary 426
Acknowledgments 428
References 428
16 Manufacturing Cyber-Physical Systems (Industrial Internet of Things) 430
1 Introduction 430
2 Preconditions and Standards 431
3 Challenges 433
3.1 Steps During the Tank Wagon Loading 433
3.2 Activities on Top of the Filling Plant 434
3.3 Activities on the Underside of the Filling Plant 434
3.4 Opening of the Tank Wagon 435
4 Requirements from the Manual Process to the Automation 436
5 Solution Concept (Available Technologies, Intelligent IT, CPS Development) 437
5.1 Structure of the System 437
5.2 Process of the Automatic Opening of the Dome Cover 438
5.3 Vision System 439
5.4 Gripper System 441
5.5 Robot System 441
5.6 Security System 442
5.6.1 Risks 442
5.6.2 Technical and Complementary Protective Actions 443
5.7 Total System: Dome Cover Opening System 444
5.8 Flow and Operating Concept 447
6 Research Challenges 448
7 Conclusions 450
References 451
17 Cyber-Physical System Intelligence 453
1 Introduction 453
2 Autonomy of Cyber-Physical Systems (CPS) in a Smart Factory 455
2.1 Components for CPS Autonomy 455
3 Achieving CPS Autonomy in Smart Factories 458
4 The RoboCup Logistics League (RCLL) 462
5 Case Studies on Task-Level Executives for the RCLL 464
5.1 CLIPS-Based Agent Program 464
5.2 OpenPRS 467
5.3 YAGI 469
5.4 Common Behavioral Architecture 469
6 Evaluation 470
6.1 RoboCup 2014 Evaluation Using the CLIPS-Based Agent 470
6.2 Automated Simulation Tournament for CLIPS and OpenPRS 472
7 Conclusion 474
Acknowledgments 474
References 475
18 Big Data and Machine Learning for the Smart Factory—Solutions for Condition Monitoring, Diagnosis and Optimization 479
1 Introduction 479
2 Big Data in CPSs 480
3 Requirements and Challenges to Data Quality 482
4 Condition Monitoring and Diagnosis 483
4.1 Anomaly Detection Using Identified Hybrid Timed Automata 483
4.2 Identification of Behavior Models Using Map/Reduce Technology 484
4.3 Condition Monitoring in Continuous Processes 485
5 System Optimization 487
6 Smart Services and Applications 488
7 Summary and Outlook 490
References 491
19 Overview of the CPS for Smart Factories Project: Deep Learning, Knowledge Acquisition, Anomaly Detection and Intelligent User Interfaces 492
1 Introduction 492
2 Technical Infrastructure 493
2.1 Deep Learning 494
2.2 Knowledge Acquisition 495
2.3 Anomaly Detection 496
2.4 Intelligent User Interfaces 498
3 Use Cases 501
3.1 Industrial Robots and Anomaly Modeling 501
3.2 Anomaly Treatment in the Steel Domain 502
3.3 Outlook: Anomaly Detection in the Energy Domain 505
Acknowledgments 507
References 507
20 Applying Multi-objective Optimization Algorithms to a Weaving Machine as Cyber-Physical Production System 510
1 Introduction 510
2 Approach and Implementation of Multi-objective Self-optimization Procedure 512
2.1 Signal Processing 512
2.2 Measurement Technology of Warp Tension 514
2.3 Measurement Technology for Energy Consumption 514
2.3.1 Air Consumption Measurement 514
2.3.2 Active Power Measurement 514
2.3.3 Measurement Technology for Fabric Quality 515
2.4 Program Steps 515
3 Desirability Functions and Nelder/Mead Algorithm 516
3.1 Desirability Functions 516
3.2 Nelder/Mead Algorithm 517
3.3 Experimental Results 518
4 Conclusion and Outlook 521
References 521
21 Cyber Physical Production Control 523
1 Current Challenges of Production Control 523
2 Vision of a Cyber Physical Production Control 525
2.1 Smart Decision Support in Daily Life 525
2.2 Enabler for Decision Support Systems in Production Control 526
2.2.1 Cyber-Physical Systems 526
2.2.2 Fast Mobile Internet Infrastructure 527
2.2.3 Automatic Model Generation in Simulation Software 528
3 Data Analytics Enable Cyber Physical Production Control 529
3.1 Data Quality as an Enabler of Cyber Physical Production Control 532
3.2 Descriptive Analytics 534
3.3 Diagnostic Analytics 535
3.4 Predictive Analytics 537
3.5 Prescriptive Analytics 538
3.6 Adjusting Production 539
4 Summary and Outlook 539
Acknowledgments 540
References 540
22 A Versatile and Scalable Production Planning and Control System for Small Batch Series 544
1 Introduction 544
1.1 Industry 4.0 Based on Cyber-Physical Systems 546
1.2 Smart Factory Versus Smart Logistics 547
1.3 Area of Conflict: Deterministic Planning Versus Decentralized Control 548
1.4 Overview of Structure 549
2 Conceptual Approach 549
3 Exploiting Flexibility Potentials 552
4 Production Planning 555
5 Production Control 557
6 Conclusion and Outlook 559
Acknowledgments 560
References 560
Evolution of Workforce and Human-Machine Interaction 563
23 CPS and the Worker: Reorientation and Requalification? 564
1 Enter the Process Worker 564
2 The Opening of the Lights-Out Factory 565
3 What Skills Now? 566
4 The (Temporary?) Return of the Gods 567
5 The New Factory: Connecting the Dots 568
6 Worker and Management: Converging Roles? 570
7 Conclusion: Moving up the Ladder 571
References 573
24 Towards User-Driven Cyber-Physical Systems—Strategies to Support User Intervention in Provisioning of Information and Capabilities of Cyber-Physical Systems 576
1 Introduction 576
2 Background 578
2.1 Cyber-Physical Systems 578
2.2 Solutions for User Intervention in Cyber-Physical Systems 579
2.3 Architectures for Cyber-Physical Systems 580
2.4 Requirements of User Intervention for Cyber-Physical System Architecture 581
3 Example—A User-Driven Cyber Physical Production System 581
4 Strategies for User-Driven Cyber-Physical Systems 584
4.1 Strategies for User Intervention in Behaviour of CPS 585
4.2 Strategies for User Intervention in Behaviour of Supporting Services 586
5 Architecture for a User-Driven Cyber-Physical System 588
5.1 End User Viewpoint 588
5.2 CPS Developer Viewpoint 590
6 Discussion 591
7 Conclusions 593
References 593
25 Competence Management in the Age of Cyber Physical Systems 595
1 Companies in the Age of Industrie 4.0 595
2 Cyber Physical Systems 597
3 Competencies and Competence Management 599
3.1 Defining Individual and Organizational Competencies 599
3.2 Classification and Measuring of Competencies 601
3.2.1 Competence Classification 601
3.2.2 Competence Measurement 602
4 Consequences and New Competence Requirements for Employees Through CPS Complexity 604
5 Development of a Measurement Instrument for Competencies in the Age of CPS 608
6 Conclusions 610
References 610
Adjacent Fields and Ecosystems 615
26 Cyber-Physical Systems for Agricultural and Construction Machinery—Current Applications and Future Potential 616
1 Introduction 616
2 Challenges 617
2.1 Challenges in Agricultural Machinery 618
2.2 Challenges in Construction Machinery 619
3 CPS for Mobile Machines 619
4 Data 621
5 Key Technologies 623
6 Key Algorithms 625
7 Exemplary Processes 628
7.1 Construction Process 628
7.1.1 Conclusion CPS in Construction Machinery and Future Potential 632
7.2 Agricultural Process 633
7.2.1 Conclusion CPS in Agricultural Machinery and Future Potential 636
8 Conclusion 637
References 638
27 Application of CPS Within Wind Energy—Current Implementation and Future Potential 645
1 Motivation 645
2 Potential in Wind Energy 648
2.1 Wind Turbine 649
2.2 Wind Farm 649
2.3 Grid and Local Smart Grid 650
2.4 Community Interests 651
3 CPS in Wind Energy 651
3.1 Current Applications of CPS in Wind Energy 652
3.1.1 Condition Monitoring System 652
3.1.2 Supervisory Control and Data Acquisition 653
3.1.3 Smart Grid and the Integration of Wind Energy 654
3.1.4 Interaction of Wind Energy and Storage Systems 655
3.2 Future CPS in Wind Energy 657
3.2.1 Wind Turbine/Farm 657
3.2.2 Wind Data 659
3.2.3 Supply Predictor 660
3.2.4 Operation and Condition Analyzer 660
3.2.5 Demand Predictor/Power Requirement 661
3.2.6 Storage 661
3.2.7 Electricity Exchange 662
3.2.8 Community Interests 663
3.2.9 Central Analyzer and Optimizer 663
3.2.10 Operation and Maintenance Controller 664
3.2.11 Energy Controller 665
4 Outlook and Conclusions 666
References 666
28 Transfer Printing for Cyber-Manufacturing Systems 669
1 Introduction 669
2 Fundamentals of Transfer Printing 670
2.1 Basic Concepts in Transfer Printing 671
2.2 Advanced Transfer Printing Techniques 673
3 Opportunities of Transfer Printing for Cyber-Manufacturing Systems 675
3.1 Stretchable Electronics 676
3.2 Dissolvable Electronics 677
3.3 Opportunities of Transfer Printing Enabled Devices for Cyber-Manufacturing Systems 679
4 Challenges of Transfer Printing for Cyber-Manufacturing Systems 680
5 Future Scope 683
Acknowledgments 684
References 684
29 Advanced Manufacturing Innovation Ecosystems: The Case of Massachusetts 689
1 Introduction 689
2 Definition of Key Terms 691
3 Trends in Advanced Manufacturing 693
4 The Competitive Position of Manufacturing in Massachusetts 694
5 The Massachusetts Manufacturing Innovation Ecosystem 697
5.1 OEMs Within the Manufacturing Innovation Ecosystem 699
5.2 SMEs Within the Manufacturing Innovation Ecosystem 701
5.3 Universities in the Manufacturing Innovation Ecosystem 703
5.4 Startups in the Manufacturing Innovation Ecosystem 705
6 Manufacturing Intermediaries 707
7 Recommendations to Improve the Innovation Ecosystem 708
7.1 Advanced Manufacturing Strategy and Agenda 708
7.2 Collaboration with OEMs to Drive Innovation and Upgrade SME Capabilities 709
7.3 Technological and Managerial Support for Innovation in SMEs 709
7.4 Connections Between Startups and the Innovation Ecosystem 710
8 Conclusion 711
References 712
30 Erratum to: Industrial Internet of Things 714
Erratum to:& #6