E-maintenance (eBook)

Kenneth Holmberg is a research professor in tribology, condition monitoring and operational reliability at the VTT Technical Research Centre of Finland. He is the author and editor of several books and has published more than 150 scientific papers, mainly in the areas of tribology, surface engineering, lubrication, operational reliability, maintenance and diagnostics. Professor Holmberg has given 36 invited keynote lectures at international conferences and has been responsible for organising major international conferences in tribology, monitoring and diagnostics. He is vice president of the International Tribology Council and was president of the OECD IRG Wear group 1992-2006, and chairman of the European COST 516&532 TRIBOLOGY joint research actions 1995-2007. He is Chief Engineer Councillor at the Supreme Administrative Court of Finland and a frequently used expert in the European Community and European Science Foundation research actions and programmes. Professor Holmberg is on the editorial board of five scientific journals and is frequently consulted for industrial contracts and R&D projects. Adam Adgar received his PhD in control engineering from the University of Sunderland where he worked for many years. He is an associate member of the Institution of Chemical Engineers and his main research interests are process modelling and control; artificial neural networks; water treatment; statistical process control; and condition monitoring. He is currently a senior lecturer at Teesside University . Aitor Arnaiz has a PhD in computing technologies, related to condition monitoring tasks, from the University of Sunderland. He is currently working as senior researcher and head of Tekniker's Unit of Technologies for Diagnostics and Prediction in Eibar, Spain. His current professional interests are intelligent systems in monitoring, prediction and diagnosis; machine learning; uncertainty management; and Bayesian networks. Erkki Jantunen has a PhD from Helsinki University of Technology. He works as a senior research scientist in the knowledge centre of Smart Machines at the VTT Technical Research Centre of Finland. His research interests are related to information technology applications in maintenance engineering, condition monitoring, machine diagnostics and prognostics, and signal analysis. Julien Mascolo works for the FIAT Research Centre in Turin, Italy. He is a manager in the Infomobility Business Line for projects related to the optimisation of industrial processes: manufacturing, logistics and product development process. In the FIAT Sectors (IVECO, CNH and FIAT Group Automobiles) he is involved in the development of mobility and productivity services based on telematics, and on product lifecycle management. Samir Mekid received his PhD in Precision Engineering. He is a member and chartered engineer (CEng) of IMechE. He was a lecturer at UMIST, then The University of Manchester (UK), and is currently associate professor at KFUPM (KSA). He is engaged in multidisciplinary research activities, including precision machine design, instrumentation, sensors and metrology.

Preface 5
Acknowledgements 7
Contents 9
Contributors 17
Abbreviations 21
1 Introduction 26
References 28
2 Maintenance Today and Future Trends 29
2.1 State of the Art in Management 29
2.2 Integrated Programmes and Planning Processes 32
2.2.1 Reliability-centred Maintenance 32
2.2.2 Total Productive Maintenance 33
2.2.3 Total Quality Maintenance 33
2.3 Strategies 34
2.3.1 Run-to-failure 35
2.3.2 Time-based Maintenance 36
2.3.3 Opportunity Maintenance 38
2.3.4 Design Out 38
2.3.5 Condition-based Maintenance 39
2.3.6 Summary 40
2.4 Maintenance Information and Control Systems 41
2.4.1 Features of the Typical Maintenance System: from SME to Global Enterprises 41
2.4.2 Limitations to the Penetration of Integrated Systems 42
2.5 State of the Art in Technology 43
2.5.1 Computing Tools 43
2.5.2 Measurement Tools and Services 44
2.5.3 Portable Instruments 45
2.5.4 Laboratory-based Services 47
2.6 New Paradigms: Customisation and Sustainability 47
2.7 New Developments in Decision Making 49
2.8 New Developments in Technological Tools 50
2.8.1 Wireless Sensors 50
2.8.2 Miniaturisation, Cost Reduction and MEMS 52
2.8.3 Disruptive Technologies and the Future 55
2.8.4 Pervasive Sensing and Intelligence 57
2.9 Conclusions 59
References 60
3 Information and Communication Technologies Within E-maintenance 62
3.1 Introduction 62
3.2 Introduction to E-maintenance 63
3.2.1 Maintenance Today: What Are the Main Issues? 64
3.2.2 E-maintenance: Towards a Consensus or a Lot of Different Definitions? 66
3.2.3 E-maintenance: a Symbiosis Between Maintenance Services and Maintenance Technologies 67
3.3 ICT for E-maintenance 68
3.3.1 Miniaturisation Technologies for Data Acquisition 69
3.3.1.1 New Sensor Systems 69
3.3.1.2 Smart PDAs and Mobile Devices 70
3.3.1.3 Ubiquitous Computing 71
3.3.2 Standards for Data and Information Communication 72
3.3.2.1 Wireless Standards and Technologies 72
3.3.2.2 OSA-CBM Architecture 75
3.3.2.3 MIMOSA Protocols and OSA-EAI Architecture 76
3.3.3 Data and Information Processing and the Impact of Machine Learning Systems 78
3.4 Conclusions 81
References 81
4 A New Integrated E-maintenance Concept 84
4.1 Introduction 84
4.2 E-maintenance Scenario Analysis 85
4.3 DynaWeb Integrated Solution 87
4.3.1 Standards and Technologies for Data Interoperability 89
4.3.2 Implementing the Solution 91
4.4 Intelligent Sensors 94
4.5 Information and Communication Infrastructure 96
4.6 Cost-effectiveness Based Decision Support System 100
4.7 DynaWeb Demonstrations 102
4.8 Conclusions 104
References 105
5 Intelligent Wireless Sensors 106
5.1 Introduction 106
5.1.1 Fundamental Definitions 106
5.1.1.1 Definition of an Intelligent Sensor or Smart Transducer 106
5.1.1.2 Effectiveness of Conventional Sensors 107
5.1.2 Benefits of Using Intelligent Sensors 108
5.1.3 Businesses Driven Development of Intelligent Sensors 109
5.2 State-of-the-art Intelligent Sensors 110
5.2.1 Several Functions Within One Platform 111
5.2.2 Hardware 112
5.2.3 Wireless RF Standards 114
5.2.4 Intelligent Sensor Networks 117
5.3 Expected Features and Design of Intelligent Sensors 118
5.3.1 Conventional Sensors 118
5.3.2 Examples of Application of Conventional Sensors 119
5.3.2 Expected Features of Intelligent Sensors 120
5.3.2.1 Applications in Engineering Areas 121
5.3.2.2 Future Directions for Intelligent Sensors 122
5.3.3 Processing Capacity Offered by the Use of Intelligent Sensors 123
5.3.4 General Design Requirements for Intelligent Sensors 126
5.3.4.1 Quantifiable Requirements 127
5.3.4.2 Unquantifiable Requirements 128
5.4 Hardware Requirements for Wireless Sensors 129
5.4.1 Hardware Components 130
5.4.1.1 Analogue-to-digital Converter Unit 130
5.4.1.2 Sensing Unit 130
5.4.1.3 Power Sources 131
5.4.1.4 Housekeeping and Information Processing 132
5.4.2 ZigBee as a Suggested Communication Technology 134
5.4.2.1 ZigBee Interference 135
5.4.2.2 Network Topologies Offered by ZigBee Protocol 135
5.4.2.3 Performance and Network Reliability Assessment 137
5.5 Power Reduction Methods Available in ZigBee Protocol 140
5.5.1 Orthogonal Signalling – Used for 2.45 GHz 141
5.5.2 Warm-up Power Loss – DSSS 141
5.5.3 Transmitting and Receiving 142
5.5.4 Recovery Effect in Batteries 142
5.5.5 Cost Based Routing Algorithm – Link Quality and Hop Count 142
5.5.6 Power Consumption Tests 143
5.6 Conclusions 143
References 144
Bibliography 145
6 MEMS Sensors 147
6.1 Introduction 147
6.2 State-of-the-art of MEMS 152
6.3 Characteristics of MEMS Sensors 155
6.4 Specification of Multi-MEMS Sensor Platform 158
6.4.1 Introduction 158
6.4.2 Objectives 159
6.4.3 Possible Profiles of Intelligent Sensors 160
6.4.3.1 Autonomy Intelligent Sensor – Profile 1 160
6.4.3.2 Cooperation Intelligent Sensor – Profile 2 161
6.4.3.3 Slave Master Intelligent Sensor – Profile 3 162
6.4.3.4 Simplest Intelligent Sensor – Profile 4 162
6.5 Simulation of a Multi-MEMS Sensor Platform 167
6.5.1 Sensing Unit 167
6.5.2 Processing Unit 169
6.5.3 Hardware Implementation 170
6.5.4 Data Sampling 172
6.5.5 Local Decision Making Based on Condition 173
6.5.6 Threshold with Event Triggering 174
6.5.7 Data Pre-processing 176
6.5.8 Transmission on Intervals 178
6.6 Power Management 181
6.6.1 Sleep Mode 181
6.6.2 Performance versus Power Consumption 182
6.6.3 Energy Harvesting System 183
6.6.4 Energy Transducers 183
6.6.4.1 Piezo Film 183
6.6.4.2 Piezo Buzzer 184
6.6.4.3 Piezoelectric Fibre Composites 184
6.6.4.4 Electromagnetic Generators 185
6.6.4.5 Solar Panels 186
6.6.4.6 Heating Transducer 186
6.6.5 Energy Converting and Storing Subsystems 187
6.6.6 Implementation of an Energy Harvester 190
6.6.6.1 Hardware Structure and Implementation 190
6.6.6.2 The Work Process of the System 191
6.7 Conclusions 193
References 193
7 Lubricating Oil Sensors 194
7.1 Introduction 194
7.2 State-of-the-art 195
7.2.1 Oxidation 195
7.2.2 Viscosity 196
7.2.3 Corrosion 197
7.2.4 Water 197
7.2.5 Particles 197
7.2.6 Others 198
7.3 New Sensor Developments 198
7.3.1 Detection of Solid Contaminants 198
7.3.1.1 Fibre Optic Solid Contaminant Sensors 198
7.3.1.2 Particle Sensors 205
7.3.2 Water Detection 208
7.3.2.1 Water Sensor Development 208
7.3.3 Lubrication Deterioration by Ageing 213
7.4 Conclusions 215
References 216
8 Smart Tags 218
8.1 Introduction 218
8.2 Overview of the Technology 219
8.2.1 Technical Basics 219
8.2.1.1 RFID Tags 221
8.2.1.2 RFID Smart Labels 221
8.2.1.3 Tagging Mode (Active versus Passive) 221
8.2.1.4 Read-only versus Read-write 222
8.2.1.5 RFID Readers 223
8.2.1.6 Key Attributes 223
8.2.2 RFID Software Considerations 224
8.2.3 RFID Standards 225
8.2.4 Costs Involved 226
8.2.5 Advantages and Disadvantages 226
8.2.6 Privacy Issues 227
8.2.7 Applications for RFID 228
8.3 Real-time Locating Systems Using Active RFID 229
8.3.1 Time of Arrival 229
8.3.2 Time Difference of Arrival 230
8.3.3 Angle of Arrival 231
8.3.4 Received Signal Strength Induction 232
8.3.5 LANDMARC 233
8.4 Background to Applications of RFID 233
8.5 Review of RFID Applications in Maintenance 234
8.6 Applications and Scenarios 235
8.6.1 Tools 237
8.6.2 Spare Parts 237
8.6.3 Machines 238
8.6.4 Personnel 238
8.7 Smart Tag Demonstrators 238
8.7.1 Inventory Tracking (Passive) 239
8.7.2 Asset Identification and Query System for PDAs (Passive) 240
8.7.3 Mobile Assets Positioning System (Active) 242
8.8 Conclusions 245
References 246
Bibliography 246
9 Mobile Devices and Services 247
9.1 Introduction 248
9.2 Mobile Devices in Maintenance Management 249
9.3 Role of PDA Within DynaWeb 250
9.4 Description of Typical PDA Usage Scenario in Maintenance Operations 253
9.5 Wireless Communication 258
9.6 Technical Requirements 259
9.7 Practical Limitations Today 259
9.8 Mobile User Interface Issues 260
9.9 Trends 262
9.10 Conclusions 265
References 265
10 Wireless Communication 267
10.1 Introduction 267
10.2 State-of-the-art 270
10.2.1 WLANs (IEEE 802.11) 270
10.2.2 Bluetooth (IEEE 802.15.1) 276
10.2.3 ZigBee (IEEE 802.15.4) 279
10.2.4 Assessment of Previous Technologies to Support E-maintenance Applications 282
10.2.5 Conclusions 286
10.3 New Developments 286
10.3.1 Wireless Gateway 287
10.3.2 Wireless Collector 290
10.4 Conclusions and Recommendations 291
References 291
11 Semantic Web Services for Distributed Intelligence 293
11.1 Introduction 293
11.2 State-of-art in Application of the Semantic Web to Industrial Automation 294
11.2.1 What Is Ontology? 294
11.2.2 Advantages of Semantic Web Techniques 294
11.2.2.1 Improved Web Search 295
11.2.2.2 Better Integration 295
11.2.2.3 Lexicon Flexibility and Standardisation 295
11.2.2.4 Composition of Complex Systems 296
11.2.3 Semantic Web Languages 296
11.2.4 Semantic Web Platforms 297
11.2.4.1 Protégé 2000 297
11.2.4.2 Altova Semantic Works 2008 297
11.2.4.3 SMORE 299
11.2.5 Semantic Web Development in Industrial Automation 300
11.2.5.1 OntoServ.NET 300
11.2.5.2 Obelix 300
11.2.5.3 Rewerse 301
11.2.5.4 Knowledge Web 301
11.2.5.5 Other Related Works 302
11.3 Web Services for Dynamic Condition Based Maintenance 302
11.3.1 Web Service for Condition Monitoring 307
11.3.2 Web Service for Diagnosis Based on Vibration and Oil Data 308
11.3.3 Web Service for Prognosis 309
11.3.3.1 Proportional Hazard Model 311
11.3.3.2 Exponential Curve Fitting 311
11.3.4 Web Service for Scheduling 312
11.3.5 Testing Web Services 313
11.4 Conclusions 315
References 315
12 Strategies for Maintenance Cost-effectiveness 317
12.1 Introduction 318
12.2 Development of Strategies for Cost-effectiveness 318
12.2.1 Theoretical Background 319
12.2.1.1 Maintenance Related Economic Factors 320
12.2.1.2 Diagnosis Techniques 321
12.2.1.3 Maintenance Management IT-systems 322
12.2.2 The Role of Maintenance in Company Business 324
12.3 Development of a Maintenance Decision Support System (MDSS) 327
12.3.1 Objectives of MDSS 328
12.3.2 MDSS Toolsets and Tools 329
12.3.2.1 Accurate Maintenance Decisions: PreVib, ProFail and ResLife 332
12.3.2.2 Maintenance Cost-effectiveness: MMME and MainSave 346
12.3.3.3 Simulation of the Most Cost-effective Solution (AltSim) 356
12.4 Conclusions 361
References 362
13 Dynamic and Cost-effective Maintenance Decisions 365
13.1 Introduction 366
13.2 MDSS for Dynamic and Cost-effective Maintenance Decisions 366
13.2.1 Deterministic and Probabilistic Approaches 367
13.2.2 Dynamic and Cost-effective Maintenance Decisions 369
13.2.3 Application Scenario of MDSS 371
13.3 Data Required to Run MDSS 374
13.3.1 Datasets 374
13.3.2 Data Gathering 381
13.4 Database Required for MDSS 382
13.4.1 MDSS Data Model 382
13.4.2 Mapping to Company Data Models 385
13.4.3 Mapping to CRIS/MIMOSA 387
13.4.4 CRIS/MIMOSA Database User-interface 389
13.4.5 Test of CRIS/MIMOSA Database User-interface 391
13.5 Case Studies for Applying MDSS 392
13.5.1 Toolset 1: PreVib, ProFail and ResLife 392
13.5.2 Toolset 2: AltSim 397
13.5.3 Toolset 3: MMME and MainSave 404
13.6 Results and Discussions 407
13.7 Conclusions 408
References 409
14 Industrial Demonstrations of E-maintenance Solutions 411
14.1 Global Demonstration in a Milling Machine Environment 413
14.1.1 Objectives of the Test and Demonstrations 414
14.1.2 Description of the Test Platform 416
14.1.3 Description of the DynaWeb Components Tested 417
14.1.3.1 Smart Tags and PDA Support 417
14.1.3.2 Handheld PDA Vibration Data Collector 422
14.1.3.3 Vibration Measurement System 423
14.1.3.4 Oil Sensors 425
14.1.3.5 Communication 429
14.1.3.6 MDSS 432
14.1.4 Economical Evaluation 435
14.1.5 Conclusions 436
14.2 Foundry Hydraulic System Demonstrator 437
14.2.1 Objectives of the Test and Demonstrations 438
14.2.2 Description of the Test Platform 438
14.2.3 Description of the DynaWeb Components Tested 439
14.2.3.1 Sensor Measuring Oxidation of the Lubricant by Spectroscopy of Visible Light 439
14.2.3.2 TESSnet Platform 441
14.2.3.3 Data Storage in the Global MIMOSA Database 442
14.2.4 Reference Measurements and Software 444
14.2.5 Results 444
14.2.5.1 Sensor Measuring Oxidation of the Lubricant by Spectroscopy of Visible Light 444
14.2.5.2 Data Storage and Communication 445
14.2.6 Technical Evaluation 445
14.2.7 Economical Evaluation 446
14.2.8 Conclusions and Recommendations 446
14.2.8.1 Conclusions 446
14.2.8.2 Recommendations 447
14.3 Automatic Strip Stamping and Cutting Machine Demonstrator 448
14.3.1 Objectives of the Test and Demonstrations 451
14.3.1.1 Isolated Validation 452
14.3.1.2 Integrated Validation 453
14.3.2 Description of the Test Platform 453
14.3.3 Description of the DynaWeb Components Tested 455
14.3.4 Reference Testing Procedure 459
14.3.4.1 Procedure for Internal Tests 459
14.3.4.2 Procedure for Integration Tests 461
14.3.5 Results 465
14.3.5.1 Preliminary Tests 466
14.3.5.2 Internal Tests 467
14.3.5.3 Integration Tests 467
14.3.6 Conclusions 469
14.4 Machine Tool Demonstrator 470
14.4.1 Objectives of the Test and Demonstrations 470
14.4.2 Description of the Test Platform 471
14.4.3 Description of the DynaWeb Components Tested 473
14.4.4 Reference Measurements/Software 477
14.4.5 Results 479
14.4.6 Technical Evaluation 479
14.4.7 Economical Evaluation 480
14.4.8 Conclusions and Recommendations 480
14.5 Maritime Lubrication System Demonstrator 481
14.5.1 Objectives of the Test and Demonstrations 482
14.5.2 Description of the Test Platform 483
14.5.2.1 The Sampling System 484
14.5.2.2 Testing Method 485
14.5.2.3 Technical Specifications of the Test Rig 485
14.5.3 Description of the DynaWeb Components Tested 486
14.5.4 Reference Measurements/Software 488
14.5.5 Results of the Demonstration 489
14.5.6 Technical Evaluation 490
14.5.7 Economical Evaluation 490
14.5.8 Conclusions 492
References 493
15 E-training in Maintenance 495
15.1 Introduction 495
15.2 The Need for Maintenance E-training 496
15.3 E-learning Technologies 498
15.3.1 Adaptive Learning 499
15.3.2 Learning Objects, Standards and Interoperability 501
15.3.3 Learning Management Systems 505
15.3.4 Moodle LMS 508
15.3.5 Advanced Learning Technologies 510
15.3.6 Vocational Training in Maintenance 511
15.4 E-training for E-maintenance 513
15.4.1 Dynamite E-training: the DynaTrain Platform 513
15.4.2 Vibration Sensing 514
15.4.3 Data Acquisition 517
15.4.4 Inventory Tracking System 519
15.4.5 Prognosis Web Services 520
15.4.6 MIMOSA Translator 521
15.5 Conclusions 523
References 524
16 Conclusions and Future Perspectives 527