Holmberg / Adgar / Arnaiz E-maintenance

1. Auflage 2010
ISBN: 978-1-84996-205-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 511 Seiten

E-maintenance is the synthesis of two major trends in today's society: the growing importance of maintenance as a key technology and the rapid development of information and communication technology. E-maintenance gives the reader an overview of the possibilities offered by new and advanced information and communication technology to achieve efficient maintenance solutions in industry, energy production and transportation, thereby supporting sustainable development in society. Sixteen chapters cover a range of different technologies, such as: new micro sensors, on-line lubrication sensors, smart tags for condition monitoring, wireless communication and smart personal digital assistants. E-maintenance also discusses semantic data-structuring solutions; ontology structured communications; implementation of diagnostics and prognostics; and maintenance decision support by economic optimisation. It includes four industrial cases that are both described and analysed in detail, with an outline of a global application solution. E-maintenance is a useful tool for engineers and technicians who wish to develop e-maintenance in industrial sites. It is also a source of new and stimulating ideas for researchers looking to make the next step towards sustainable development.

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.

Autoren/Hrsg.

Holmberg, Kenneth

Adgar, Adam

Arnaiz, Aitor

Jantunen, Erkki

Mascolo, Julien

Mekid, Samir

Inhaltsverzeichnis

1;Preface;5
2;Acknowledgements;7
3;Contents;9
4;Contributors;17
5;Abbreviations;21
6;1 Introduction;26
6.1;References;28
7;2 Maintenance Today and Future Trends;29
7.1;2.1 State of the Art in Management;29
7.2;2.2 Integrated Programmes and Planning Processes;32
7.2.1;2.2.1 Reliability-centred Maintenance;32
7.2.2;2.2.2 Total Productive Maintenance;33
7.2.3;2.2.3 Total Quality Maintenance;33
7.3;2.3 Strategies;34
7.3.1;2.3.1 Run-to-failure;35
7.3.2;2.3.2 Time-based Maintenance;36
7.3.3;2.3.3 Opportunity Maintenance;38
7.3.4;2.3.4 Design Out;38
7.3.5;2.3.5 Condition-based Maintenance;39
7.3.6;2.3.6 Summary;40
7.4;2.4 Maintenance Information and Control Systems;41
7.4.1;2.4.1 Features of the Typical Maintenance System: from SME to Global Enterprises;41
7.4.2;2.4.2 Limitations to the Penetration of Integrated Systems;42
7.5;2.5 State of the Art in Technology;43
7.5.1;2.5.1 Computing Tools;43
7.5.2;2.5.2 Measurement Tools and Services;44
7.5.3;2.5.3 Portable Instruments;45
7.5.4;2.5.4 Laboratory-based Services;47
7.6;2.6 New Paradigms: Customisation and Sustainability;47
7.7;2.7 New Developments in Decision Making;49
7.8;2.8 New Developments in Technological Tools;50
7.8.1;2.8.1 Wireless Sensors;50
7.8.2;2.8.2 Miniaturisation, Cost Reduction and MEMS;52
7.8.3;2.8.3 Disruptive Technologies and the Future;55
7.8.4;2.8.4 Pervasive Sensing and Intelligence;57
7.9;2.9 Conclusions;59
7.10;References;60
8;3 Information and Communication Technologies Within E-maintenance;62
8.1;3.1 Introduction;62
8.2;3.2 Introduction to E-maintenance;63
8.2.1;3.2.1 Maintenance Today: What Are the Main Issues?;64
8.2.2;3.2.2 E-maintenance: Towards a Consensus or a Lot of Different Definitions?;66
8.2.3;3.2.3 E-maintenance: a Symbiosis Between Maintenance Services and Maintenance Technologies;67
8.3;3.3 ICT for E-maintenance;68
8.3.1;3.3.1 Miniaturisation Technologies for Data Acquisition;69
8.3.1.1;3.3.1.1 New Sensor Systems;69
8.3.1.2;3.3.1.2 Smart PDAs and Mobile Devices;70
8.3.1.3;3.3.1.3 Ubiquitous Computing;71
8.3.2;3.3.2 Standards for Data and Information Communication;72
8.3.2.1;3.3.2.1 Wireless Standards and Technologies;72
8.3.2.2;3.3.2.2 OSA-CBM Architecture;75
8.3.2.3;3.3.2.3 MIMOSA Protocols and OSA-EAI Architecture;76
8.3.3;3.3.3 Data and Information Processing and the Impact of Machine Learning Systems;78
8.4;3.4 Conclusions;81
8.5;References;81
9;4 A New Integrated E-maintenance Concept;84
9.1;4.1 Introduction;84
9.2;4.2 E-maintenance Scenario Analysis;85
9.3;4.3 DynaWeb Integrated Solution;87
9.3.1;4.3.1 Standards and Technologies for Data Interoperability;89
9.3.2;4.3.2 Implementing the Solution;91
9.4;4.4 Intelligent Sensors;94
9.5;4.5 Information and Communication Infrastructure;96
9.6;4.6 Cost-effectiveness Based Decision Support System;100
9.7;4.7 DynaWeb Demonstrations;102
9.8;4.8 Conclusions;104
9.9;References;105
10;5 Intelligent Wireless Sensors;106
10.1;5.1 Introduction;106
10.1.1;5.1.1 Fundamental Definitions;106
10.1.1.1;5.1.1.1 Definition of an Intelligent Sensor or Smart Transducer;106
10.1.1.2;5.1.1.2 Effectiveness of Conventional Sensors;107
10.1.2;5.1.2 Benefits of Using Intelligent Sensors;108
10.1.3;5.1.3 Businesses Driven Development of Intelligent Sensors;109
10.2;5.2 State-of-the-art Intelligent Sensors;110
10.2.1;5.2.1 Several Functions Within One Platform;111
10.2.2;5.2.2 Hardware;112
10.2.3;5.2.3 Wireless RF Standards;114
10.2.4;5.2.4 Intelligent Sensor Networks;117
10.3;5.3 Expected Features and Design of Intelligent Sensors;118
10.3.1;5.3.1 Conventional Sensors;118
10.3.2;5.3.2 Examples of Application of Conventional Sensors;119
10.3.3;5.3.2 Expected Features of Intelligent Sensors;120
10.3.3.1;5.3.2.1 Applications in Engineering Areas;121
10.3.3.2;5.3.2.2 Future Directions for Intelligent Sensors;122
10.3.4;5.3.3 Processing Capacity Offered by the Use of Intelligent Sensors;123
10.3.5;5.3.4 General Design Requirements for Intelligent Sensors;126
10.3.5.1;5.3.4.1 Quantifiable Requirements;127
10.3.5.2;5.3.4.2 Unquantifiable Requirements;128
10.4;5.4 Hardware Requirements for Wireless Sensors;129
10.4.1;5.4.1 Hardware Components;130
10.4.1.1;5.4.1.1 Analogue-to-digital Converter Unit;130
10.4.1.2;5.4.1.2 Sensing Unit;130
10.4.1.3;5.4.1.3 Power Sources;131
10.4.1.4;5.4.1.4 Housekeeping and Information Processing;132
10.4.2;5.4.2 ZigBee as a Suggested Communication Technology;134
10.4.2.1;5.4.2.1 ZigBee Interference;135
10.4.2.2;5.4.2.2 Network Topologies Offered by ZigBee Protocol;135
10.4.2.3;5.4.2.3 Performance and Network Reliability Assessment;137
10.5;5.5 Power Reduction Methods Available in ZigBee Protocol;140
10.5.1;5.5.1 Orthogonal Signalling – Used for 2.45 GHz;141
10.5.2;5.5.2 Warm-up Power Loss – DSSS;141
10.5.3;5.5.3 Transmitting and Receiving;142
10.5.4;5.5.4 Recovery Effect in Batteries;142
10.5.5;5.5.5 Cost Based Routing Algorithm – Link Quality and Hop Count;142
10.5.6;5.5.6 Power Consumption Tests;143
10.6;5.6 Conclusions;143
10.7;References;144
10.8;Bibliography;145
11;6 MEMS Sensors;147
11.1;6.1 Introduction;147
11.2;6.2 State-of-the-art of MEMS;152
11.3;6.3 Characteristics of MEMS Sensors;155
11.4;6.4 Specification of Multi-MEMS Sensor Platform;158
11.4.1;6.4.1 Introduction;158
11.4.2;6.4.2 Objectives;159
11.4.3;6.4.3 Possible Profiles of Intelligent Sensors;160
11.4.3.1;6.4.3.1 Autonomy Intelligent Sensor – Profile 1;160
11.4.3.2;6.4.3.2 Cooperation Intelligent Sensor – Profile 2;161
11.4.3.3;6.4.3.3 Slave Master Intelligent Sensor – Profile 3;162
11.4.3.4;6.4.3.4 Simplest Intelligent Sensor – Profile 4;162
11.5;6.5 Simulation of a Multi-MEMS Sensor Platform;167
11.5.1;6.5.1 Sensing Unit;167
11.5.2;6.5.2 Processing Unit;169
11.5.3;6.5.3 Hardware Implementation;170
11.5.4;6.5.4 Data Sampling;172
11.5.5;6.5.5 Local Decision Making Based on Condition;173
11.5.6;6.5.6 Threshold with Event Triggering;174
11.5.7;6.5.7 Data Pre-processing;176
11.5.8;6.5.8 Transmission on Intervals;178
11.6;6.6 Power Management;181
11.6.1;6.6.1 Sleep Mode;181
11.6.2;6.6.2 Performance versus Power Consumption;182
11.6.3;6.6.3 Energy Harvesting System;183
11.6.4;6.6.4 Energy Transducers;183
11.6.4.1;6.6.4.1 Piezo Film;183
11.6.4.2;6.6.4.2 Piezo Buzzer;184
11.6.4.3;6.6.4.3 Piezoelectric Fibre Composites;184
11.6.4.4;6.6.4.4 Electromagnetic Generators;185
11.6.4.5;6.6.4.5 Solar Panels;186
11.6.4.6;6.6.4.6 Heating Transducer;186
11.6.5;6.6.5 Energy Converting and Storing Subsystems;187
11.6.6;6.6.6 Implementation of an Energy Harvester;190
11.6.6.1;6.6.6.1 Hardware Structure and Implementation;190
11.6.6.2;6.6.6.2 The Work Process of the System;191
11.7;6.7 Conclusions;193
11.8;References;193
12;7 Lubricating Oil Sensors;194
12.1;7.1 Introduction;194
12.2;7.2 State-of-the-art;195
12.2.1;7.2.1 Oxidation;195
12.2.2;7.2.2 Viscosity;196
12.2.3;7.2.3 Corrosion;197
12.2.4;7.2.4 Water;197
12.2.5;7.2.5 Particles;197
12.2.6;7.2.6 Others;198
12.3;7.3 New Sensor Developments;198
12.3.1;7.3.1 Detection of Solid Contaminants;198
12.3.1.1;7.3.1.1 Fibre Optic Solid Contaminant Sensors;198
12.3.1.2;7.3.1.2 Particle Sensors;205
12.3.2;7.3.2 Water Detection;208
12.3.2.1;7.3.2.1 Water Sensor Development;208
12.3.3;7.3.3 Lubrication Deterioration by Ageing;213
12.4;7.4 Conclusions;215
12.5;References;216
13;8 Smart Tags;218
13.1;8.1 Introduction;218
13.2;8.2 Overview of the Technology;219
13.2.1;8.2.1 Technical Basics;219
13.2.1.1;8.2.1.1 RFID Tags;221
13.2.1.2;8.2.1.2 RFID Smart Labels;221
13.2.1.3;8.2.1.3 Tagging Mode (Active versus Passive);221
13.2.1.4;8.2.1.4 Read-only versus Read-write;222
13.2.1.5;8.2.1.5 RFID Readers;223
13.2.1.6;8.2.1.6 Key Attributes;223
13.2.2;8.2.2 RFID Software Considerations;224
13.2.3;8.2.3 RFID Standards;225
13.2.4;8.2.4 Costs Involved;226
13.2.5;8.2.5 Advantages and Disadvantages;226
13.2.6;8.2.6 Privacy Issues;227
13.2.7;8.2.7 Applications for RFID;228
13.3;8.3 Real-time Locating Systems Using Active RFID;229
13.3.1;8.3.1 Time of Arrival;229
13.3.2;8.3.2 Time Difference of Arrival;230
13.3.3;8.3.3 Angle of Arrival;231
13.3.4;8.3.4 Received Signal Strength Induction;232
13.3.5;8.3.5 LANDMARC;233
13.4;8.4 Background to Applications of RFID;233
13.5;8.5 Review of RFID Applications in Maintenance;234
13.6;8.6 Applications and Scenarios;235
13.6.1;8.6.1 Tools;237
13.6.2;8.6.2 Spare Parts;237
13.6.3;8.6.3 Machines;238
13.6.4;8.6.4 Personnel;238
13.7;8.7 Smart Tag Demonstrators;238
13.7.1;8.7.1 Inventory Tracking (Passive);239
13.7.2;8.7.2 Asset Identification and Query System for PDAs (Passive);240
13.7.3;8.7.3 Mobile Assets Positioning System (Active);242
13.8;8.8 Conclusions;245
13.9;References;246
13.10;Bibliography;246
14;9 Mobile Devices and Services;247
14.1;9.1 Introduction;248
14.2;9.2 Mobile Devices in Maintenance Management;249
14.3;9.3 Role of PDA Within DynaWeb;250
14.4;9.4 Description of Typical PDA Usage Scenario in Maintenance Operations;253
14.5;9.5 Wireless Communication;258
14.6;9.6 Technical Requirements;259
14.7;9.7 Practical Limitations Today;259
14.8;9.8 Mobile User Interface Issues;260
14.9;9.9 Trends;262
14.10;9.10 Conclusions;265
14.11;References;265
15;10 Wireless Communication;267
15.1;10.1 Introduction;267
15.2;10.2 State-of-the-art;270
15.2.1;10.2.1 WLANs (IEEE 802.11);270
15.2.2;10.2.2 Bluetooth (IEEE 802.15.1);276
15.2.3;10.2.3 ZigBee (IEEE 802.15.4);279
15.2.4;10.2.4 Assessment of Previous Technologies to Support E-maintenance Applications;282
15.2.5;10.2.5 Conclusions;286
15.3;10.3 New Developments;286
15.3.1;10.3.1 Wireless Gateway;287
15.3.2;10.3.2 Wireless Collector;290
15.4;10.4 Conclusions and Recommendations;291
15.5;References;291
16;11 Semantic Web Services for Distributed Intelligence;293
16.1;11.1 Introduction;293
16.2;11.2 State-of-art in Application of the Semantic Web to Industrial Automation;294
16.2.1;11.2.1 What Is Ontology?;294
16.2.2;11.2.2 Advantages of Semantic Web Techniques;294
16.2.2.1;11.2.2.1 Improved Web Search;295
16.2.2.2;11.2.2.2 Better Integration;295
16.2.2.3;11.2.2.3 Lexicon Flexibility and Standardisation;295
16.2.2.4;11.2.2.4 Composition of Complex Systems;296
16.2.3;11.2.3 Semantic Web Languages;296
16.2.4;11.2.4 Semantic Web Platforms;297
16.2.4.1;11.2.4.1 Protégé 2000;297
16.2.4.2;11.2.4.2 Altova Semantic Works 2008;297
16.2.4.3;11.2.4.3 SMORE;299
16.2.5;11.2.5 Semantic Web Development in Industrial Automation;300
16.2.5.1;11.2.5.1 OntoServ.NET;300
16.2.5.2;11.2.5.2 Obelix;300
16.2.5.3;11.2.5.3 Rewerse;301
16.2.5.4;11.2.5.4 Knowledge Web;301
16.2.5.5;11.2.5.5 Other Related Works;302
16.3;11.3 Web Services for Dynamic Condition Based Maintenance;302
16.3.1;11.3.1 Web Service for Condition Monitoring;307
16.3.2;11.3.2 Web Service for Diagnosis Based on Vibration and Oil Data;308
16.3.3;11.3.3 Web Service for Prognosis;309
16.3.3.1;11.3.3.1 Proportional Hazard Model;311
16.3.3.2;11.3.3.2 Exponential Curve Fitting;311
16.3.4;11.3.4 Web Service for Scheduling;312
16.3.5;11.3.5 Testing Web Services;313
16.4;11.4 Conclusions;315
16.5;References;315
17;12 Strategies for Maintenance Cost-effectiveness;317
17.1;12.1 Introduction;318
17.2;12.2 Development of Strategies for Cost-effectiveness;318
17.2.1;12.2.1 Theoretical Background;319
17.2.1.1;12.2.1.1 Maintenance Related Economic Factors;320
17.2.1.2;12.2.1.2 Diagnosis Techniques;321
17.2.1.3;12.2.1.3 Maintenance Management IT-systems;322
17.2.2;12.2.2 The Role of Maintenance in Company Business;324
17.3;12.3 Development of a Maintenance Decision Support System (MDSS);327
17.3.1;12.3.1 Objectives of MDSS;328
17.3.2;12.3.2 MDSS Toolsets and Tools;329
17.3.2.1;12.3.2.1 Accurate Maintenance Decisions: PreVib, ProFail and ResLife;332
17.3.2.2;12.3.2.2 Maintenance Cost-effectiveness: MMME and MainSave;346
17.3.2.3;12.3.3.3 Simulation of the Most Cost-effective Solution (AltSim);356
17.4;12.4 Conclusions;361
17.5;References;362
18;13 Dynamic and Cost-effective Maintenance Decisions;365
18.1;13.1 Introduction;366
18.2;13.2 MDSS for Dynamic and Cost-effective Maintenance Decisions;366
18.2.1;13.2.1 Deterministic and Probabilistic Approaches;367
18.2.2;13.2.2 Dynamic and Cost-effective Maintenance Decisions;369
18.2.3;13.2.3 Application Scenario of MDSS;371
18.3;13.3 Data Required to Run MDSS;374
18.3.1;13.3.1 Datasets;374
18.3.2;13.3.2 Data Gathering;381
18.4;13.4 Database Required for MDSS;382
18.4.1;13.4.1 MDSS Data Model;382
18.4.2;13.4.2 Mapping to Company Data Models;385
18.4.3;13.4.3 Mapping to CRIS/MIMOSA;387
18.4.4;13.4.4 CRIS/MIMOSA Database User-interface;389
18.4.5;13.4.5 Test of CRIS/MIMOSA Database User-interface;391
18.5;13.5 Case Studies for Applying MDSS;392
18.5.1;13.5.1 Toolset 1: PreVib, ProFail and ResLife;392
18.5.2;13.5.2 Toolset 2: AltSim;397
18.5.3;13.5.3 Toolset 3: MMME and MainSave;404
18.6;13.6 Results and Discussions;407
18.7;13.7 Conclusions;408
18.8;References;409
19;14 Industrial Demonstrations of E-maintenance Solutions;411
19.1;14.1 Global Demonstration in a Milling Machine Environment;413
19.1.1;14.1.1 Objectives of the Test and Demonstrations;414
19.1.2;14.1.2 Description of the Test Platform;416
19.1.3;14.1.3 Description of the DynaWeb Components Tested;417
19.1.3.1;14.1.3.1 Smart Tags and PDA Support;417
19.1.3.2;14.1.3.2 Handheld PDA Vibration Data Collector;422
19.1.3.3;14.1.3.3 Vibration Measurement System;423
19.1.3.4;14.1.3.4 Oil Sensors;425
19.1.3.5;14.1.3.5 Communication;429
19.1.3.6;14.1.3.6 MDSS;432
19.1.4;14.1.4 Economical Evaluation;435
19.1.5;14.1.5 Conclusions;436
19.2;14.2 Foundry Hydraulic System Demonstrator;437
19.2.1;14.2.1 Objectives of the Test and Demonstrations;438
19.2.2;14.2.2 Description of the Test Platform;438
19.2.3;14.2.3 Description of the DynaWeb Components Tested;439
19.2.3.1;14.2.3.1 Sensor Measuring Oxidation of the Lubricant by Spectroscopy of Visible Light;439
19.2.3.2;14.2.3.2 TESSnet Platform;441
19.2.3.3;14.2.3.3 Data Storage in the Global MIMOSA Database;442
19.2.4;14.2.4 Reference Measurements and Software;444
19.2.5;14.2.5 Results;444
19.2.5.1;14.2.5.1 Sensor Measuring Oxidation of the Lubricant by Spectroscopy of Visible Light;444
19.2.5.2;14.2.5.2 Data Storage and Communication;445
19.2.6;14.2.6 Technical Evaluation;445
19.2.7;14.2.7 Economical Evaluation;446
19.2.8;14.2.8 Conclusions and Recommendations;446
19.2.8.1;14.2.8.1 Conclusions;446
19.2.8.2;14.2.8.2 Recommendations;447
19.3;14.3 Automatic Strip Stamping and Cutting Machine Demonstrator;448
19.3.1;14.3.1 Objectives of the Test and Demonstrations;451
19.3.1.1;14.3.1.1 Isolated Validation;452
19.3.1.2;14.3.1.2 Integrated Validation;453
19.3.2;14.3.2 Description of the Test Platform;453
19.3.3;14.3.3 Description of the DynaWeb Components Tested;455
19.3.4;14.3.4 Reference Testing Procedure;459
19.3.4.1;14.3.4.1 Procedure for Internal Tests;459
19.3.4.2;14.3.4.2 Procedure for Integration Tests;461
19.3.5;14.3.5 Results;465
19.3.5.1;14.3.5.1 Preliminary Tests;466
19.3.5.2;14.3.5.2 Internal Tests;467
19.3.5.3;14.3.5.3 Integration Tests;467
19.3.6;14.3.6 Conclusions;469
19.4;14.4 Machine Tool Demonstrator;470
19.4.1;14.4.1 Objectives of the Test and Demonstrations;470
19.4.2;14.4.2 Description of the Test Platform;471
19.4.3;14.4.3 Description of the DynaWeb Components Tested;473
19.4.4;14.4.4 Reference Measurements/Software;477
19.4.5;14.4.5 Results;479
19.4.6;14.4.6 Technical Evaluation;479
19.4.7;14.4.7 Economical Evaluation;480
19.4.8;14.4.8 Conclusions and Recommendations;480
19.5;14.5 Maritime Lubrication System Demonstrator;481
19.5.1;14.5.1 Objectives of the Test and Demonstrations;482
19.5.2;14.5.2 Description of the Test Platform;483
19.5.2.1;14.5.2.1 The Sampling System;484
19.5.2.2;14.5.2.2 Testing Method;485
19.5.2.3;14.5.2.3 Technical Specifications of the Test Rig;485
19.5.3;14.5.3 Description of the DynaWeb Components Tested;486
19.5.4;14.5.4 Reference Measurements/Software;488
19.5.5;14.5.5 Results of the Demonstration;489
19.5.6;14.5.6 Technical Evaluation;490
19.5.7;14.5.7 Economical Evaluation;490
19.5.8;14.5.8 Conclusions;492
19.6;References;493
20;15 E-training in Maintenance;495
20.1;15.1 Introduction;495
20.2;15.2 The Need for Maintenance E-training;496
20.3;15.3 E-learning Technologies;498
20.3.1;15.3.1 Adaptive Learning;499
20.3.2;15.3.2 Learning Objects, Standards and Interoperability;501
20.3.3;15.3.3 Learning Management Systems;505
20.3.4;15.3.4 Moodle LMS;508
20.3.5;15.3.5 Advanced Learning Technologies;510
20.3.6;15.3.6 Vocational Training in Maintenance;511
20.4;15.4 E-training for E-maintenance;513
20.4.1;15.4.1 Dynamite E-training: the DynaTrain Platform;513
20.4.2;15.4.2 Vibration Sensing;514
20.4.3;15.4.3 Data Acquisition;517
20.4.4;15.4.4 Inventory Tracking System;519
20.4.5;15.4.5 Prognosis Web Services;520
20.4.6;15.4.6 MIMOSA Translator;521
20.5;15.5 Conclusions;523
20.6;References;524
21;16 Conclusions and Future Perspectives;527