Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy

1st Edition - June 22, 2023
Editors: Fausto Pedro Garcia Marquez, Mayorkinos Papaelias, Valter Luiz Jantara Junior
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 6 6 6 - 2
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 1 0 0 - 5

Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maint… Read more

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Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maintenance to ensure their high availability and uninterrupted operation. Presenting the main concepts, state-of-the-art advances and case studies, this book approaches the topic by considering it as an integral part of the overall operation of any wind energy project. Linking the essential NDT subject with its sub disciplines, the book uses computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques to support analysis of prognostic problems with defined constraints and requirements.

This book is the first of its kind and will provide useful insights to industrial engineers and scientists, academics and students in the possibilities that NDT and condition monitoring technologies can offer.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Biographies
Foreword
Preface
Introduction to non-destructive testing and condition monitoring techniques in wind energy
Background and purpose of the book
Target audience
1. SCADA-based fault detection in wind turbines: data-driven techniques and applications
1. SCADA-based condition monitoring
2. Early fault detection with normal behavior models and SCADA data
3. Case study: early fault detection in gear bearings with multi-target normal behavior models and SCADA data
2. Fault detection in small wind turbines using condition monitoring techniques and machine learning algorithms (a predictive approach)
1. Introduction
2. Related works
3. Condition monitoring techniques
4. System design
5. Machine learning algorithms
6. Implementation
7. Results and discussion
8. Conclusion
3. Prediction and classification of different wind turbine alarms using K-nearest neighbors
1. Introduction
2. Approach
3. Case study and results
4. Conclusions
4. Artificial neural networks applied for wind turbines maintenance management in unmanned aerial vehicle acoustic inspection case
1. State of the art
2. Wind turbines acoustic inspection case
3. Artificial neural networks application
4. Results
5. Conclusions
5. Quantitative interlink wear estimation method for the mooring chain
1. Introduction
2. Proposed interlink wear estimation method
3. Application to a spar-type floating structure moored with three catenary lines
4. Wear estimation considering the rolling motion between adjacent chain links
5. Conclusions
Appendix A: Newly designed wear test machine possible to use mooring chain surface under high pressure
6. Enhanced sparse representation-based intelligent recognition framework for fault diagnosis of wind turbine drive trains
1. Introduction
2. Related works
3. Enhanced sparse representation-based intelligent recognition framework
4. Experiment validations
5. Conclusions
7. An optimal combined production and maintenance policies for a wind farm with environmental and operational considerations
1. Introduction
2. Literature review
3. Problem setting and notations
4. Description of production and maintenance policies
5. Mathematical formulation of the production strategy
6. Mathematical formulation of maintenance strategies
7. Optimization algorithms
8. Numerical examples
9. Conclusion
8. The valuation of geothermal power projects in Indonesia using real options valuation
1. Introduction
2. Literature review
3. Methodology
4. Result and discussion
5. Conclusion
9. A robust multiple open-switch fault diagnosis approach for converter in wind energy system
1. Introduction
2. A brief literature review and motivation
3. System description and fault analysis
4. Fault diagnostic method
5. Simulation result and analysis
6. Conclusion
7. Funding statement
10. Condition monitoring in wind turbines: a review
Abbreviations
1. Introduction
2. Requirements for condition monitoring in WECS
3. Features extracted for condition monitoring in WECS
4. Technologies for condition monitoring in WECS
5. Research challenges
6. Conclusion
11. Artificial intelligence techniques and cloud computing for wind turbine pitch bearing fault detection
1. Introduction
2. Temporal convolutional augmented Bayesian search (TCABS)
3. Discussion for TCABS
4. Fault diagnosis
5. Simulation
6. Experiments
7. Damage validation
8. Conclusion
12. Alarms and false-alarm analysis by support vector machine in wind turbines
1. Introduction
2. Approach
3. Case study and results
4. Conclusions
13. Background, advancement, and applications of in situ structural health monitoring based on different modes of failure detection in composites: a review
Abbreviations
1. Introduction
2. Nanomaterials and in situ SHM
3. Applications of advanced in-situ SHM in the detection of specific modes of failures in composites
4. Computational modeling and in-situ SHM
5. Conclusion
Data availability statement
Conflicts of interest
14. Numerical simulations of offshore wind farms considering accidental scenarios
Abbreviations
1. Introduction
2. Scenarios of accidents and accidental loads for offshore wind farms
3. Numerical simulations of offshore wind turbines with internal accidents
4. Numerical simulations of wind turbines with external accidents
5. Concluding remarks and future outlook
15. Multibody dynamic analysis of onshore horizontal-axis wind turbine
1. Introduction
2. Overview of the chapter
3. Mathematical model
4. Numerical simulation
5. Conclusion
6. Appendix: coordinate transformation
16. Foundation monitoring system of offshore wind turbines
1. Introduction
2. Numerical simulation of scouring effect on dynamic characteristics of offshore wind turbine
3. Numerical simulation of scouring effect on dynamic responses of offshore wind turbines
4. Real-time monitoring system for scouring around monopile foundations of offshore wind turbines
5. Real-time uneven settlement and stress monitoring systems for jacket-foundation offshore wind turbines
6. Conclusion
Index

Fausto Pedro Garcia Marquez

Professor Fausto works as Professor at Universidad De Castilla-La Mancha, Spain. Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute. He has published more than 150 papers and is author and editor of 31 books (Elsevier, Springer, Pearson, Mc-GrawHill, Intech, IGI, Marcombo, AlfaOmega). He is Editor of 5 Int. Journals, Committee Member more than 40 Int. Conferences. He has been Principal Investigator in 4 European Projects, 6 National Projects, and more than 150 projects for Universities, Companies, etc. His main interests are: Artificial Intelligence, Maintenance, Management, Renewable Energy, Transport, Advanced Analytics, Data Science. He is an expert in the European Union in AI4People (EISMD), and ESF and Director of www.ingeniumgroup.eu.

Affiliations and expertise

Professor, Universidad De Castilla-La Mancha, Spain

Mayorkinos Papaelias

Mayorkinos Papaelias is a Reader in NDT and Condition Monitoring in the School of Metallogy and Materials at the University of Birmingham, UK. Dr Papaelias leads the research activity in Non-Destructive Testing and Structural Health Condition Monitoring at the Birmingham Railway Centre for Research and Education and conducts research in structural health condition monitoring of wind turbine towers, and advanced condition monitoring of wind turbine gearboxes and rotating machinery. He served as a technical consultant to TWI, ENGITEC, Innovative Technology and Science Ltd, and Instituto de Soldadura e Qualidade. He is editor of two books on fault detection and condition monitoring, and has contributed chapters to books in fault detection and rail inspection. Mayorkinos is chairman of the Education Committee of the International Society for Condition Monitoring of the British Institute of Non-Destructive Testing.

Affiliations and expertise

Reader in NDT and Condition Monitoring, School of Metallogy and Materials, University of Birmingham, UK

Valter Luiz Jantara Junior

Dr. Valter is a postdoctoral research fellow at the school of metallurgy and materials at the University of Birmingham, UK. His main research interests are damage modelling, finite element analysis, failure analysis, surface engineering, condition monitoring, and advanced material characterisation. He has been distinguished with the Len Gelman Award for the best paper by a person in the early stages of their career, in the Proceedings of the First World Congress on Condition Monitoring, 2017. He is a member of the Institute of Materials, Minerals & Mining (IOM3). He has experienced in the in the following areas: electron (including FEG, EBSD, EDS) and optical microscopy; tensile and fatigue testing; tribological testing; plasma nitriding, including DC and active screen; vibration analysis; acoustic emission monitoring; and modelling of materials.

Affiliations and expertise

Postdoctoral Research Fellow, School of Metallurgy and Materials, University of Birmingham, UK

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Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy

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Fausto Pedro Garcia Marquez

Mayorkinos Papaelias

Valter Luiz Jantara Junior