Prognostics and Remaining Useful Life (RUL) Estimation: Predicting with Confidence
Autor Diego Galar, Kai Goebel, Peter Sandborn, Uday Kumaren Limba Engleză Paperback – 4 oct 2024
FEATURES
- Presents a compendium of RUL estimation methods and technologies used in predictive maintenance
- Describes different approaches and prognosis methods for different assets
- Includes a comprehensive compilation of methods from model-based and data-driven to hybrid
- Discusses the benchmarking of RUL estimation methods according to accuracy and uncertainty, depending on the target application, the type of asset, and the forecast performance expected
- Contains a toolset of methods and a way of deployment aimed at a versatile audience
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Specificații
ISBN-13: 9780367563097
ISBN-10: 0367563096
Pagini: 489
Ilustrații: 406
Dimensiuni: 178 x 254 mm
Greutate: 0.9 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States
ISBN-10: 0367563096
Pagini: 489
Ilustrații: 406
Dimensiuni: 178 x 254 mm
Greutate: 0.9 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States
Public țintă
AcademicNotă biografică
Dr. Diego Galar is Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova.
He is also principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport.
He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance.
In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA) and the Universidad Pontificia Católica de Chile. Currently, he is visiting professor in University of Sunderland (UK), University of Maryland (USA), and Chongqing University in China.
Dr. Kai Goebel is a Principal Scientist in the System Sciences Lab at Palo Alto Research Center (PARC). His interest is broadly in condition-based maintenance and systems health management for a broad spectrum of cyber-physical systems in the transportation, energy, aerospace, defense, and manufacturing sectors. Prior to joining PARC, Dr. Goebel worked at NASA Ames Research Center and General Electric Corporate Research & Development center. At NASA, he was a branch chief leading the Discovery and Systems Health tech area which included groups for machine learning, quantum computing, physics modeling, and diagnostics & prognostics. He founded and directed the Prognostics Center of Excellence which advanced our understanding of the fundamental aspects of prognostics. He holds 18 patents and has published more than 350 papers, including a book on Prognostics. Dr. Goebel was an adjunct professor at Rensselaer Polytechnic Institute and is now adjunct professor at Lulea Technical University. He is a co-founder of the Prognostics and Health Management Society, and associate editor of the International Journal of PHM.
Peter Sandborn is a Professor in the CALCE Electronic Products and Systems Center and the Director of the Maryland Technology Enterprise Institute at the University of Maryland. Dr. Sandborn’s group develops life-cycle cost models and business case support for long field life systems. This work includes: obsolescence forecasting algorithms, strategic design refresh planning, lifetime buy quantity optimization, return on investment models for maintenance planning and system health management, and outcome-based contract design and optimization. Dr. Sandborn is the developer of the MOCA refresh planning tool. Dr. Sandborn is an Associate Editor of the IEEE Transactions on Electronics Packaging Manufacturing and a member of the Board of Directors of the PHM Society. He is the author of over 200 technical publications and several books on electronic packaging and electronic systems cost analysis. He was the winner of the 2004 SOLE Proceedings, the 2006 Eugene L. Grant, the 2017 ASME Kos Ishii-Toshiba, and the 2018 Jacques S. Gansler awards. He has a B.S. degree in engineering physics from the University of Colorado, Boulder, in 1982, and the M.S. degree in electrical science and Ph.D. degree in electrical engineering, both from the University of Michigan, Ann Arbor, in 1983 and 1987, respectively. He is a Fellow of the IEEE, the ASME and the PHM Society.
Dr. Uday Kumar is the Chair Professor of Operation and Maintenance Engineering, Director of Research and Innovation (Sustainable Transport) at Luleå University of Technology and Director of Luleå Railway Research Center.
His teaching, research & consulting interests are equipment maintenance, reliability and maintainability analysis, product support, Life Cycle Costing(LCC) , Risk analysis, system analysis, eMaintenance, asset management etc.
He is visiting faculty at the Center of Intelligent Maintenance System(IMS)- a centre sponsored by National Science Foundation , Cincinnati, USA since 2011, External examiner and Program Reviewer for Reliability and Asset Management Program of The University of Manchester, Distinguished visiting Professor at Tsinghua University Beijing, Honorary Professor at Beijing Jiaotong University, Beijing, etc. Earlier he has been visiting faculty at Imperial College London, Helsinki University of Technology, Helsinki, Univ of Stavanger , Norway, etc.
He has more than 30 years of experiences in consulting and finding solutions to industrial problems directly or indirectly related to maintenance of engineering asserts. He has published more than 300 papers in International Journals and Conference Proceedings dealing with various aspects of maintenance of engineering systems, and has co-authored 4 books on Maintenance Engineering and contributed to World Encyclopaedia on Risk Management.
He is an elected member of Royal Swedish Academy of Engineering Sciences.
He is also principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport.
He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance.
In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA) and the Universidad Pontificia Católica de Chile. Currently, he is visiting professor in University of Sunderland (UK), University of Maryland (USA), and Chongqing University in China.
Dr. Kai Goebel is a Principal Scientist in the System Sciences Lab at Palo Alto Research Center (PARC). His interest is broadly in condition-based maintenance and systems health management for a broad spectrum of cyber-physical systems in the transportation, energy, aerospace, defense, and manufacturing sectors. Prior to joining PARC, Dr. Goebel worked at NASA Ames Research Center and General Electric Corporate Research & Development center. At NASA, he was a branch chief leading the Discovery and Systems Health tech area which included groups for machine learning, quantum computing, physics modeling, and diagnostics & prognostics. He founded and directed the Prognostics Center of Excellence which advanced our understanding of the fundamental aspects of prognostics. He holds 18 patents and has published more than 350 papers, including a book on Prognostics. Dr. Goebel was an adjunct professor at Rensselaer Polytechnic Institute and is now adjunct professor at Lulea Technical University. He is a co-founder of the Prognostics and Health Management Society, and associate editor of the International Journal of PHM.
Peter Sandborn is a Professor in the CALCE Electronic Products and Systems Center and the Director of the Maryland Technology Enterprise Institute at the University of Maryland. Dr. Sandborn’s group develops life-cycle cost models and business case support for long field life systems. This work includes: obsolescence forecasting algorithms, strategic design refresh planning, lifetime buy quantity optimization, return on investment models for maintenance planning and system health management, and outcome-based contract design and optimization. Dr. Sandborn is the developer of the MOCA refresh planning tool. Dr. Sandborn is an Associate Editor of the IEEE Transactions on Electronics Packaging Manufacturing and a member of the Board of Directors of the PHM Society. He is the author of over 200 technical publications and several books on electronic packaging and electronic systems cost analysis. He was the winner of the 2004 SOLE Proceedings, the 2006 Eugene L. Grant, the 2017 ASME Kos Ishii-Toshiba, and the 2018 Jacques S. Gansler awards. He has a B.S. degree in engineering physics from the University of Colorado, Boulder, in 1982, and the M.S. degree in electrical science and Ph.D. degree in electrical engineering, both from the University of Michigan, Ann Arbor, in 1983 and 1987, respectively. He is a Fellow of the IEEE, the ASME and the PHM Society.
Dr. Uday Kumar is the Chair Professor of Operation and Maintenance Engineering, Director of Research and Innovation (Sustainable Transport) at Luleå University of Technology and Director of Luleå Railway Research Center.
His teaching, research & consulting interests are equipment maintenance, reliability and maintainability analysis, product support, Life Cycle Costing(LCC) , Risk analysis, system analysis, eMaintenance, asset management etc.
He is visiting faculty at the Center of Intelligent Maintenance System(IMS)- a centre sponsored by National Science Foundation , Cincinnati, USA since 2011, External examiner and Program Reviewer for Reliability and Asset Management Program of The University of Manchester, Distinguished visiting Professor at Tsinghua University Beijing, Honorary Professor at Beijing Jiaotong University, Beijing, etc. Earlier he has been visiting faculty at Imperial College London, Helsinki University of Technology, Helsinki, Univ of Stavanger , Norway, etc.
He has more than 30 years of experiences in consulting and finding solutions to industrial problems directly or indirectly related to maintenance of engineering asserts. He has published more than 300 papers in International Journals and Conference Proceedings dealing with various aspects of maintenance of engineering systems, and has co-authored 4 books on Maintenance Engineering and contributed to World Encyclopaedia on Risk Management.
He is an elected member of Royal Swedish Academy of Engineering Sciences.
Cuprins
1. Information in Maintenance 2. Predictive Maintenance Programs and Servitization Maintenance as a Service (MaaS) Creating Value through Prognosis Capabilities 3. RUL Estimation Powered by Data-Driven Techniques 4. Context Awareness and Situation Awareness in Prognostics 5. Black Swans and Physics of Failure 6. Hybrid Prognostics Combining Physics-Based and Data-Driven Approaches 7. Prognosis in Prescriptive Analytics 8. Uncertainty Management and the Confidence of RUL Predictions 9. RUL Estimation of Dynamic and Static Assets 10. Principles of Digital Twin 11. Application of Prognosis in Industry, Energy, and Transportation
Descriere
Current book compares the techniques and models used to estimate the RUL of different assets including review of the relevant literature on prognostic techniques and their use in the industrial field. It describes different approaches and prognosis methods for different assets backed up by appropriate case studies.