Fault diagnosis and prognostics based on cognitive computing and geometric space transformation
Autor Chen Lu, Laifa Tao, Jian Ma, Yujie Cheng, Yu Dingen Limba Engleză Hardback – 11 aug 2024
Preț: 1059.32 lei
Preț vechi: 1324.16 lei
-20% Nou
Puncte Express: 1589
Preț estimativ în valută:
202.77€ • 212.86$ • 167.50£
202.77€ • 212.86$ • 167.50£
Carte nepublicată încă
Doresc să fiu notificat când acest titlu va fi disponibil:
Se trimite...
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9789819989164
ISBN-10: 9819989167
Pagini: 700
Ilustrații: Approx. 700 p. 355 illus., 302 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:2024
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9819989167
Pagini: 700
Ilustrații: Approx. 700 p. 355 illus., 302 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:2024
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
Chapter 1 Introduction.- Chapter 2 Fault diagnosis and prognostics based on deep learning and transfer learning.- Chapter 3 Fault diagnosis and health assessment based on visual cognitive computing.- Chapter 4 Fault diagnosis based on compressed sensing.- Chapter 5 Fault diagnosis and health assessment based on differential geometry.- Chapter 6 Performance degradation prediction and assessment based on geometric transformation and pattern recognition.- Chapter 7 Conclusion.
Notă biografică
Chen Lu received his PhD in Power Machinery Engineering from Dalian University of Technology. He is currently the full professor and director of Institute of Reliability Engineering at Beihang University and serves as the executive deputy director of the National Key Laboratory of Reliability and Environmental Engineering Technology (Beihang University) . Prof. Chen Lu has won a number of national and provincial leading talent titles and honors. He is Fellow of IET (Institution of Engineering and Technology) and Fellow of ISEAM (International Society of Engineering Asset Management). His research interests include fault diagnosis, prognostics and health management, and intelligent maintenance systems, where he has published more than 150 refereed papers in journals and conferences, including more than 70 in SCI-indexed journals. He has coauthored three academic books and was granted over 60 invention patents.
Laifa Tao received the BSc and PhD degrees from the School of Reliability and Systems Engineering, Beihang University, in 2010 and 2014, respectively. He is currently the full professor with the School of Reliability and Systems Engineering, Beihang University, and has been listed in national youth top talent. His research interests include fault diagnosis, prognostics, health state assessment, optimization and determination, and health management for complex systems.
Jian Ma received the BS degree in automation and the MS and PhD degrees in systems engineering from Beihang University, China, in 2009, 2012, and 2015, respectively. He was a visiting scholar with ENSMM, France, from 2018 to 2019. He is currently an associate professor with the School of Reliability and Systems Engineering, Beihang University, and has been listed in national youth top talent. His current interests of research mainly include intelligent fault diagnosis and prognostics and system health management.
Yujie Cheng received her PhD degree in the School of Reliabilityand Systems Engineering at Beihang University in 2016. She was a postdoctor in FEMTO-ST/ ENSMM, Besancon, France, sponsored by China Scholarship Council from 2017 to 2018. Now she is working as an associate professor at Beihang University. Her current interests of research are focusing on intelligent fault diagnosis, prognostics, and maintenance decision for complex systems.
Yu Ding received his PhD degree in systems engineering from Beihang University, Beijing, China, in 2019. He is currently an associate professor with the Institute of Reliability Engineering, Beihang University, Beijing, China. His research interests cover prognostics and health management, fault diagnosis, deep learning, and deep reinforcement learning. He has authored or co-authored over 20 publications in journals and received the Excellent Doctoral Dissertation Award in 2021 from the Chinese Society of Aeronautics and Astronautics.
Laifa Tao received the BSc and PhD degrees from the School of Reliability and Systems Engineering, Beihang University, in 2010 and 2014, respectively. He is currently the full professor with the School of Reliability and Systems Engineering, Beihang University, and has been listed in national youth top talent. His research interests include fault diagnosis, prognostics, health state assessment, optimization and determination, and health management for complex systems.
Jian Ma received the BS degree in automation and the MS and PhD degrees in systems engineering from Beihang University, China, in 2009, 2012, and 2015, respectively. He was a visiting scholar with ENSMM, France, from 2018 to 2019. He is currently an associate professor with the School of Reliability and Systems Engineering, Beihang University, and has been listed in national youth top talent. His current interests of research mainly include intelligent fault diagnosis and prognostics and system health management.
Yujie Cheng received her PhD degree in the School of Reliabilityand Systems Engineering at Beihang University in 2016. She was a postdoctor in FEMTO-ST/ ENSMM, Besancon, France, sponsored by China Scholarship Council from 2017 to 2018. Now she is working as an associate professor at Beihang University. Her current interests of research are focusing on intelligent fault diagnosis, prognostics, and maintenance decision for complex systems.
Yu Ding received his PhD degree in systems engineering from Beihang University, Beijing, China, in 2019. He is currently an associate professor with the Institute of Reliability Engineering, Beihang University, Beijing, China. His research interests cover prognostics and health management, fault diagnosis, deep learning, and deep reinforcement learning. He has authored or co-authored over 20 publications in journals and received the Excellent Doctoral Dissertation Award in 2021 from the Chinese Society of Aeronautics and Astronautics.
Textul de pe ultima copertă
This monograph introduces readers to new theories and methods applying cognitive computing and geometric space transformation to the field of fault diagnosis and prognostics. It summarizes the basic concepts and technical aspects of fault diagnosis and prognostics technology. Existing bottleneck problems are examined, and the advantages of applying cognitive computing and geometric space transformation are explained. In turn, the book highlights fault diagnosis, prognostic, and health assessment technologies based on cognitive computing methods, including deep learning, transfer learning, visual cognition, and compressed sensing. Lastly, it covers technologies based on differential geometry, space transformation, and pattern recognition.
Caracteristici
Includes the latest findings on fault diagnosis and prognosis for complex equipment Applies new cognitive methods to fault diagnosis, health assessment and prognoses for electromechanical products Includes a wealth of simulations, experiments and engineering cases