Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems (PDF)
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This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era.
Features:
- Addresses the critical challenges in the field of PHM at present
- Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis
- Provides abundant experimental validations and engineering cases of the presented methodologies
Naipeng Li is currently an assistant professor in School of Mechanical Engineering at Xi'an Jiaotong University, P. R. China. He received the B.S. degree in Mechanical Engineering from Shandong Agricultural University, P. R. China, in 2012, and the Ph.D. degree in Mechanical Engineering from Xi'an Jiaotong University, P. R. China, in 2019. He was also a visiting scholar of Georgia Institute of Technology, Atlanta, USA, from 2016 to 2018. His research interests include condition monitoring, intelligent fault diagnostics, and RUL prediction of rotating machinery.
Xiang Li is currently an associate professor in School of Mechanical Engineering at Xi'an Jiaotong University, P. R. China. He received the B.S. and Ph.D. degrees both in Mechanics from Tianjin University, P. R. China, in 2012 and 2017,
- Autoren: Yaguo Lei , Naipeng Li , Xiang Li
- 2022, 1st ed. 2023, 281 Seiten, Englisch
- Verlag: Springer Nature Singapore
- ISBN-10: 9811691312
- ISBN-13: 9789811691317
- Erscheinungsdatum: 19.10.2022
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- Dateiformat: PDF
- Größe: 12 MB
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