TY - BOOK
T1 - Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
AU - Lei, Yaguo
AU - Li, Naipeng
AU - Li, Xiang
N1 - Publisher Copyright:
© Xi’an Jiaotong University Press 2023.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Data-model fusion
KW - Deep learning
KW - Industrial big data
KW - Intelligent fault diagnosis
KW - Remaining useful life
KW - Rotating machinery
KW - Transfer learning
UR - https://www.scopus.com/pages/publications/85148022802
U2 - 10.1007/978-981-16-9131-7
DO - 10.1007/978-981-16-9131-7
M3 - 书
AN - SCOPUS:85148022802
SN - 9789811691300
BT - Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
PB - Springer Nature
ER -