Lei / Li Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

1. Auflage 2022
ISBN: 978-981-16-9131-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 281 Seiten

Reihe: Engineering

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

Zielgruppe

Research

Autoren/Hrsg.

Lei, Yaguo

Li, Naipeng

Li, Xiang

Inhaltsverzeichnis

Introduction and Background.- Traditional Intelligent Fault Diagnosis.- Hybrid Intelligent Fault Diagnosis Methods.- Deep Learning-Based Intelligent Fault Diagnosis.- Data-Driven RUL Prediction.- Data-Model Fusion RUL Prediction.

Über Autor(innen)

Yaguo Lei is a full professor in School of Mechanical Engineering at Xi’an Jiaotong University (XJTU), P. R. China, which he joined as an associate professor in 2010. Prior to that, he worked at the University of Alberta, Canada, as a postdoctoral research fellow. He ever worked at the University of Duisburg-Essen, Germany, as an Alexander von Humboldt fellow in 2012. He was promoted to full professor in 2013. He received the B.S. and the Ph.D. degrees both in Mechanical Engineering from XJTU, in 2002 and 2007, respectively. He is an associate editor or a member of the editorial boards of more than ten journals, including IEEE Transactions on Industrial Electronics, Mechanical Systems and Signal Processing, Measurement Science & Technology, and Neural Computing & Applications. He is also a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the International Society of Engineering Asset Management (ISEAM), a senior member of IEEE and a member of ASME, respectively. He has pioneered many signal processing techniques, intelligent fault diagnosis methods, and remaining useful life prediction models for mechanical systems.

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, respectively. Prior to joining Xi’an Jiaotong University, he was a postdoctoral fellow in Intelligent Maintenance Systems Center at University of Cincinnati, USA, and an associate professor at Northeastern University, P. R. China. He was also a visiting scholar in School of Engineering at University of California, Merced, USA, from 2015 to 2016. His research interests include industrial artificial intelligence, industrial big data, and machinery fault diagnosis and prognosis. He is an early career advisory board member of IEEE/CAA Journal of Automatica Sinica, and an editor of three international journals.