Prognostics and Health Management for Intelligent Electromechanical Systems

Erscheinungsjahr 2025
ISBN: 978-981-967217-2
Verlag: Springer

Buch, Englisch, 208 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 558 g

This book gives a detailed introduction to the technical background, feature extraction methods, PHM models and big data embedding methods of the big data theory in PHM for intelligent electromechanical systems. Combination with deep learning and big data, this book explains the hybrid algorithm framework of PHM such as ensemble intelligence and optimized intelligence and introduces PHM models for bearing, IGBT, MOSFET and other components and their big data embedding platform. This book improves the PHM method and theory of electromechanical system under industrial big data and provides reference for the development of intelligent electromechanical equipment and intelligent industrial production in the future.

Zielgruppe

Research

Autoren/Hrsg.

Liu, Hui

Cheng, Fang

Li, Yanfei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik

Inhaltsverzeichnis

Introduction.- Feature extraction of bearing vibration signal.- Ensemble intelligent diagnosis for bearing faults.- Deep learning based prediction for bearing remaining useful life.-Optimization based prediction for IGBT remaining useful life.

Über Autor(innen)

Hui Liu is Professor of Artificial Intelligence and Smart Cities, Central South University, China; Vice Dean, Faculty of Traffic and Transportation Engineering, Central South University, China. He has double Ph.D. degrees from China and Germany (Ph.D. in Traffic & Transportation Engineering from Central South University of China in 2011/Ph.D. in Automation Engineering from Rostock University of Germany in 2013). He obtained his habilitation degree (professorship degree) on Automation Engineering from University of Rostock, Germany, in 2016. He had been employed as the BMBF Junior Group Leader of Ministry of Education & Research of Germany at CELISCA (Center for Life Science Automation), University of Rostock, Germany, since 01/2015 until 12/2016. He authorized 40 China invention patents in the field of data science, air quality monitoring and time series modeling, as the first inventor. He won many reputable research awards, including the second prize for Natural Science Research by Ministry of Education of China in 2017, the first prize for National Scientific and Technological Progress Award of China in 2018, the first prize for Scientific and Technology Award of China Railway Society in 2018, etc. He also received Elsevier Applied Energy Most Highly Cited Research Paper Award (20 papers globally selected in 2015), national outstanding doctoral thesis nomination awards of China, Hunan outstanding doctoral thesis of China, Baosteel Education Award, Mao Yisheng Railway Award and other personal honours.

Ms. Fang Cheng is now pursuing Ph.D. at Central South University, Changsha, China. Her research interests include machine learning for predictive health management.

Yanfei Li is Associate Professor, Shennong Chair Professor, College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, China. Prof. Li published 2 monographs, 45 SCI/EI papers, including 27 JCR Q1 journal papers, 2 ESI 1% highly-cited papers, and authorized 10 China national invention patents as the first inventor as well. She leaded several important research projects, including China National Key Research and Development Project, Human Natural Science Foundation Project. She won the second prize of the Natural Science Award of Ministry of Education in 2017, the first prize of the Hunan New Energy Science and Technology Progress Award in 2020, the Logistics Technology Innovation Award of China Federation of Logistics and Purchasing in 2021 and the Transportation and Logistics Innovation Award of China Highway and Transportation Society in 2020.