Selective Maintenance Modelling and Optimization

Basic Methods and Some Recent Advances

de

, ,

Éditeur :

Springer


Paru le : 2023-01-20



eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
116,04

Téléchargement immédiat
Dès validation de votre commande
Ajouter à ma liste d'envies
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

Description
This book is a detailed introduction to selective maintenance and updates readers on recent advances in this field, emphasizing mathematical formulation and optimization techniques. The book is useful for reliability engineers and managers engaged in the practice of reliability engineering and maintenance management. It also provides references that will lead to further studies at the end of each chapter. This book is a reference for researchers in reliability and maintenance and can be used as an advanced text for students.
Pages
192 pages
Collection
n.c
Parution
2023-01-20
Marque
Springer
EAN papier
9783031173226
EAN PDF
9783031173233

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
4915 Ko
Prix
116,04 €
EAN EPUB
9783031173233

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
21074 Ko
Prix
116,04 €

Dr. Yu Liu is a Full Professor of Industrial Engineering with the School of Mechanical and Electrical Engineering at the University of Electronic Science and Technology of China. He received his Ph.D. degree in Mechatronics Engineering from the University of Electronic Science and Technology of China. He was a Visiting Pre-doctoral Fellow in the Department of Mechanical Engineering at Northwestern University, USA, from 2008 to 2010, and a Postdoctoral Research Fellow in the Department of Mechanical Engineering at the University of Alberta, Canada, from 2012 to 2013. He has published over 70 peer-reviewed papers in international journals. His research interests include system reliability modelling and analysis, maintenance optimization, prognostics and health management, and design under uncertainty. Prof. Liu has been recognized as one of the Most Cited Chinese Researchers by Elsevier since 2016. He was a recipient of the National Science Fund for Excellent Young Scholars. He has served as the Vice President of the Reliability Committee of Operations Research Society of China since 2022. He is an ISEAM Fellow and an Associate Editor of IISE Transactions and IEEE Transactions on Reliability. 


Dr. Hong-Zhong Huang is a Full Professor of Mechanical Engineering with the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China. He is also Director of the Center for System Reliability and Safety, at the University of Electronic Science and Technology of China. He has held visiting appointments at several universities in the USA, Canada, and Asia. He has authored or co-authored more than 350 journal papers and eight books in the fields of reliability engineering, optimization design, fuzzy sets theory, and product development. Prof. Huang is an ISEAM Fellow, Technical Committee Member of ESRA, Co-Editor-in-Chief for the International Journal of Reliability and Applications, and Editorial Board Member of several international journals. Prof. Huang has been recognized as one of the Most Cited Chinese Researchers by Elsevier since 2014.


Dr. Tao Jiang received his Ph.D. degree in Mechanical Engineering from the University of Electronic Science and Technology of China, Chengdu, China in 2022. He was a Research Assistant in the Department of Systems Engineering and Engineering Management at the City University of Hong Kong in 2016 and a Visiting Pre-doctoral Fellow in the Department of Industrial Systems Engineering and Management at National University of Singapore from 2019 to 2021. He has published over 20 papers in international journals, conferences, and edited books. His research interests include system reliability evaluation and maintenance optimization. 

Suggestions personnalisées