Reliability Modeling in Industry 4.0

Erscheinungsjahr 2023
ISBN: 978-0-323-99204-6
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 430 Seiten, Format (B × H): 230 mm x 153 mm, Gewicht: 898 g

Reliability Modeling with Industry 4.0 explores the emerging theoretical and practical developments in reliability engineering in highly digitized industries, including power, computer systems, railway systems, and robotics. Drawing on leading research from around the globe, as well as the latest in industry practice, this book provides cutting edge advice on how to integrate a fully digitized industry 4.0 system for enhanced reliability and reduced maintenance cost. Technologies such as big data, artificial intelligence, and the industrial internet of things are addressed in the context of reliability engineering, providing practical advice on applications.

Zielgruppe

<p>Researchers in industry and academia interested in reliability and industry 4.0 </p>

Autoren/Hrsg.

Xing, Liudong

Ram, Mangey

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Zuverlässigkeitstechnik

Inhaltsverzeichnis

1.Reliability analysis and maintenance optimization for the cost-based component maintenance priority with Industry 4.0
2.System reliability in IoT-based data collecting systems using low-cost particulate matter sensors
3.Reliability and risk analysis in critical infrastructure protection
4.Applied issues of chaotic dynamics in the management of unique evolutionary systems
5.Safety-critical railway systems
6.Supporting digital transformation in Micro and Small Enterprise (MSE): An operational framework
7.Modeling and object recognition skill transfer in industrial intelligent robots
8.Systems reliability for industrial multivariate processes: A comparative approach
9.Predicting vulnerability discovery processes in an operating system: Stochastic modeling approach
10.Efficiency of condensing thermal power plant as a complex system-An algorithm for assessing and improving energy efficiency and reliability during operation and maintenance

11.Maintenance and safety of industrial systems: Developed model for assessing the criticality of elements of technical systems
12.Risk-informed decision-making: Overview and applications
13.Digital Transformation of Engineering Education for Smart Education: A systematic literature review
14.MSS principles and application
15.On the combined m-consecutive-k-out-of-n: F and consecutive kc-out-of-n: F reliability system: Some advances
16.MIRCE Science approach to real-time prediction of fleet reliability with Industry 4.0
17.Reliability assessment of an electrified regional commuter train in greater Munich area

Über Autor(innen)

Xing, Liudong
Liudong Xing is currently a professor at the Department of Electrical and Computer Engineering, University of Massachusetts, Dartmouth, United States. Her research interests include reliability and resilience modeling, analysis, and optimization of complex systems and networks (e.g., IoT and wireless sensor networks). Prof. Xing has received multiple teaching and scholar awards and was corecipient of the Best (Student) Paper Award at several international conferences and journals. She has published over 290 journal articles, and 2 books titled Binary Decision Diagrams and Extensions for System Reliability Analysis and Dynamic System Reliability: Modeling and Analysis of Dynamic and Dependent Behaviors. She has served as an associate editor or editorial board member of multiple journals, including Reliability Engineering & System Safety, IEEE Internet of Things Journal, IEEE Access, and International Journal of Mathematical, Engineering and Management Sciences. She is a fellow of the International Society of Engineering Asset Management and a senior member of IEEE.