Stochastic Models in Reliability Engineering (eBook, PDF)

Redaktion: Cui, Lirong; Lisnianski, Anatoly; Frenkel, Ilia

• Format: PDF

Produktbeschreibung

This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working on the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, which have seen extended research activates in the recent years.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 482
• Erscheinungstermin: 29. Juli 2020
• Englisch
• ISBN-13: 9781000094596
• Artikelnr.: 59806134

Autorenporträt

Lirong Cui is a professor in the School of Management & Economics at Beijing Institute of Technology. He received his Ph.D. degree in Probability and Statistics from the University of Wales, UK, in 1994. He has worked on quality and reliability-related problems since 1986 and published more than 120 papers and technical reports. He has served as an associate editors for several journals. In 2005, he was awarded the New Century Excellent Talents in China. His recent research interests are in stochastic modeling, Hawkes processes, reliability engineering, optimization, operations research, and applications of probability and statistics in various fields. Ilia Frenkel, PhD, Researcher in the Center for Reliability and Risk Management, SCE - Shamoon College of Engineering, Beer Sheva, Israel He has obtained M.Sc. degree in Applied Mathematics from Voronezh State University, Russia, and PhD degree in Operational Research and Computer Science from Institute of Economy, Ukrainian Academy of Science, formerly USSR. He has more then 40 years of academic experience and teaching in Universities and Institutions of Russia and Israel. From 1988 till 1991 he worked as Department Chair and Associate Professor in the Applied Mathematics Department and at Computers Department, Volgograd Civil Engineering Institute, Russia .From 2001 he wes served as Senior Lecturer of the Industrial Engineering and Management Department and from 2005 as Chair of the Center for Reliability and Risk Management, SCE-Shamoon College of Engineering, Beer Sheva, Israel. In 2018 he was retired. He has specialized in applied statistics and reliability with application to preventive maintenance. He published two books and more than 50 scientific articles and book chapters in the fields of reliability, applied statistics and production and operation management. He is an Editor and a member of editorial board of scientific and professional journals . Anatoly Lisnianski, PhD, Expert-engineer in the Reliability Department of The Israel Electric Corporation Ltd., Israel Anatoly Lisnianski received his M.Sc. degree in Electrical Engineering from the University of Information Technologies, Precision Mechanics and Optics, Sankt-Petersburg, Russia; and Ph.D. degree in Reliability in 1984 from Federal Scientific and Production Center ''Aurora'' in Sankt-Petersburg. From 1975 through 1989, he was a researcher at this center. Starting in 1991 he is working in the Reliability Department of the Israel Electric Corporation as an engineering expert. He is specialized in reliability assessment and optimization for complex technical systems. From 2005 he wes served as Scientific Supervisor of the Center for Reliability and Risk Management, SCE-Shamoon College of Engineering, Beer Sheva, Israel. He is the author and co-author of more than 120 journal papers, three books, some book chapters, and inventions in the field of reliability and applied statistics. He is a senior member of IEEE and a member of Israel Statistical Association.

Inhaltsangabe

Reliability Analysis of A Pseudo Working Markov Repairable System.
System Reliability Assessment with Multivariate Dependence Models.
Reliability Modeling of Multi-Phased Linear Consecutively Connected
Systems.
A Method for Complex Multistate Systems Reliability Analysis Based on
Compression Inference Algorithm and Bayesian Network.
Reliability Analysis of Demand-Based Warm Standby System with Multi-State
Common Bus.
An Upside-Down Bathtub Shaped Failure Rate Model Using DUS Transformation
of Lomax Distribution.
Reliability Analysis of Multi-State Systems with Dependent Failures Based
on Copula.
Modelling and Inference for Special Types of Semi-Markov Processes.
Weighted Multi-Attribute Acceptance Sampling Plans.
Reliability Assessment for Systems Suffering Common Cause Failure Based on
Bayesian Networks and Proportional Hazards Model.
Early Warning Strategy of Sparse Failures for Highly Reliable Products
Based on Bayesian Method.
Fault Detection and Prognostics of Aero Engine by Sensor Data Analytics.
Stochastic Modeling of Opportunistic Maintenance for Series Systems with
Degrading Components.
On Censored and Truncated Data in Survival Analysis and Reliability Models.
Analysis of Node Resilience Measures for Network Systems.
Reliability Analysis of General Purpose Parts for Special Vehicles Based on
Durability Testing Technology.
State of Health Prognostics of Lithium-Ion Batteries.
Life Prediction of Device Based on Material's Microstructure Evolution by
Means of Computational Materials Science.
Low-Cycle Fatigue Damage Assessment of Turbine Blades Using A
Substructure-Based Reliability Approach.
Phased-Mission Modeling of Physical Layer Reliability for Smart Homes.
Comparative Reliability Analysis of Different Traction Drive Topologies for
A Search-and-Rescue Helicopter.
Reliability and Fault Tolerance Assessment of Different Operation Modes of
Air Conditioning Systems for Chemical Laboratories.
Dependability Analysis of Ship Propulsion Systems.
Application of Markov Reward Processes to Reliability, Safety, Performance
Analysis of Multi-State Systems with Internal and External Testing.
Multi-Objective Maintenance Optimization of Complex System Based on
Redundant-Cost Importance.
Which Replacement Maintenance Policy is Better for Multi-State Systems:
Policy T or Policy N?
Design of Multi-Stress Accelerated Life Testing Plans Based on D-Optimal
Experimental Design.
An Extended Optimal Replacement Policy for a Simple Repairable Modelling.