
"This book presents the latest developments in the field of reliability science focusing on applied reliability, probabilistic models and risk analysis. It provides readers with the most up-to-date developments in this field and consolidates research activities in several areas of applied reliability engineering. The publication is timed to commemorate Boris Gnedenko's centennial by bringing together leading researchers, scientists, and practitioners in the field of Prof. Gnednko's expertise. The Introduction, written by Prof. Igor Ushakov, a personal friend and a colleague of Boris Gnedenko, explains the significant impact and contribution Gnedenko's work made on the reliability theory and the modern reliability practice. The book covers conventional and contemporary (recently emerged) topics in reliability science, which have seen extended research activities in the recent years. These topics include: degradation analysis and multi-state system reliability
physics of failures and reliability demonstration. All of these topics present a great interest to researchers and practitioners, having been extensively researched in the past years and covered at a large number of international conferences and in a multitude of journal articles. This book pulls together this information with a coherent flow of chapters, and is written by the lead scientists, researchers and practitioners in their respective fields. Logically divided into five sections, each contains several chapters covering theoretical and practical issues, while case studies support the topics under discussion"--
Elektronische Ressource probabilistic models and statistical inference editors Ilia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, Andre Kleyner
Includes bibliographical references and index
statistical inference in reliability, and
Online-Ressource (xxxv, 413 pages
networks and large scale systems
maintenance models
online resource
620/.00452
Mathematical models, Reliability (Engineering), Risk assessment
Mathematical models, Reliability (Engineering), Risk assessment
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