Authors:
- Provides the foundations for an in-depth understanding of reliability engineering
- Includes illustrations, exercises, and case studies to aid learning
- Presents real-world applications of the general theory discussed
Part of the book series: Springer Series in Reliability Engineering (RELIABILITY)
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Table of contents (38 chapters)
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Front Matter
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Introduction, Background and Overview
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Front Matter
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Risk Identification and Qualitative Analyses
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Front Matter
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Modelling of Static Systems. Boolean Approaches
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Front Matter
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About this book
This book provides, as simply as possible, sound foundations for an in-depth understanding of reliability engineering with regard to qualitative analysis, modelling, and probabilistic calculations of safety and production systems.
Drawing on the authors’ extensive experience within the field of reliability engineering, it addresses and discusses a variety of topics, including:
• Background and overview of safety and dependability studies;
• Explanation and critical analysis of definitions related to core concepts;
• Risk identification through qualitative approaches (preliminary hazard analysis, HAZOP, FMECA, etc.);
• Modelling of industrial systems through static (fault tree, reliability block diagram), sequential (cause-consequence diagrams, event trees, LOPA, bowtie), and dynamic (Markov graphs, Petri nets) approaches;
• Probabilistic calculations through state-of-the-art analytical or Monte Carlo simulation techniques;
• Analysis, modelling, and calculations of common cause failure and uncertainties;
• Linkages and combinations between the various modelling and calculation approaches;
• Reliability data collection and standardization.
The book features illustrations, explanations, examples, and exercises to help readers gain a detailed understanding of the topic and implement it into their own work. Further, it analyses the production availability of production systems and the functional safety of safety systems (SIL calculations), showcasing specific applications of the general theory discussed. Given its scope, this book is a valuable resource for engineers, software designers, standard developers, professors, and students.
Authors and Affiliations
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Total Professeurs Associés, Sedzère, France
Jean-Pierre Signoret
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Montreuil, France
Alain Leroy
About the authors
Alain Leroy started his career in offshore engineering and was for ten years with the insurance industry. However, he has worked for most of his career in reliability engineering, focusing mainly on the implementation and use of novel approaches and techniques for assessing dependability parameters in the oil and gas industry. He has delivered courses on reliability at both basic and high levels.
Jean-Pierre Signoret has been in charge of the research and development of methods and tools in reliability engineering, as well as of practical studies related to reliability modelling. He is still involved in the development of several international standards, including IEC and ISO, related to dependability, functional safety, reliability data collection, and production assurance. As a member of TPA, he gives short courses in various universities on this topic.
Bibliographic Information
Book Title: Reliability Assessment of Safety and Production Systems
Book Subtitle: Analysis, Modelling, Calculations and Case Studies
Authors: Jean-Pierre Signoret, Alain Leroy
Series Title: Springer Series in Reliability Engineering
DOI: https://doi.org/10.1007/978-3-030-64708-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-64707-0Published: 24 March 2021
Softcover ISBN: 978-3-030-64710-0Published: 24 March 2022
eBook ISBN: 978-3-030-64708-7Published: 23 March 2021
Series ISSN: 1614-7839
Series E-ISSN: 2196-999X
Edition Number: 1
Number of Pages: XXXIV, 878
Number of Illustrations: 595 b/w illustrations
Topics: Quality Control, Reliability, Safety and Risk, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Markov model, Bayesian Inference, Simulation and Modeling