A collection of papers presented at the PSAM 7 – ESREL ’04 conference in June 2004, reflecting a wide variety of disciplines, such as principles and theory of reliability and risk analysis, systems modelling and simulation, consequence assessment, human and organisational factors, structural reliability methods, software reliability and safety, insights and lessons from risk studies and management/decision making. This volume covers both well-established practices and open issues in these fields, identifying areas where maturity has been reached and those where more development is needed.
In addition to presenting methodology, it shows how to identify accident vulnerability in the two industries. It reviews the causes of the two major nuclear accidents and many fatal accidents in the chemical industry, including Bhopal. Many examples of applications of PSA to both industries are presented."--BOOK JACKET. "Problems are included at the end of many chapters with answers at the back of the book."--Jacket.
Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed.
Over the past 30 years, numerous concerns have been raised in the literature regarding the capability of static modeling approaches such as the event-tree (ET)/fault-tree (FT) methodology to adequately account for the impact of process/hardware/software/firmware/human interactions on nuclear power plant safety assessment, and methodologies to augment the ET/FT approach have been proposed. Often referred to as dynamic probabilistic risk/safety assessment (DPRA/DPSA) methodologies, which use a time-dependent phenomenological model of system evolution along with a model of its stochastic behavior to model for possible dependencies among failure events. The book contains a collection of papers that describe at existing plant level applicable DPRA/DPSA tools, as well as techniques that can be used to augment the ET/FT approach when needed.
This book is a methodological approach to the goal-based safety design procedure that will soon be an international requirement. This is the first single volume book to describe how to satisfy safety goals by modern reliability engineering. Its focus is on the quantitative aspects of the international standards using a methodological approach. Case studies illustrate the methodologies presented.
Presents a practical approach for incorporating human reliability analysis (HRA) into probabilistic safety assessment (PSA). This document describes the steps needed and the documentation that should be provided both to support the PSA itself and to ensure effective communication of important information arising from the studies.
The present publication can support PSA practitioners in appropriate planning of a PSA project taking into account possible uses of the PSA in the future. It can also be used by reviewers as an aid in assessing the quality of PSAs and judging the adequacy of a PSA for particular applications.
The book has been developed in conjunction with NERS 462, a course offered every year to seniors and graduate students in the University of Michigan NERS program. The first half of the book covers the principles of risk analysis, the techniques used to develop and update a reliability data base, the reliability of multi-component systems, Markov methods used to analyze the unavailability of systems with repairs, fault trees and event trees used in probabilistic risk assessments (PRAs), and failure modes of systems. All of this material is general enough that it could be used in non-nuclear applications, although there is an emphasis placed on the analysis of nuclear systems. The second half of the book covers the safety analysis of nuclear energy systems, an analysis of major accidents and incidents that occurred in commercial nuclear plants, applications of PRA techniques to the safety analysis of nuclear power plants (focusing on a major PRA study for five nuclear power plants), practical PRA examples, and emerging techniques in the structure of dynamic event trees and fault trees that can provide a more realistic representation of complex sequences of events. The book concludes with a discussion on passive safety features of advanced nuclear energy systems under development and approaches taken for risk-informed regulations for nuclear plants.
This book proposes a new approach to dynamic and online risk assessment of automated and autonomous marine systems, taking into account different environmental and operational conditions. The book presents lessons learnt from dynamic positioning incidents and accidents, and discusses the challenges of risk assessment of complex systems. The book begins by introducing dynamic and online risk assessment, before presenting automated and autonomous marine systems, as well as numerous dynamic positioning incidents. It then discusses human interactions with technology and explores how to quantify human error. Dynamic probabilistic risk assessment and online risk assessment are both considered fully, including case studies with the application of assisting operators in decision making in emergency situations. Finally, areas for future research are suggested. This practical volume offers tools and methodologies to help operators make better decisions and improve the safety of automated and autonomous marine systems. It provides a guideline for researchers and practitioners to perform dynamic probabilistic and online risk assessment, which also should be applicable to other complex systems outside the marine and maritime domain, such as nuclear power plants, chemical processes, autonomous transport systems, and space shuttles.
Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.
