Risk-Based Reliability Analysis and Generic Principles for Risk Reduction

Risk-Based Reliability Analysis and Generic Principles for Risk Reduction

1st Edition - November 3, 2006

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  • Author: Michael Todinov
  • Hardcover ISBN: 9780080447285
  • eBook ISBN: 9780080467559

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Description

This book has been written with the intention to fill two big gaps in the reliability and risk literature: the risk-based reliability analysis as a powerful alternative to the traditional reliability analysis and the generic principles for reducing technical risk. An important theme in the book is the generic principles and techniques for reducing technical risk. These have been classified into three major categories: preventive (reducing the likelihood of failure), protective (reducing the consequences from failure) and dual (reducing both, the likelihood and the consequences from failure). Many of these principles (for example: avoiding clustering of events, deliberately introducing weak links, reducing sensitivity, introducing changes with opposite sign, etc.) are discussed in the reliability literature for the first time. Significant space has been allocated to component reliability. In the last chapter of the book, several applications are discussed of a powerful equation which constitutes the core of a new theory of locally initiated component failure by flaws whose number is a random variable.

Key Features

  • Offers a shift in the existing paradigm for conducting reliability analyses
  • Covers risk-based reliability analysis and generic principles for reducing risk
  • Provides a new measure of risk based on the distribution of the potential losses from failure as well as the basic principles for risk-based design
  • Incorporates fast algorithms for system reliability analysis and discrete-event simulators
  • Includes the probability of failure of a structure with complex shape expressed with a simple equation

Readership

This book is suitable for all students studying risk analysis, reliability and mechanical engineering. Also for risk analysts, reliability consultants, lecturers and practising reliability engineers. It is also suitable for engineering students, reliability and risk practitioners.

Table of Contents

  • Dedication

    PREFACE

    Chapter 1: RISK-BASED RELIABILITY ANALYSIS: A POWERFUL ALTERNATIVE TO THE TRADITIONAL RELIABILITY ANALYSIS

    Chapter 2: BASIC RELIABILITY CONCEPTS AND CONVENTIONS USED FOR DETERMINING THE LOSSES FROM FAILURES

    2.1 RELIABILITY AND FAILURE

    2.2 HAZARD RATE AND TIME TO FAILURE DISTRIBUTION

    2.3 HOMOGENEOUS POISSON PROCESS AND ITS LINK WITH THE NEGATIVE EXPONENTIAL DISTRIBUTION

    2.4 WEIBULL MODEL FOR THE DISTRIBUTION OF THE TIME TO FAILURE

    2.5 RELIABILITY BATHTUB CURVE FOR NON-REPAIRABLE COMPONENTS/SYSTEMS

    2.6 PRODUCTION AVAILABILITY

    2.7 TIME TO FAILURE DISTRIBUTION OF A SERIES ARRANGEMENT, COMPOSED OF COMPONENTS WITH CONSTANT HAZARD RATES

    2.8 REDUNDANCY

    2.9 BUILDING RELIABILITY NETWORKS

    2.10 TYPE OF COMPONENTS IN A RELIABILITY NETWORK

    2.11 PSEUDO-CODE CONVENTIONS USED IN THE ALGORITHMS FOR RISK-BASED RELIABILITY ANALYSIS

    APPENDIX 2.1

    Chapter 3: METHODS FOR ANALYSIS OF COMPLEX RELIABILITY NETWORKS

    3.1 NETWORK REDUCTION METHOD FOR RELIABILITY ANALYSIS OF COMPLEX SYSTEMS AND ITS LIMITATIONS

    3.2 DECOMPOSITION METHOD FOR RELIABILITY ANALYSIS OF SYSTEMS WITH COMPLEX TOPOLOGY AND ITS LIMITATIONS

    3.3 METHODS FOR SYSTEM RELIABILITY ANALYSIS BASED ON MINIMUM PATH SETS AND CUT SETS AND THEIR LIMITATIONS

    3.4 MONTE CARLO SIMULATION ALGORITHMS FOR SYSTEM RELIABILITY ANALYSIS BASED ON TESTING MINIMAL PATHS OR MINIMAL CUT SETS

    Algorithm 3.2

    3.5 DRAWBACKS OF THE METHODS FOR SYSTEM RELIABILITY ANALYSIS BASED ON MINIMUM PATH SETS AND MINIMUM CUT SETS

    3.6 SYSTEM RELIABILITY ANALYSIS BASED ON FINDING PATHS THROUGH WORKING COMPONENTS IN RELIABILITY NETWORKS

    3.7 PRESENTING THE TOPOLOGY OF A RELIABILITY NETWORK BY ADJACENCY ARRAYS

    3.8 UPDATING THE ADJACENCY MATRIX AND THE ADJACENCY ARRAYS AFTER A COMPONENT FAILURE

    3.9 AN ALGORITHM FOR DETERMINING THE EXISTENCE OF A PATH THROUGH WORKING COMPONENTS IN COMPLEX RELIABILITY NETWORKS

    3.10 AN EFFICIENT ALGORITHM FOR DETERMINING THE EXISTENCE OF A PATH IN A COMPLEX RELIABILITY NETWORK REPRESENTED BY ADJACENCY ARRAYS

    3.11 AN EFFICIENT ALGORITHM FOR DETERMINING THE EXISTENCE OF k OUT OF n PATHS IN COMPLEX RELIABILITY NETWORKS CONTAINING MULTIPLE END NODES

    3.12 AN ALGORITHM FOR DETERMINING THE RELIABILITY OF A COMPLEX RELIABILITY NETWORK

    3.13 APPLICATIONS: RELIABILITY ANALYSIS OF COMPLEX RELIABILITY NETWORKS INCLUDING A LARGE NUMBER OF COMPONENTS

    Chapter 4: PROBABILISTIC RISK ASSESSMENT AND RISK MANAGEMENT

    4.1 TRADITIONAL ENGINEERING RISK ASSESSMENT

    4.2 A RISK ACCEPTABILITY CRITERION BASED ON A SPECIFIED MAXIMUM TOLERABLE RISK LEVEL

    4.3 RISK OF FAILURE IN CASE OF A TIME-DEPENDENT COST OF FAILURE

    4.4 RISK-ASSESSMENT TOOLS

    4.5 RISK MANAGEMENT

    4.6 REDUCING THE RISK OF FAILURE BY DESIGNING AND MAINTAINING BARRIERS

    Chapter 5: POTENTIAL LOSS FROM FAILURE FOR NON-REPAIRABLE COMPONENTS AND SYSTEMS WITH MULTIPLE FAILURE MODES

    5.1 DRAWBACKS OF THE EXPECTED LOSS AS A MEASURE OF THE LOSS FROM FAILURES

    5.2 POTENTIAL LOSS, CONDITIONAL LOSS AND RISK OF FAILURE

    5.3 VARIANCE OF THE CONDITIONAL LOSS AND THE POTENTIAL LOSS FROM MULTIPLE MUTUALLY EXCLUSIVE FAILURE MODES

    5.4 COUNTEREXAMPLES RELATED TO THE RISK OF FAILURE OF NON-REPAIRABLE SYSTEMS

    5.5 DETERMINING THE LIFE DISTRIBUTION AND THE RISK OF FAILURE OF A COMPONENT CHARACTERISED BY MULTIPLE FAILURE MODES

    Algorithm 5.1

    5.6 UNCERTAINTY AND ERRORS ASSOCIATED WITH RELIABILITY PREDICTIONS

    Algorithm 5.2

    Algorithm 5.3

    5.7 POTENTIAL LOSS AND POTENTIAL OPPORTUNITY

    Chapter 6: LOSSES FROM FAILURES FOR REPAIRABLE SYSTEMS WITH COMPONENTS LOGICALLY ARRANGED IN SERIES

    Algorithm 6.1

    6.1 LOSSES FROM FAILURES FOR REPAIRABLE SYSTEMS WHOSE COMPONENT FAILURES FOLLOW A NON-HOMOGENEOUS POISSON PROCESS

    6.2 LOSSES FROM FAILURES FOR REPAIRABLE SYSTEMS WHOSE COMPONENT FAILURES FOLLOW A HOMOGENEOUS POISSON PROCESS

    6.3 COUNTEREXAMPLE RELATED TO REPAIRABLE SYSTEMS

    6.4 FAILURE AND OPPORTUNITY

    Chapter 7: RELIABILITY ANALYSIS OF COMPLEX REPAIRABLE SYSTEMS BASED ON CONSTRUCTING THE DISTRIBUTION OF THE POTENTIAL LOSSES

    7.1 RELIABILITY NETWORKS OF TWO COMPETING PRODUCTION SYSTEMS

    7.2 AN ALGORITHM FOR RELIABILITY ANALYSIS BASED ON THE POTENTIAL LOSSES FROM FAILURES

    Algorithm 7.1

    7.3 INPUT DATA AND RESULTS RELATED TO THE POTENTIAL LOSSES FOR TWO COMPETING PRODUCTION SYSTEMS

    7.4 ANALYSIS OF THE RESULTS

    7.5 INFLUENCE OF THE SYSTEM TOPOLOGY ON THE LOSSES FROM FAILURES

    Chapter 8: RELIABILITY VALUE ANALYSIS FOR COMPLEX SYSTEMS

    8.1 DERIVING THE VALUE FROM DISCOUNTED CASH-FLOW CALCULATIONS

    8.2 INPUT DATA FOR THE RELIABILITY VALUE ANALYSIS

    8.3 DETERMINING THE DISTRIBUTION OF THE NPV

    Algorithm 8.1

    8.4 RESULTS AND ANALYSIS RELATED TO THE NPV

    8.5 ANALYSIS OF THE RESULTS RELATED TO THE PROBABILITY OF EXISTENCE OF THE MFFOP

    Chapter 9: RELIABILITY ALLOCATION BASED ON MINIMISING THE TOTAL COST

    9.1 MINIMISING THE TOTAL COST: VALUE FROM THE RELIABILITY INVESTMENT

    9.2 RELIABILITY ALLOCATION TO MINIMISE THE TOTAL COST

    9.3 RELIABILITY ALLOCATION TO MINIMISE THE TOTAL COST FOR A SYSTEM WITH COMPONENTS LOGICALLY ARRANGED IN SERIES

    9.4 RELIABILITY ALLOCATION BY EXHAUSTIVE SEARCH THROUGH ALL AVAILABLE ALTERNATIVES

    Algorithm 9.1

    9.5 NUMERICAL EXAMPLES

    9.6 APPLICATIONS

    9.7 RELIABILITY ALLOCATION TO LIMIT THE EXPECTED LOSSES FROM FAILURES BELOW A MAXIMUM ACCEPTABLE LEVEL

    Chapter 10: GENERIC APPROACHES TO REDUCING THE LIKELIHOOD OF CRITICAL FAILURES

    10.1 REDUCING THE LOSSES FROM FAILURES BY IMPROVING THE RELIABILITY OF COMPONENTS

    10.2 MEASURES GUARANTEEING A SMALL LIKELIHOOD OF A CRITICAL FAILURE DURING A SPECIFIED MINIMUM FAILURE-FREE OPERATING PERIOD

    10.3 PREVENTIVE BARRIERS FOR REDUCING THE LIKELIHOOD OF FAILURE

    10.4 INCREASING THE RELIABILITY OF COMPONENTS IN PROPORTION WITH THE LOSSES FROM FAILURES ASSOCIATED WITH THEM

    10.5 LIMITING THE POTENTIAL LOSSES BY REDUCING THE LENGTH OF EXPOSURE

    Chapter 11: SPECIFIC PRINCIPLES FOR REDUCING THE LIKELIHOOD OF FAILURES

    11.1 REDUCING THE RISK OF FAILURE BY BUILDING IN REDUNDANCY

    11.2 REDUCING THE RISK OF FAILURE BY INCREASING THE CONNECTIVITY OF THE RELIABILITY NETWORKS

    11.3 DECREASING THE PROBABILITY OF AN ERROR OUTPUT BY USING VOTING SYSTEMS

    11.4 REDUCING THE RISK OF FAILURE BY REDUCING THE SENSITIVITY TO FAILURE OF SINGLE COMPONENTS

    11.5 REDUCING THE RISK OF FAILURE BY DERATING

    11.6 IMPROVING RELIABILITY BY SIMPLIFYING COMPONENTS AND SYSTEMS

    11.7 IMPROVING RELIABILITY BY ELIMINATING WEAK LINKS IN THE DESIGN

    11.8 REDUCING THE RISK OF FAILURE BY A PROPER DESIGN OF MOVING PARTS AND REDUCING THEIR NUMBER

    11.9 REDUCING THE RISK OF FAILURE BY MAINTAINING THE CONTINUITY OF ACTION

    11.10 REDUCING THE RISK OF FAILURE BY INTRODUCING CHANGES WITH OPPOSITE SIGN TO UNFAVOURABLE CHANGES DURING SERVICE

    11.11 REDUCING THE RISK OF FAILURE BY REDUCING THE FREQUENCY OF LOAD APPLICATIONS

    11.12 RISK REDUCTION BY MODIFYING THE SHAPE OF COMPONENTS AND CHANGING THE AGGREGATE STATE

    11.13 REDUCING THE RISK OF FAILURE CAUSED BY HUMAN ERRORS

    11.14 REDUCING THE RISK OF FAILURE BY REDUCING THE PROBABILITY OF CLUSTERING OF EVENTS

    Chapter 12: REDUCING THE RISK OF FAILURE BY REDUCING THE NEGATIVE IMPACT FROM THE VARIABILITY OF DESIGN PARAMETERS

    12.1 IMPROVING RELIABILITY BY REDUCING THE VARIABILITY OF DESIGN PARAMETERS

    12.2 REDUCING THE VARIABILITY OF STRENGTH BY IMPROVING THE MATERIAL QUALITY

    12.3 REDUCING THE VARIABILITY OF GEOMETRICAL PARAMETERS, PREVENTING FITTING FAILURES AND JAMMING

    12.4 REDUCING THE RISK OF FAILURE BY MAKING THE DESIGN ROBUST

    Algorithm 12.1

    Chapter 13: GENERIC SOLUTIONS FOR REDUCING THE LIKELIHOOD OF OVERSTRESS AND WEAROUT FAILURES

    13.1 IMPROVING RELIABILITY BY A RELATIVE SEPARATION OF THE UPPER TAIL OF THE LOAD DISTRIBUTION AND THE LOWER TAIL OF THE STRENGTH DISTRIBUTION

    13.2 INCREASING THE RESISTANCE AGAINST FAILURES CAUSED BY EXCESSIVE STRESSES

    13.3 REDUCING THE RISK OF FAILURE BY OPTIMISING LOADING AND AVOIDING UNFAVOURABLE STRESS STATES

    13.4 REDUCING THE RISK OF FAILURE DUE TO EXCESSIVE DEFORMATION

    13.5 REDUCING THE RISK OF FAILURE BY IMPROVING THE RESISTANCE TO FRACTURE

    13.6 REDUCING THE RISK OF OVERSTRESS FAILURE BY MODIFYING THE COMPONENT GEOMETRY

    13.7 GENERIC METHODS FOR REDUCING WEAROUT FAILURES

    13.8 IMPROVING RELIABILITY BY ELIMINATING TENSILE RESIDUAL STRESSES AT THE SURFACE OF COMPONENTS

    13.9 REDUCING THE RISK OF FAILURE BY MITIGATING THE HARMFUL EFFECT OF THE ENVIRONMENT

    Chapter 14: REDUCING THE RISK OF FAILURE BY REMOVING LATENT FAULTS, AND AVOIDING COMMON CAUSE FAILURES

    14.1 FAULTS AND FAILURES

    14.2 ASSESSING THE LIKELIHOOD OF LATENT CRITICAL FAULTS

    14.3 REDUCING THE RISK OF FAILURE BY REMOVING LATENT FAULTS AND DESIGNING FAULT-TOLERANT SYSTEMS

    14.4 IMPROVING COMPONENT RELIABILITY BY TESTING TO PRECIPITATE LATENT FAULTS

    14.5 COMMON CAUSE FAILURES AND REDUCING THE RISK ASSOCIATED WITH THEM

    Chapter 15: CONSEQUENCE ANALYSIS AND GENERIC PRINCIPLES FOR REDUCING THE CONSEQUENCES FROM FAILURES

    15.1 CONSEQUENCE ANALYSIS AND CONSEQUENCE MODELLING TOOLS

    15.2 GENERIC PRINCIPLES AND TECHNIQUES FOR REDUCING THE CONSEQUENCES FROM FAILURES

    15.3 GENERIC DUAL MEASURES FOR REDUCING BOTH THE LIKELIHOOD OF FAILURES AND THE CONSEQUENCES

    Chapter 16: LOCALLY INITIATED FAILURE AND RISK REDUCTION

    16.1 A GENERIC EQUATION RELATED TO THE PROBABILITY OF FAILURE OF A STRESSED COMPONENT WITH COMPLEX SHAPE

    16.2 DETERMINING THE CONDITIONAL INDIVIDUAL PROBABILITY OF INITIATING FAILURE, CHARACTERISING A SINGLE FLAW

    16.3 IMPORTANT SPECIAL CASES RELATED TO THE CONDITIONAL INDIVIDUAL PROBABILITY OF INITIATING FAILURE

    Algorithm 16.1

    16.4 DETERMINING THE LOWER TAIL OF THE STRENGTH DISTRIBUTION

    16.5 STATISTICS OF FAILURE INITIATED BY FLAWS

    16.6 OPTIMISING DESIGNS BY DECREASING THEIR VULNERABILITY TO FAILURE INITIATED BY FLAWS

    16.7 DETERMINING THE SPATIAL DISTRIBUTION OF THE FAILURE INITIATION SITE

    16.8 PROBABILITY OF LOCALLY INITIATED FAILURE IN A FINITE DOMAIN

    16.9 EQUATION RELATED TO THE FATIGUE LIFE DISTRIBUTION OF A COMPONENT CONTAINING DEFECTS

    16.10 PROBABILITY OF FAILURE FROM TWO STATISTICALLY DEPENDENT FAILURE MODES

    APPENDIX 16.1

    APPENDIX: MONTE CARLO SIMULATION ROUTINES USED IN THE ALGORITHMS FOR RISK-BASED RELIABILITY ANALYSIS

    REFERENCES

    INDEX

Product details

  • No. of pages: 400
  • Language: English
  • Copyright: © Elsevier Science 2006
  • Published: November 3, 2006
  • Imprint: Elsevier Science
  • Hardcover ISBN: 9780080447285
  • eBook ISBN: 9780080467559

About the Author

Michael Todinov

Prof. Todinov’s background is Engineering, Mathematics and Computer Science. He holds a PhD and a higher doctorate (DEng) from the University of Birmingham. His name is associated with key results in the areas: Reliability and Risk, Flow networks, Probability, Statistics of inhomogeneous media, Theory of phase transformations, Residual stresses and Probabilistic fatigue and fracture.

M.Todinov pioneered research on: the theory of repairable flow networks and networks with disturbed flows, risk-based reliability analysis - driven by the cost of system failure, fracture initiated by flaws in components with complex shape, reliability dependent on the relative configurations of random variables and optimal allocation of a fixed budget to achieve a maximal risk reduction.

A sample of M.Todinov’s results include: introducing the hazard stress function for modelling the probability of failure of materials and deriving the correct alternative of the Weibull model; stating a theorem regarding the exact upper bound of properties from multiple sources and a theorem regarding variance of a distribution mixture; the formulation and proof of the necessary and sufficient conditions of the Palmgren-Miner rule and Scheil’s additivity rule; deriving the correct alternative of the Johnson-Mehl-Avrami-Kolmogorov equation and stating the dual network theorems for static flows networks and networks with disturbed flows.

Affiliations and Expertise

Department of Mechanical Engineering and Mathematical Sciences, Oxford Brookes University, Oxford, UK

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