Safety and Reliability Modeling and Its Applications (Advances in Reliability Science)

✍ Scribed by Mangey Ram (editor), Hoang Pham (editor)

• Publisher: Elsevier
• Year: 2021
• Tongue: English
• Leaves: 439
• Edition: 1
• Category: Library

✦ Synopsis

Safety and Reliability Modeling and Its Applications combines work by leading researchers in engineering, statistics and mathematics who provide innovative methods and solutions for this fast-moving field. Safety and reliability analysis is one of the most multidimensional topics in engineering today. Its rapid development has created many opportunities and challenges for both industrialists and academics, while also completely changing the global design and systems engineering environment. As more modeling tasks can now be undertaken within a computer environment using simulation and virtual reality technologies, this book helps readers understand the number and variety of research studies focusing on this important topic.

The book addresses these important recent developments, presenting new theoretical issues that were not previously presented in the literature, along with solutions to important practical problems and case studies that illustrate how to apply the methodology.

✦ Table of Contents

Front Matter
Series Page:
Front Matter
Copyright
Contents
Preface
Acknowledgment
About the Editors
List of Contributors
1 Reliability analysis of asphalt pavements: concepts and applications
1.1 Preamble
1.2 Concepts of reliability
1.2.1 Levels of Reliability Methods
1.3 Literature regarding the application of reliability concepts for asphalt pavements
1.4 Issues with estimation of pavement reliability
1.4.1 Input Parameters Variability
1.4.2 Performance models
1.4.3 Interaction between the failure modes
1.4.4 Material strength degradation
1.5 Conclusions
Acknowledgements
Disclosure statement
References
2 Markov modeling of multi-state systems with simultaneous component failures/repairs, using an extended concept of component importance
2.1 Introduction
2.2 Basic assumptions, notation and definitions
2.3 Theoretical background
2.4 The illustrative model of an example system
2.5 Intensities of transitions between the system states
2.6 Obtaining useful reliability parameters from transition intensities
2.7 Conclusion and future work
References
3 Reliability analysis of solar array drive assembly by dynamic fault tree
3.1 Introduction
3.2 DFT method
3.3 DFT Modeling for SADA
3.3.1 Structure and working principle of SADA
3.3.2 DFT model
3.4 Reliability analysis of SADA
3.4.1 Reliability analysis of the dynamic module based on Markov model
3.4.2 Analysis of the static module based on BDD
3.4.3 System reliability calculation
3.5 Conclusion
Acknowledgements
References
4 Reliability and maintainability of safety instrumented system
4.1 Introduction
4.1.1 Preamble
4.1.2 Failure modes and failure rates
4.1.3 Spurious Trips
4.1.4 Probabilistic evaluation of SIS
4.1.5 SIS design optimization
4.2 Literature review
4.2.1 Probability of failure on demand
4.2.2 Spurious activation
4.2.3 Causes of spurious shutdowns
4.2.4 KooN configurations
4.2.5 Partial stroke testing
4.2.6 SIS subject to degradation due to aging and external demands
4.3 Problem formulation solution methodology
4.3.1 Problem formulation
4.3.2 Solution Methodology
4.4 Reliability and maintainability
4.4.1 Reliability
4.4.2 Maintainability
4.5 Case study on reliability and maintainability of SIS
4.5.1 Short manufacturing procedure
4.5.2 Hazard
4.5.3 Hazard consequences and targets
4.5.4 Discussion and Recommendation
4.5.5 Description of operation
4.5.6 Failure scenarios
4.5.7 Protective arrangements
4.5.8 Assumptions
4.5.9 Hazard analysis
4.5.10 Notes
4.5.11 Likelihood of fatality
4.5.12 PFD calculation
4.5.13 Installation and Commissioning
4.5.14 Conclusion
4.6 Fault analysis
4.6.1 Introduction
4.6.2 Dynamic fault tree
4.6.3 Case study
4.6.4 Dynamic fault tree of SIS failure
Conclusion
Bibliography
5 Application of Markovian models in reliability and availability analysis: advanced topics
5.1 Introduction
5.2 Markov chains theoretical foundation
5.2.1 Discrete-time Markov chains
5.2.2 Continuous-time Markov chains
5.3 Application of Markov chains to the reliability and availability analysis of engineering systems
5.3.1 Basics of reliability engineering
5.3.2 Series and parallel configurations
5.3.3 Standby systems
5.3.4 Load-sharing systems
5.3.5 Repairable systems
5.3.6 State-space reduction for reliability analysis
5.3.7 A practical application of state-space reduction
5.4 Importance measures using Markov chains
5.4.1 Traditional importance measures
5.4.2 Importance analysis using Markov chains
5.4.3 Example
5.5 Uncertainty propagation in Markov chains
5.5.1 Failure rates and their uncertainties
5.5.2 Procedure to evaluate the uncertainty propagation in a Markov Chain
5.5.3 Example
5.6 Multiphase Markov chains and their application to availability studies
5.6.1 Basic concepts of MPMC
5.6.2 Safety integrity level (SIL)
5.6.3 SIL assessment using MPMC
5.7 Final considerations
References
6 A method of vulnerability analysis based on deep learning for open source software
6.1 Introduction
6.2 Deep learning approach to fault big data
6.3 Estimation of Vulnerability Based on Deep Learning
6.4 Numerical Examples for Estimation of Vulnerability
6.5 Concluding remarks
6.6 Acknowledgements
References
7 Mathematical and physical reality of reliability
Dedication
7.1 Introduction
7.2 Mathematical reality of reliability
7.2.1 The concept of failure function
7.2.2 Reliability model of a component
7.2.3 Reliability model of a system
7.3 Voyage to the ice
7.3.1 Impact of VTTI on reliability modelling at the MIRCE Akademy
7.4 Physical meanings of mathematical reality of reliability
7.4.1 Mathematical reality: quality of components production is one hundred percent
7.4.2 Mathematical reality: errors during system transportation, storage and installation tasks are zero percent
7.4.3 Mathematical reality: all components are one hundred percent independent
7.4.4 Mathematical reality: zero maintenance actions (inspections, repair, cleaning, etc.)
7.4.5 Mathematical reality: continuous operation of the system and components
7.4.6 Mathematical reality: time counts from the “birth” of the system
7.4.7 Mathematical reality: fixed operational scenario (load, stress, temperature, pressure, etc.)
7.4.8 Mathematical reality: reliability is independent of the location in space (GPS or stellar coordinates)
7.4.9 Mathematical reality: reliability is independent of human actions
7.4.10 Mathematical reality: reliability is independent of maintenance actions
7.4.11 Mathematical reality: Reliability is independent of calendar time (seasons do not exist)
7.4.12 Mathematical reality: reliability is independent of the natural environment
7.4.13 Concluding remarks regarding mathematical reality of reliability function
7.5 Physical reality of reliability
7.5.1 Physical reality: Quality of produced components and assemblies is less than 100 percent
7.5.2 Physical reality: transportation, storage and installation tasks are not 100 percent error free
7.5.3 Physical reality: there are interactions between “independent” components
7.5.4 Physical reality: maintenance activities like: inspections, repair, cleaning, etc., have significant impact on the reliability of a system
7.5.4.2 ANA grounded Boeing 787 for Rolls Royce engines inspections2626MIRCE Akademy Archive- MIRCE Functionability Event 20160828
7.5.4.3 Chemical residue causes in-flight shutdown to A3802727MIRCE Akademy Archive- MIRCE Functionability Event 20170500
7.5.5 Physical reality: neither all systems nor all components operate continuously
7.5.6 Physical reality: Components and a system have different “times”
7.5.7 Physical reality: Variable operation scenarios (load, stress, temperature, pressure, etc.)
7.5.8 Physical reality: Reliability is dependent on the location in space defined by GPS coordinates
7.5.9 Physical reality: Reliability is dependent on humans
7.5.10 Physical reality: Maintenance induced failures
7.5.11 Physical reality: Reliability is dependent on natural environment
7.5.12 Closing remarks regarding physical reality of reliability
7.6 Mathematical versus physical reality of reliability
7.7 Closing Question
Acknowledgement
References
8 Optimum staggered testing strategy for 1- and 2-out-of-3 redundant safety instrumented systems
8.1 Introduction
8.2 PFD of redundant safety systems
8.2.1 PFD in staggered testing
8.2.2 Optimal staggered testing points: 1-out-of- 2 structure
8.3 Staggered testing in 1-out-of-3 structure
8.3.1 Case with three different testing time points
8.3.2 Case with two different testing time points
8.4 Staggered testing in 2-out- of-3 structure
8.4.1 Case with three different testing time points
8.4.2 Case with two different testing time points
8.4.3 Numerical examples
8.5 Conclusions
References
9 Modified failure modes and effects analysis model for critical and complex repairable systems
9.1 Introduction
9.2 Repairable Systems and Imperfect Repair
9.3 Fuzzy AHP
9.3.1 Fuzzy extent analysis method for weights calculation
9.4 Estimation of RPN
9.4.1 Classification of severity
9.4.2 Detection
9.5 Case study11The paper has been published in Springer International Publishing AG, part of Springer Nature 2019, R. L. Boring (Ed.): AHFE 2018, AISC 778, pp. 77-87, 2019.https://doi.org/10.1007/978-3-319-94391-6_8
9.5.1 Remedial measures
9.6 Conclusion and Future Scope
Exercise
References
10 Methodology to select human reliability analysis technique for repairable systems
10.1 Introduction
10.1.1 Various HRA Techniques
10.2 Selection of the best HRA technique for a particular case
10.2.1 Fuzzy Analytical Hierarchical Process
10.2.2 Neural network modeling for selection and ranking of alternatives
10.3 Case study of space station11The paper has been published in Springer International Publishing AG, part of Springer Nature 2019, R. L. Boring (Ed.): AHFE 2018, AISC 778, pp. 128-137, 2019. https://doi.org/10.1007/978-3-319-94391-6_13
10.3.1 Fuzzy AHP Weights Estimation
10.3.2 ANN Model for ECLSS
10.4 Conclusion and future scope
Exercise
Appendix
References
11 Operation risk assessment of the main-fan installations of mines in gas and nongas conditions
11.1 Introduction
11.2 The ventilation system failures role in assessing the risk of flammable gases explosion
11.3 Analysis of the occurrence and development of accidents
11.4 Analysis of the probability of explosion of flammable gases/hydrogen sulfide at the mine from electrical equipment
11.5 The risk analysis results
11.6 Conclusion
References
12 Generalized renewal processes
12.1 Introduction
12.2 The GRP models
12.2.1 Maximum likelihood estimation of the GRP model
12.2.2 The asymptotic confidence intervals of the GRP parameters
12.2.3 Inverse function of the GRP model
12.3 The UGRP modeling
12.3.1 The continuous uniform model
12.3.2 The UGRP model
12.4 The WGRP modeling
12.4.1 The Weibull model
12.4.2 The WGRP model
12.4.3 Maximum likelihood estimation of the WGRP model
12.4.4 From WGRP to HPP
12.4.5 Some contributions of power law transformation
12.4.6 Asymptotic confidence intervals for the WGRP parameters
12.4.7 Case studies of WGRP
12.4.8 Applying the WGRP GOFT
12.4.9 Applying the WGRP confidence intervals
12.5 The Gumbel GRP (GuGRP) modeling
12.5.1 The Gumbel distribution
12.5.2 The GuGRP model
12.5.3 Maximum likelihood estimation of the GuGRP model
12.5.4 Goodness-of-fit test for GuGRP
12.5.5 Case studies of GuGRP
12.6 Conclusion
Acknowledgement
References
13 Multiresponse maintenance modeling using desirability function and Taguchi methods
13.1 Introduction
13.2 Related works
13.2.1 Taguchi method
13.2.2 Desirability function
13.2.3 Regression analysis
13.3 Methodology
13.4 Case study
13.5 Result analysis
13.6 Conclusion and future research directions
References
14 Signature-based reliability study of r-within-consecutive-k-out-of-n: F systems
14.1 Introduction
14.2 The signature vector of the r-within-consecutive-k-out-of-n: F structure
14.3 Further reliability characteristics of the r-within-consecutive-k-out-of-n: F structure
14.4 Signature-based comparisons among consecutive-type systems
14.5 Discussion
References
15 Assessment of fuzzy reliability and signature of series-parallel multistate system
15.1 Introduction
15.2 Fuzzy Weibull distribution
15.2.1 Fuzzy set
15.2.2 Intuitionistic fuzzy set
15.2.3 Triangular fuzzy number
15.3 Evolution of signature, tail signature, minimal signature, and cost from structure function of the system
15.4 Algorithm for computing the system availability (see Levitin, 2005) as
15.5 Example
15.5.1 Example
15.6 Conclusion
References
Index