E-Book, Englisch, 316 Seiten
Faulin / Juan / Martorell Alsina Simulation Methods for Reliability and Availability of Complex Systems
1. Auflage 2010
ISBN: 978-1-84882-213-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 316 Seiten
Reihe: Springer Series in Reliability Engineering
ISBN: 978-1-84882-213-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
Simulation Methods for Reliability and Availability of Complex Systems discusses the use of computer simulation-based techniques and algorithms to determine reliability and availability (R and A) levels in complex systems. The book: shares theoretical or applied models and decision support systems that make use of simulation to estimate and to improve system R and A levels, forecasts emerging technologies and trends in the use of computer simulation for R and A and proposes hybrid approaches to the development of efficient methodologies designed to solve R and A-related problems in real-life systems. Dealing with practical issues, Simulation Methods for Reliability and Availability of Complex Systems is designed to support managers and system engineers in the improvement of R and A, as well as providing a thorough exploration of the techniques and algorithms available for researchers, and for advanced undergraduate and postgraduate students.
Javier Faulin is associate professor of statistics and operations research at the Public University of Navarra, Navarra, Spain. He is also a mentor and professor of operations research for the Spanish Open University (UNED), Madrid, Spain.Dr Faulin has been a visiting professor at the University of Sabana, Bogotá, Colombia, and the University of Montreal, Montreal, Canada, and a visiting scholar at the University of Cincinnati, Ohio, USA, and University College of Business, Dublin, Ireland. He also spent three years at the University of Surrey, Guildford, UK, as associate lecturer in decision making in business, and in 2007 and 2008 was a professeur invité at the Université de Rennes, Rennes, France.Angel A. Juan is an associate professor of simulation and data analysis at the Open University of Catalonia. He is also a lecturer of statistics at the Technical University of Catalonia.Dr Juan has also been a teacher of mathematics and statistics at Elian's Boston School, Boston, USA; an assistant professor of mathematics at the University of Alicante, Alicante, Spain; a teacher of mathematics and computer science for the Catalan government's Department of Education; assistant professor of applied statistics, simulation of computer networks and mathematics at the Open University of Catalonia, Barcelona, Spain; and a teacher of programming languages, computer networks and database management systems for the Catalan government's Department of Education.Sebastián Martorell is associate professor in nuclear engineering and director of the Department of Chemical and Nuclear Engineering at the Universidad Politécnica de Valencia, from which he also received his MSc and PhD.Dr Martorell is vice-chairman of the European Safety and Reliability Association (ESRA). He is also a member of the editorial board of the European Journal of Industrial Engineering.Jose Emmanuel Ramirez-Marquez is assistant professor at the Stevens Institute of Technology, Hoboken, USA. He is also director of the Quality Control and Reliability Engineering Division Board for the Institute of Industrial Engineers.Prior to receiving his PhD in 2004, Dr Ramirez-Marquez was an area officer for the Secretaria de Hacienda y Credito Publico Servicio de Administracion Tributaria, Ciudad de Mexico, Mexico, and a graduate assistant at Rutgers, New Brunswick, USA.
Autoren/Hrsg.
Weitere Infos & Material
1;Foreword;6
2;Preface;8
3;Contents;12
4;Part I: Fundamentals of Simulation in Reliability and Availability Issues;19
4.1;Chapter 1: Reliability Estimation by Advanced Monte Carlo Simulation;20
4.1.1;1.1 Introduction;21
4.1.2;1.2 Simulation Methods Implemented in this Study;23
4.1.2.1;1.2.1 The Subset Simulation Method;23
4.1.2.2;1.2.2 The Line Sampling Method;27
4.1.3;1.3 Simulation Methods Considered for Comparison;30
4.1.3.1;1.3.1 The Importance Sampling Method;31
4.1.3.2;1.3.2 The Dimensionality Reduction Method;32
4.1.3.3;1.3.3 The Orthogonal Axis Method;33
4.1.4;1.4 Application 1: the Cracked-plate Model;34
4.1.4.1;1.4.1 The Mechanical Model;35
4.1.4.2;1.4.2 The Structural Reliability Model;35
4.1.4.3;1.4.3 Case Studies;36
4.1.4.4;1.4.4 Results;36
4.1.5;1.5 Application 2: Thermal-fatigue Crack Growth Model;40
4.1.5.1;1.5.1 The Mechanical Model;41
4.1.5.2;1.5.2 The Structural Reliability Model;42
4.1.5.3;1.5.3 Case Studies;43
4.1.5.4;1.5.4 Results;43
4.1.6;1.6 Summary and Critical Discussion of the Techniques;46
4.1.7;Appendix 1. Markov Chain Monte Carlo Simulation;51
4.1.8;Appendix 2. The Line Sampling Algorithm;52
4.1.9;References;55
4.2;Chapter 2: Dynamic Fault Tree Analysis: Simulation Approach;57
4.2.1;2.1 Fault Tree Analysis: Static Versus Dynamic;57
4.2.2;2.2 Dynamic Fault Tree Gates;58
4.2.3;2.3 Effect of Static Gate Representation in Place of Dynamic Gates;61
4.2.4;2.4 Solving Dynamic Fault Trees;62
4.2.5;2.5 Modular Solution for Dynamic Fault Trees;62
4.2.6;2.6 Numerical Method;64
4.2.6.1;2.6.1 PAND Gate;64
4.2.6.2;2.6.2 SEQ Gate;65
4.2.6.3;2.6.3 SPARE Gate;65
4.2.7;2.7 Monte Carlo Simulation Approach for Solving Dynamic Fault Trees;66
4.2.7.1;2.7.1 PAND Gate;67
4.2.7.2;2.7.2 SPARE Gate;68
4.2.7.3;2.7.3 FDEP Gate;69
4.2.7.4;2.7.4 SEQ Gate;69
4.2.8;2.8 Example 1: Simplified Electrical (AC) Power Supply System of Typical Nuclear Power Plant;71
4.2.8.1;2.8.1 Solution with Analytical Approach;72
4.2.8.2;2.8.2 Solution with Monte Carlo Simulation;73
4.2.9;2.9 Example 2: Reactor Regulation System of a Nuclear Power Plant;76
4.2.9.1;2.9.1 Dynamic Fault Tree Modeling;77
4.2.10;2.10 Summary;77
4.2.11;References;79
4.3;Chapter 3: Analysis and Improvements of Path-based Methods for Monte Carlo Reliability Evaluation of Static Models;81
4.3.1;3.1 Introduction;82
4.3.2;3.2 Standard Monte Carlo Reliability Evaluation;84
4.3.3;3.3 A Path-based Approach;85
4.3.4;3.4 Robustness Analysis of the Algorithm;87
4.3.5;3.5 Improvement;90
4.3.6;3.6 Acceleration by Randomized Quasi-Monte Carlo;92
4.3.6.1;3.6.1 Quasi-Monte Carlo Methods;93
4.3.6.2;3.6.2 Randomized Quasi-Monte Carlo Methods;94
4.3.6.3;3.6.3 Application to Our Static Reliability Problem;95
4.3.6.4;3.6.4 Numerical Results;97
4.3.7;3.7 Conclusions;99
4.3.8;References;99
4.4;Chapter 4: Variate Generation in Reliability;101
4.4.1;4.1 Generating Random Lifetimes;101
4.4.1.1;4.1.1 Density-based Methods;103
4.4.1.2;4.1.2 Hazard-based Methods;105
4.4.2;4.2 Generating Stochastic Processes;107
4.4.2.1;4.2.1 Counting Processes;107
4.4.2.2;4.2.2 Poisson Processes;108
4.4.2.3;4.2.3 Renewal Processes;109
4.4.2.4;4.2.4 Alternating Renewal Processes;110
4.4.2.5;4.2.5 Nonhomogeneous Poisson Processes;110
4.4.2.6;4.2.6 Markov Models;111
4.4.2.7;4.2.7 Other Variants;111
4.4.2.8;4.2.8 Random Process Generation;112
4.4.3;4.3 Survival Models Involving Covariates;115
4.4.3.1;4.3.1 Accelerated Life Model;116
4.4.3.2;4.3.2 Proportional Hazards Model;116
4.4.3.3;4.3.3 Random Lifetime Generation;116
4.4.4;4.4 Conclusions and Further Reading;118
4.4.5;References;118
5;Part II: Simulation Applications in Reliability;120
5.1;Chapter 5: Simulation-based Methods for Studying Reliability and Preventive Maintenance of Public Infrastructure;121
5.1.1;5.1 Introduction;121
5.1.2;5.2 The Power of Simulation;122
5.1.3;5.3 Case Studies;123
5.1.3.1;5.3.1 Emergency Response;124
5.1.3.2;5.3.2 Preventive Maintenance of Bridges;128
5.1.4;5.4 Conclusions;133
5.1.5;References;134
5.2;Chapter 6: Reliability Models for Data Integration Systems;136
5.2.1;6.1 Introduction;136
5.2.2;6.2 Data Quality Concepts;139
5.2.2.1;6.2.1 Freshness and Accuracy Definitions;139
5.2.2.2;6.2.2 Data Integration System;140
5.2.2.3;6.2.3 Data Integration Systems Quality Evaluation;142
5.2.3;6.3 Reliability Models for Quality Management in Data Integration Systems;144
5.2.3.1;6.3.1 Single State Quality Evaluation in Data Integration Systems;145
5.2.3.2;6.3.2 Reliability-based Quality Behavior Models;146
5.2.4;6.4 Monte Carlo Simulation for Evaluating Data Integration Systems Reliability;151
5.2.5;6.5 Conclusions;155
5.2.6;References;156
5.3;Chapter 7: Power Distribution System Reliability Evaluation Using Both Analytical Reliability Network Equivalent Technique and Time-sequential Simulation Approach;158
5.3.1;7.1 Introduction;158
5.3.2;7.2 Basic Distribution System Reliability Indices;160
5.3.2.1;7.2.1 Basic Load Point Indices;160
5.3.2.2;7.2.2 Basic System Indices;161
5.3.3;7.3 Analytical Reliability Network Equivalent Technique;162
5.3.3.1;7.3.1 Definition of a General Feeder;163
5.3.3.2;7.3.2 Basic Formulas for a General Feeder;163
5.3.3.3;7.3.3 Network Reliability Equivalent;166
5.3.3.4;7.3.4 Evaluation Procedure;167
5.3.3.5;7.3.5 Example;168
5.3.4;7.4 Time-sequential Simulation Technique;171
5.3.4.1;7.4.1 Element Models and Parameters;171
5.3.4.2;7.4.2 Probability Distributions of the Element Parameters;172
5.3.4.3;7.4.3 Exponential Distribution;173
5.3.4.4;7.4.4 Generation of Random Numbers;174
5.3.4.5;7.4.5 Determination of Failed Load Point;174
5.3.4.6;7.4.6 Consideration of Overlapping Times;176
5.3.4.7;7.4.7 Reliability Indices and Their Distributions;176
5.3.4.8;7.4.8 Simulation Procedure;177
5.3.4.9;7.4.9 Stopping Rules;178
5.3.4.10;7.4.10 Example;178
5.3.4.11;7.4.11 Load Point and System Indices;178
5.3.4.12;7.4.12 Probability Distributions of the Load Point Indices;179
5.3.5;7.5 Summary;183
5.3.6;References;184
5.4;Chapter 8: Application of Reliability, Availability, and Maintainability Simulation to Process Industries: a Case Study;186
5.4.1;8.1 Introduction;186
5.4.2;8.2 Reliability, Availability, and Maintainability Analysis;187
5.4.3;8.3 Reliability Engineering in the Process Industry;187
5.4.4;8.4 Applicability of RAM Analysis to the Process Industry;188
5.4.5;8.5 Features of the Present Work;189
5.4.5.1;8.5.1 Software Used;190
5.4.6;8.6 Case Study;190
5.4.6.1;8.6.1 Natural-gas Processing Plant Reliability Block Diagram Modeling;191
5.4.6.2;8.6.2 Failure and Repair Data;197
5.4.6.3;8.6.3 Phase Diagram and Variable Throughput;198
5.4.6.4;8.6.4 Hidden and Degraded Failures Modeling;199
5.4.6.5;8.6.5 Maintenance Modeling;200
5.4.6.6;8.6.6 Crews and Spares Resources;203
5.4.6.7;8.6.7 Results;204
5.4.6.8;8.6.8 Bad Actors Identification;205
5.4.6.9;8.6.9 Cost Analysis;206
5.4.6.10;8.6.10 Sensitivity Analysis;207
5.4.7;8.7 Conclusion;208
5.4.8;References;209
5.5;Chapter 9: Potential Applications of Discrete-event Simulation and Fuzzy Rule-based Systems to Structural Reliability and Availability;211
5.5.1;9.1 Introduction;212
5.5.2;9.2 Basic Concepts on Structural Reliability;212
5.5.3;9.3 Component-level Versus Structural-level Reliability;213
5.5.4;9.4 Contribution of Probabilistic-based Approaches;214
5.5.5;9.5 Analytical Versus Simulation-based Approaches;214
5.5.6;9.6 Use of Simulation in Structural Reliability;215
5.5.7;9.7 Our Approach to the Structural Reliability Problem;216
5.5.8;9.8 Numerical Example 1: Structural Reliability;218
5.5.9;9.9 Numerical Example 2: Structural Availability;221
5.5.10;9.10 Future Work: Adding Fuzzy Rule-based Systems;223
5.5.11;9.11 Conclusions;224
5.5.12;References;225
6;Part III: Simulation Applications in Availability and Maintenance;227
6.1;Chapter 10: Maintenance Manpower Modeling: A Tool for Human Systems Integration Practitioners to Estimate Manpower, Personnel, and Training Requirements;228
6.1.1;10.1 Introduction;228
6.1.2;10.2 IMPRINT – an Human Systems Integration and MANPRINT Tool;229
6.1.3;10.3 Understanding the Maintenance Module;230
6.1.3.1;10.3.1 System Data;231
6.1.3.2;10.3.2 Scenario Data;233
6.1.4;10.4 Maintenance Modeling Architecture;234
6.1.4.1;10.4.1 The Static Model – the Brain Behind It All;235
6.1.4.2;10.4.2 A Simple Example – Putting It All Together;238
6.1.5;10.5 Results;239
6.1.6;10.6 Additional Powerful Features;240
6.1.6.1;10.6.1 System Data Importing Capabilities;240
6.1.6.2;10.6.2 Performance Moderator Effects on Repair Times;240
6.1.6.3;10.6.3 Visualization;241
6.1.7;10.7 Summary;241
6.1.8;References;242
6.2;Chapter 11: Application of Monte Carlo Simulation for the Estimation of Production Availability in Offshore Installations;244
6.2.1;11.1 Introduction;244
6.2.1.1;11.1.1 Offshore Installations;244
6.2.1.2;11.1.2 Reliability Engineering Features of Offshore Installations;245
6.2.1.3;11.1.3 Production Availability for Offshore Installations;246
6.2.2;11.2 Availability Estimation by Monte Carlo Simulation;247
6.2.3;11.3 A Pilot Case Study: Production Availability Estimation;252
6.2.3.1;11.3.1 System Functional Description;253
6.2.3.2;11.3.2 Component Failures and Repair Rates;254
6.2.3.3;11.3.3 Production Reconfiguration;255
6.2.3.4;11.3.4 Maintenance Strategies;255
6.2.3.5;11.3.5 Operational Information;258
6.2.3.6;11.3.6 Monte Carlo Simulation Model;258
6.2.4;11.4 Commercial Tools;261
6.2.5;11.5 Conclusions;262
6.2.6;References;263
6.3;Chapter 12: Simulation of Maintained Multicomponent Systems for Dependability Assessment;264
6.3.1;12.1 Maintenance Modeling for Availability Assessment;264
6.3.2;12.2 A Generic Approach to Model Complex Maintained Systems;266
6.3.3;12.3 Use of Petri Nets for Maintained System Modeling;268
6.3.3.1;12.3.1 Petri Nets Basics;268
6.3.3.2;12.3.2 Component Modeling;269
6.3.3.3;12.3.3 System Modeling;273
6.3.4;12.4 Model Simulation and Dependability Performance Assessment;275
6.3.5;12.5 Performance Assessment of a Turbo-lubricating System;276
6.3.5.1;12.5.1 Presentation of the Case Study;276
6.3.5.2;12.5.2 Assessment of the Maintained System Unavailability;279
6.3.5.3;12.5.3 Other Dependability Analysis;280
6.3.6;12.6 Conclusion;281
6.3.7;References;282
6.4;Chapter 13: Availability Estimation via Simulation for Optical Wireless Communication;284
6.4.1;13.1 Introduction;284
6.4.2;13.2 Availability;285
6.4.3;13.3 Availability Estimation;286
6.4.3.1;13.3.1 Fog Models;286
6.4.3.2;13.3.2 Rain Model;288
6.4.3.3;13.3.3 Snow Model;289
6.4.3.4;13.3.4 Link Budget Consideration;289
6.4.3.5;13.3.5 Measurement Setup and Availability Estimation via Simulation for Fog Events;290
6.4.3.6;13.3.6 Measurement Setup and Availability Estimation via Simulation for Rain Events;297
6.4.3.7;13.3.7 Availability Estimation via Simulation for Snow Events;299
6.4.3.8;13.3.8 Availability Estimation of Hybrid Networks: an Attempt to Improve Availability;301
6.4.3.9;13.3.9 Simulation Effects on Analysis;303
6.4.4;13.4 Conclusion;305
6.4.5;References;305
7;About the Editors;307
8;About the Contributors;309
9;Index;320
"Chapter 10 Maintenance Manpower Modeling: A Tool for Human Systems Integration Practitioners to Estimate Manpower, Personnel, and Training Requirements (p. 217-218)
Mala Gosakan and Susan Murray
Abstract This chapter discusses the maintenance manpower modeling capability in the Improved Performance Research Integration Tool (IMPRINT) that supports the Army’s unit of action. IMPRINT has been developed by the US Army Research Laboratory (ARL) Human Research and Engineering Directorate (HRED) in order to support the Army’s need to consider soldiers’ capabilities during the early phases of the weapon system acquisition process. The purpose of IMPRINT modeling is to consider soldiers’ performance as one element of the total system readiness equation. IMPRINT has been available since the mid 1990s, but the newest version includes significant advances.
10.1 Introduction
Even as the far-reaching implications of the next generation of weapons and information systems are being constantly redefined, one piece which has been and will continue to be central to the process is human involvement. The impacts of human performance on system performance are significant.
Human systems integration (HSI) is primarily a concept to focus on the human element in the system design process [18]. The ability thus to include and consider human involvement early in the process of system development cycle will only ease mobilization, readiness, and sustainability of the newly developed system. The Department of Defense therefore has placed increased emphasis on applying HSI concepts to evaluate and improve the performance of complex systems [16].
The US Army was the first large organization to implement HSI approach and reap the benefits of it by creating the Manpower and Personnel IntegrationManagement and Technical Program (MANPRINT) [24, 25]. As stated in the MANPRINT handbook, MANPRINT is a comprehensive management and technical program that focuses on the integration of human considerations (i.e., capabilities and limitations) into the system acquisition process.
The goal of MANPRINT is to enhance soldier-system design, reduce life-cycle ownership costs, and optimize total system performance. To facilitate this, MANPRINT is divided into the following seven domains: manpower, personnel capabilities, training, human factors engineering, system safety, health hazards, and soldier survivability. Themanpower domain focuses on the number of people required and available to operate, maintain, sustain, and provide training for systems.
The domain of personnel addresses the cognitive and physical characteristics and capabilities required to be able to train for, operate, maintain, and sustain materiel and information systems. The training domain is defined as the instruction, education, on-the-job, or selfdevelopment training required providing all personnel and units with essential job skills, and knowledge to effectively operate, deploy/employ, maintain, and support the system."
