Monte Carlo Simulation in Dependability Analysis

System dependability is a complex task to grasp and analyze since it encompasses reliability, maintainability, availability, failure mode analysis and feared events. For operational safety analyses, reliability is a quantitative basis for the other disciplines of maintainability, availability and safety. Reliability metrics such as failure rate or MTBF are often misused as they are only valid for low-maintenance applications, and wrongly for others, as MTBF is only relevant for availability. In addition, in operational safety, many equations do not have explicit solutions, and Monte Carlo simulations are a little-used way of obtaining and/or confirming the solution obtained by numerical methods.
Monte Carlo Simulation in Dependability Analysis fills this gap as best as we can. This task is a difficult one, since operational safety is a cross-disciplinary activity in the engineering sciences - cross-disciplinary in that it must be present throughout a product's life cycle.

Inhaltsverzeichnis

Foreword by Philippe Bogdanik xi
Foreword by Gilles Zwingelstein xiii
List of Notations xv
List of Acronyms xvii
Definitions xix
Introduction xxi
Part 1. Reliability 1
Chapter 1. Predictive Reliability 3
1.1. Concept of predictive reliability 3
1.2. FIDES methodology 4
1.3. Application example 7
1.4. Maintaining a reliability specification 11
1.5. Summary 12
Chapter 2. Statistical Characteristics of Exponential and Weibull Distributions 13
2.1. Refresher about exponential and Weibull distributions 13
2.2. Parameter estimation for a reliability model using the maximum likelihood method 14
2.3. Estimator properties 17
2.4. Simulation of failure times by inverting the probability of failure 19
2.5. Impact of temperature 21
2.6. Relative bias and coefficient of variation of the Weibull parameters 22
2.7. Simulation scenarios considered from the different parameters 26
2.8. Summary 31
Chapter 3. System Reliability 33
3.1. Assumptions 34
3.2. Maintenance-free systems 34
3.3. Maintenance-free systems 45
3.4. Series/parallel system 73
3.5. Parallel/serial system 78
3.6. Use cases 86
Chapter 4. Impact of Temperature on Reliability 97
4.1. Arrhenius law 97
4.2. Operational life profile 98
4.3. Sedyakin's principle 98
4.4. Consequences for reliability estimates 100
4.5. Taking the effect of maintenance into consideration 107
4.6. Summary 114
Chapter 5. Aging Tests 115
5.1. Accelerated aging test 117
5.2. Aging test design 118
5.3. Sequential test at two constant temperatures 121
5.4. Constant-level parallel testing 124
5.5. Constant-level mixed testing 125
5.6. Summary 125
Chapter 6. Application of the Noncentral Beta Distribution 127
6.1. Context 127
6.2. The "noncentral beta" probability distribution 129
6.3. Measurement modeling 130
6.4. Rejection method 132
6.5. Confidence interval for noncentral beta distribution 136
6.6. Rationale for the choice of the noncentral beta distribution 137
6.7. Summary 139
Chapter 7. Statistical Characteristics of HPP and PLP Processes 141
7.1. Reminders about Poisson processes 141
7.2. HPP homogeneous Poisson process 141
7.3. PLP power process 143
7.4. Summary 148
Part 2. Maintainability 151
Chapter 8. Maintainability 153
8.1. Average number of failures 153
8.2. Serial system 154
8.3. Parallel system 159
8.4. k/n system 162
8.5. Avionics system 164
8.6. Summary 165
Part 3. Availability 167
Chapter 9. System Availability 169
9.1. Assumptions 169
9.2. Uptime and repair time: exponential distributions 171
9.3. Exponential distribution uptime and constant repair time 174
9.4. Exponential distribution uptime and uniform distribution repair time 176
9.5. Exponential distribution uptimes and normal distribution repair times 178
9.6. Uptimes exponential distribution and repair times Weibull distribution 182
9.7. Serial system 183
9.8. Parallel system 186
9.9. k-out-of-n redundancy 189
9.10. Series/parallel system 192
9.11. Parallel/serial system 193
9.12. Energy conversion 194
9.13. Summary 200
Part 4. Safety 203
Chapter 10. FMEA Concurrent Failure Mechanisms 205
10.1. Maintenance-free industrial applications 209
10.2. Industrial applications with maintenance 211
10.3. Consideration of physical contributions 215
10.4. Summary 219
Chapter 11. Feared Events (FTA) 221
11.1. Introduction 221
11.2. Regulatory aspects 222
11.3. Probability of the occurrence of a feared event 228
11.4. Practical application 235
11.5. Summary 239
Appendices 241
References 249
Index 251

Über den Autor / die Autorin

Franck Bayle trained as an electronic engineer. He has practiced for almost 15 years, working at Crouzet and then at Thalès in Valence, France. He has also worked as Design Authority in reliability and maturity.
Laurent Denis is the CEO of StatXpert, a consulting, training, and software company specializing in statistics and operational reliability based in Pessac, France.
Adrien Gigliati is Dependability Engineer at Thalès in Valence, France.

Produktdetails

• Autoren: Franck Bayle, Franck (Thales Bayle, Laurent Denis, Laurent (StatXpert Denis, Adrien Gigliati, Adrien (Thales Gigliati
• Verlag: ISTE Ltd.
• Sprache: Englisch
• Produktform: Fester Einband
• Erscheint: 01.02.2026
• EAN: 9781836690320
• ISBN: 978-1-83669-032-0
• Seiten: 304
• Serie: ISTE Invoiced
• Thema: Naturwissenschaften, Medizin, Informatik, Technik > Mathematik