Applied Reliability for Industry 1 illustrates the multidisciplinary state-of-the-art science of predictive reliability. Many experts are now convinced that reliability is not limited to statistical sciences. In fact, many different disciplines interact in order to bring a product to its highest possible level of reliability, made available through today's technologies, developments and production methods. These three books, of which this is the first, propose new methods for analyzing the lifecycle of a system, enabling us to record the development phases according to development time and levels of complexity for its integration. Predictive reliability, as particularly focused on in Applied Reliability for Industry 1, examines all the engineering activities used to estimate or predict the reliability performance of the final mechatronic system.

Table of Contents:
Foreword xi Philippe EUDELINE Preface xiii Abdelkhalak EL HAMI, David DELAUX and Henri GRZESKOWIAK Chapter 1 FIDES, a Method for Assessing and Building the Reliability of Electronic Systems 1 Franck DAVENEL 1.1 The inadequacy of existing methods 2 1.1.1 MIL-HDBK-217F 2 1.1.2 UTE-C-80810 (or RDF2000, or IEC 62380 TR Ed.1) 2 1.1.3 PRISM or 217plus 2 1.2 The ambition of FIDES 3 1.3 General presentation of the FIDES method 5 1.3.1 Failure rate 6 1.3.2 The structure of FIDES models 7 1.3.3 Physical models 8 1.3.4 The exploitation of manufacturer data 8 1.3.5 Exploiting databases of failure mechanisms (not failure rates) 9 1.3.6 Life profile 10 1.3.7 Other contributors 11 1.3.8 Sensitivity of FIDES models 13 1.3.9 Industrial applications 14 1.4 Validity of reliability studies with FIDES 14 1.5 Conclusion 16 1.6 References 18 Chapter 2 Reliability in Maritime Transport: Choosing a Container Handling System 19 Julien RULLIER, Benjamin ECHARD and Ghislaine DELAPAYRE 2.1 Introduction 19 2.2 Proposed case study 20 2.3 Inputs of the RAMS approach 22 2.3.1 Presentation of the system 22 2.3.2 Component reliability data 25 2.3.3 Reliability of carabiners over time 25 2.4 Assessment of the system’s RAMS 31 2.4.1 Reliability assessment 31 2.4.2 Assessment of the intrinsic availability 32 2.4.3 Maintainability assessment 33 2.4.4 Safety assessment 33 2.5 Conclusion 55 2.5.1 FMECA or fault trees, how to choose? 55 2.5.2 Pitfalls to avoid 57 2.5.3 Note on low reliability targets in innovative systems 59 2.6 General conclusion 59 2.7 References 60 Chapter 3 Generation of a Failure Model through Probabilistic "Stress--Strength" Interaction in a Context of Poor Information 61 Lambert PIERRAT 3.1 Introduction 61 3.2 Aims and objectives 62 3.3 Choosing types of legislation 63 3.3.1 Principle of maximum entropy 63 3.3.2 The strength distribution 64 3.3.3 The law of constraint 65 3.3.4 Relationship between the two laws 66 3.4 Probability of failure 67 3.4.1 Formulation 67 3.4.2 Analytical solution 68 3.4.3 Parametric expression 69 3.5 Safety factor 69 3.5.1 Simplified expressions 70 3.5.2 Validity limits 70 3.6 Validation and applications 72 3.6.1 Comparative analyses 72 3.6.2 Applications 75 3.7 Conclusion and extensions 79 3.8 References 79 Chapter 4 Reliable Optimization of Dental Implants Using the Generalized Polynomial Chaos Method 83 Fatma ABID, Abdelkhalak EL HAMI, Tarek MERZOUKI, Hassen TRABELSI, Lassaad WALHA and Mohamed HADDAR 4.1 Introduction 83 4.2 Stochastic approach 84 4.2.1 The MC method 84 4.2.2 The GPC method 85 4.3 Deterministic design optimization 86 4.4 Reliability-based design optimization 87 4.4.1 The classic method 88 4.4.2 OSF using GPC 89 4.5 Numerical result 91 4.5.1 2D dental implant 91 4.6 Conclusion 96 4.7 References 96 Chapter 5 Multi-objective Reliability Optimization Based on Substitution Models Applied Case Study of a Hip Prosthesis 101 Khalil DAMMAK and Abdelkhalak EL HAMI 5.1 Introduction 101 5.2 Description of metamodeling methods 103 5.2.1 Application of a substitution model 103 5.2.2 Construction of a metamodel 104 5.2.3 Validation of metamodels 110 5.3 Optimization of multi-objective design 111 5.3.1 Deterministic MOO 111 5.3.2 Reliability-based multi-objective design optimization 113 5.4 RBMDO based on hip prosthesis surrogate models 114 5.4.1 Deterministic simulation using the finite element method 114 5.4.2 Construction of substitution models 116 5.4.3 Optimization of multi-objective design based on reliability 118 5.5 Conclusion 121 5.6 References 122 Chapter 6 CMA-ES Assisted by the Kriging Metamodel for the Optimization of Thermomechanical Performances of Mechatronic Packaging 129 Hamid HAMDANI, Bouchaib RADI and Abdelkhalak EL HAMI 6.1 Introduction 130 6.2 Presentation of the system under study 131 6.2.1 The case of wire bonding 133 6.2.2 The case of solder joints 133 6.3 Thermal fatigue models of solder joints 135 6.3.1 The Coffin--Manson model 136 6.3.2 The Morrow model 137 6.3.3 The Coffin--Manson frequency-modified model 138 6.3.4 The Morrow frequency-modified model 138 6.3.5 The Darveaux model 138 6.4 Modeling and finite element analysis of the PQFP housing 139 6.4.1 Modeling 139 6.4.2 Material properties 141 6.4.3 Thermal load 142 6.4.4 Fatigue model selected for solder joints 143 6.4.5 Numerical results 144 6.5 Evolutionary strategies 145 6.5.1 Presentation of evolutionary strategies 145 6.5.2 Principles of ESs 146 6.5.3 Covariance matrix adaptation evolution strategy 146 6.5.4 Metamodeling techniques 151 6.5.5 Kriging-assisted CMA-ES 154 6.6 Global optimization of the PQFP housing solder joints 158 6.6.1 Formulation of the problem 158 6.6.2 Numerical simulations 160 6.7 Conclusion 162 6.8 References 164 Chapter 7 Reliable Optimization of Vibro-acoustic Problems in the Presence of Uncertainties via Polynomial Chaos 171 Khalil DAMMAK and Abdelkhalak EL HAMI 7.1 Introduction 171 7.2 Robust approaches to uncertainty propagation 172 7.2.1 The Monte Carlo method 172 7.2.2 Generalized polynomial chaos 174 7.3 Structural optimization 180 7.3.1 Formulation of the optimization problem 180 7.3.2 Deterministic design optimization 181 7.3.3 Reliability design optimization 182 7.4 OSF method coupled with GPC applied to vibro-acoustic systems in the presence of uncertainties 187 7.4.1 Deterministic model 191 7.4.2 Probabilistic analysis 193 7.4.3 OSF method coupled with GPC 194 7.5 Conclusion 197 7.6 References 197 List of Authors 205 Index 207 Summaries of other volumes 211