Engineering Design Reliability Handbook

✍ Scribed by Efstratios Nikolaidis, Dan M. Ghiocel, Suren Singhal

• Publisher: CRC Press
• Year: 2004
• Tongue: English
• Leaves: 474
• Edition: 1
• Category: Library

✦ Synopsis

This is a well written and comprehensive book on reliability design. Covers both basic concepts and advanced topics. Many applications of reliability design to real life problems are presented. Highly recommended.

✦ Table of Contents

Engineering Design Reliability HANDBOOK......Page 1
Reliability Handbook......Page 4
Preface......Page 6
About the Editors......Page 8
Contributors......Page 9
Disclaimer......Page 16
Contents......Page 12
Section I Status and Future of Nondeterministic Approaches ( NDAs)......Page 17
Contents......Page 0
1.1.2 Part II: Nondeterministic Modeling: Critical Issues and Recent Advances......Page 19
1.1.2.2 Reliability Assessment and Uncertainty Propagation......Page 20
1.1.2.4 Reliability Certification......Page 21
1.1.3 Part III: Applications......Page 22
2.1 Introduction......Page 23
2.1.3 Chapter Outline......Page 25
2.2 Types of Uncertainties and Uncertainty Measures......Page 26
2.4 Nondeterministic Analysis Approaches: Categories......Page 27
2.4.1 Bounding Uncertainties in Simulation Models......Page 28
2.5.1 Advanced Virtual Product Development Facilities......Page 29
2.5.3 Uncertainty Quantification......Page 30
2.6 Verification and Validation of Numerical Simulations......Page 31
2.7.3 Performance......Page 32
2.8 Response Surface Methodology (RSM)......Page 33
2.10 Robustness......Page 34
2.12 Key Components of Advanced Simulation and Modeling Environments......Page 35
2.12.1 Intelligent Tools and Facilities......Page 36
2.13 Nondeterministic Approaches Research and Learning Network......Page 37
References......Page 39
3.1 Introduction......Page 42
3.2 The Future Nondeterministic Design Environment......Page 43
3.3.1 Current Nondeterministic Design Technology......Page 47
3.3.2 Some Transition History......Page 48
3.3.2.2 Use of Weibull Models in Aircraft Engine Field Support......Page 49
3.3.2.4 Probability Integration Algorithms......Page 50
3.3.3 Finite Element Based Probabilistic Analysis......Page 51
3.4.1 Neural Networks [61Ò66]......Page 52
3.4.3 Interval Arithmetic [82-85]......Page 53
3.4.7 Expert Systems......Page 54
3.5.2 Verification and Validation of NDA Methods......Page 55
3.5.3 NDA Error Estimation......Page 57
3.5.4 Confidence Interval Modeling......Page 58
3.5.5 Traditional Reliability and Safety Assessment Methods......Page 59
Acknowledgments......Page 60
References......Page 61
4.1 Introduction......Page 66
4.2 The Business Case......Page 67
4.3 Strategies for Design Approach for Low-Cost Development......Page 69
4.4 Role of Design Space Exploration Approaches (Deterministic and Nondeterministic) in the Product Development Phase......Page 70
4.5 Sensitivity Analysis......Page 73
4.6 Probabilistic Analysis Approaches......Page 74
4.7 The Need and Role of Multidisciplinary Analysis in Deterministic and Nondeterministic Analysis......Page 76
4.8 Technology Transition and Software Implementation......Page 77
4.9 Needed Technology Advances......Page 78
Acknowledgments......Page 79
5.1 Introduction......Page 80
5.2 The Vehicle Development Process......Page 81
5.3 Vehicle Development Process: A Decision-Analytic View......Page 82
5.4 The Decision Analysis Cycle......Page 83
5 .4.1 Illustration: Door Seal Selection......Page 84
5.4.1.1 The Door Sealing System......Page 85
5.4.2 Deterministic Phase......Page 86
5.4.2.1 Door Seal Selection Continued: Deterministic Phase......Page 87
5.4.3.1 Uncertainty Characterization Methods......Page 89
5.4.3.2 Door Seal Selection Continued: Probabilistic Phase......Page 90
5.4.4.2 Model Validation: Current Practice......Page 92
5.4.4.3 Model Validation: A Formal Process......Page 93
5.4.4.4 Door Seal Selection Continued: Information Phase......Page 94
5.5 Concluding Comments and Challenges......Page 95
References......Page 96
6.1 Introduction......Page 98
6.2.2 Research Needs and Trends......Page 99
6.3.1 Current Methods......Page 101
6.3.2 Research Needs and Trends......Page 102
6.4.2 Research Needs and Trends......Page 104
6.5.1 Current Methods......Page 105
6.5.2 Research Needs and Trends......Page 106
References......Page 107
7.1 Background......Page 109
7.1.1 Critical Element of the Design Process......Page 110
7.1.2 The Need for a New Approach......Page 111
7.2 An Inductive Approach......Page 113
7.2.1.2 Hierarchical Bayes......Page 114
7.3 Information Aggregation......Page 115
7.3.2 Data Congeries......Page 116
7.4.1 Distribution or Process Model?......Page 117
T......Page 118
7.5 Value of Information......Page 119
7.7 Summary......Page 121
References......Page 122
Section II Nondeterministic Modeling: Critical Issues and Recent Advances......Page 124
uncertainty......Page 127
X......Page 128
X, X......Page 130
Example 3: Uncertainty in Design of the Automotive Component in Example 1......Page 131
of an Automotive Component......Page 132
Example 5: Different Types of Uncertainty in Automotive Component Design......Page 135
8.2.2 Taxonomies According to the Nature of Uncertainty......Page 136
Randomly from a Batch......Page 137
8.3.1 Types of Uncertainty......Page 138
of an Automotive Component......Page 139
8.3.2 Types of Information......Page 140
When We Only Know the Stress Range......Page 141
Y,......Page 142
of a Chemical Solvent......Page 143
8.4 Chapters in This Book on Methods for Collecting Information and for Constructing Models of Uncertainty......Page 144
References......Page 145
9.1 Introduction: Uncertainty-Based Information Theory in Modeling and Simulation......Page 147
9.2.1 Logical and Set Theoretical Approaches......Page 149
Borel field......Page 150
normalization......Page 152
cumulative distribution......Page 153
9.2.3.2 Interpretations of Probability......Page 154
Law of Total Probability......Page 155
9.2.3.4 Distribution Function Formulation of Bayes Theorem......Page 156
9.2.3.5 Binomial/Beta Reliability Example......Page 157
9.3.1 Historical Development of GIT......Page 158
Norms and Conorms:......Page 159
membership function......Page 160
Proposition 7:......Page 161
Implication:......Page 162
fuzzy quantity......Page 163
U......Page 164
UU......Page 165
linguistic variable.......Page 166
normal......Page 167
plausibility......Page 168
C......Page 169
random variable.......Page 170
9.3.5.3 The Information Content of a Random Set......Page 171
specific:......Page 172
U(......Page 173
p-box......Page 174
possibility distribution......Page 175
necessity measure:......Page 176
C=......Page 177
C=......Page 178
9.3.6.4 Relations between Probabilistic and Possibilistic Concepts......Page 179
9.4 Conclusion and Summary......Page 180
R......Page 182
53,......Page 184
4,......Page 185
30:......Page 186
10.1.1 Background......Page 187
10.1.2 Improved Models for Epistemic Uncertainty......Page 189
10.2.1 Belief, Plausibility, and BPA Functions......Page 190
10.2.2 Cumulative and Complementary Cumulative Functions......Page 192
10.2.3 Input/Output Uncertainty Mapping......Page 194
10.2.4 Simple Conceptual Examples......Page 196
Example 2......Page 197
10.3.1 Problem Description......Page 198
10.3.2.1 Combination of Evidence......Page 200
10.3.2.2 Construction of Probabilistic Response......Page 201
10.3.3.1 Construction of Basic Probability Assignments for Individual Inputs......Page 203
10.3.3.2 Combination of Evidence......Page 204
10.3.3.3 Construction of Basic Probability Assignments for the Product Space......Page 205
10.3.3.4 Construction of Belief and Plausibility for the System Response......Page 206
10.3.4 Comparison and Interpretation of Results......Page 211
10.4 Research Topics in the Application of Evidence Theory......Page 212
References......Page 213
11.1 Introduction and Overview......Page 217
11.2 Design of a Cantilever with Uncertain Load......Page 218
11.2.1 Performance Optimization......Page 219
11.2.2 Robustness to Uncertain Load......Page 221
11.2.3 Info-Gap Robust-Optimal Design: Clash with Performance- Optimal Design......Page 223
11.2.4 Resolving the Clash......Page 224
11.2.5 Opportunity from Uncertain Load......Page 225
11.3.1 Model Uncertainty......Page 227
11.3.2 Performance Optimization with the Best Model......Page 228
11.3.3 Robustness Function......Page 229
11.3.4 Example......Page 231
11.4.2 Uncertainty and Robustness......Page 233
11.4.3 Example......Page 235
11.5.1 Info-Gap Robustness as a Decision Monitor......Page 238
11.5.2 Nonlinear Spring......Page 239
11.6.1 The Basic Lemma......Page 241
11.6.2 Optimal-Performance vs. Optimal Robustness: The Theorem......Page 243
11.6.3 Information-Gap Models of Uncertainty......Page 244
11.7 Conclusion: A Historical Perspective......Page 245
References......Page 246
12.1 Introduction......Page 247
12.2.1 Definitions, Applications, and Scope......Page 248
12.2.2 Dependency......Page 251
12.2.4 Linear Interval Equations......Page 252
12.3 Interval Methods for Predicting System Response Due to Uncertain Parameters......Page 254
12.3.2 Interval Finite Element Methods......Page 255
but physically inconsistent)......Page 257
underlying physics......Page 258
12.4.1 Approximation Errors......Page 263
12.4.2 Rounding-off Errors......Page 264
12.5 Future Developments of Interval Methods for Reliable Engineering Computations......Page 265
References......Page 267
Appendix 12.1 Interval Arithmetic Operations......Page 269
13.1 Introduction......Page 271
13.2.1.2 Knowledge Concepts......Page 273
13.2.1.3 Roles......Page 274
13.2.2.1 Criteria for an Expert Elicitation Approach......Page 275
13.2.3.1 Identifying the Adviser-Expert......Page 276
13.2.3.2 Defining a Collaborative Strategy......Page 277
13.2.4.1 What Is a Knowledge Model?......Page 278
13.2.4.2 Process for Eliciting Expertise......Page 279
13.2.4.3 Knowledge Representation Techniques for Modeling Expertise......Page 280
13.2.4.4 Success and Failure for the Problem Area......Page 283
13.3.1.1 What Is Expert Judgment?......Page 284
13.3.1.2 What to Elicit?......Page 285
13.3.1.3 Identifying the Experts......Page 286
13.3.1.5 Expert Cognition and the Problem of Bias......Page 287
13.3.1.7 Question Phrasing......Page 290
13.3.2 A Modified Delphi for Reliability Analysis......Page 292
13.4.1 Characterizing Uncertainties......Page 293
13.4.1.3 Aggregation of Expert Judgments......Page 296
13.4.2.1 How Expert Knowledge Combines with Other Information......Page 298
References......Page 299
14.1 Introduction......Page 302
14.2.1 Statistically Independent Random Variables......Page 304
14.2.3 Random Variables with Nataf Distribution......Page 305
14.2.4 Dependent Nonnormal Random Variables......Page 306
14.3.1 Component Reliability by FORM......Page 307
14.3.2 System Reliability by FORM......Page 310
14.3.2.1 Example: Series System Reliability Analysis of a Frame by FORM......Page 313
14.3.3 FORM Importance and Sensitivity Measures......Page 314
14.4 The Second-Order Reliability Method......Page 316
14.5 Time-Variant Reliability Analysis......Page 320
14.5.1 Example: Mean Out-Crossing Rate of a Column under Stochastic Loads......Page 322
References......Page 323
15.1 Introduction......Page 326
15.2.2 Fundamental Systems......Page 327
15.2.3 Modeling of Systems at Level N......Page 329
15.2.5 Formal Representation of Systems......Page 331
15.3.1 Introduction......Page 335
15.3.2 Assessment of the Probability of Failure of Series Systems......Page 337
15.3.3 Reliability Bounds for Series Systems......Page 338
15.3.4 Series Systems with Equally Correlated Elements......Page 340
15.3.5 Series Systems with Unequally Correlated Elements......Page 342
15.4.1 Introduction......Page 343
15.4.2 Assessment of the Probability of Failure of Parallel Systems......Page 344
15.4.3 Reliability Bounds for Parallel Systems......Page 345
15.4.4 Equivalent Linear Safety Margins for Parallel Systems......Page 347
15.4.5 Parallel Systems with Equally Correlated Elements......Page 349
15.5.1 Introduction......Page 351
15.5.3 Assessment of System Reliability at Level 2......Page 352
15.5.4 Assessment of System Reliability at Level N > 2......Page 353
15.5.5 Assessment of System Reliability at Mechanism Level......Page 354
15.5.6 Examples......Page 357
15.6.1 Reliability of a Tubular Joint......Page 363
15.6.2 Reliability-Based Optimal Design of a Steel-Jacket Offshore Structure......Page 365
15.6.3 Reliability-Based Optimal Maintenance Strategies......Page 366
References......Page 368
16.1 Introduction......Page 371
16.3 Probability Distributions of the ÏQuantum Mass RatioÓ and Its Logarithm......Page 372
16.4 Logarithmic Variance and Other Statistics of the Ï Quantum Mass RatioÓ......Page 374
16.5 Probability Distribution of the ÏQuantum Size RatioÓ......Page 379
16.6 Extensions and Applications to Reliability Analysis......Page 381
16.7 Conclusion......Page 382
References......Page 383
17.1 Introduction and Objectives......Page 384
17.2.1 Modeling Random Processes......Page 385
17.2.2 Calculation of the Response......Page 388
17.2.3 Failure Analysis......Page 391
17.3 Evaluation of Stochastic Response and Failure Analysis: Linear Systems......Page 393
17.3.1 Evaluation of Stochastic Response......Page 394
17.3.2 Failure Analysis......Page 395
17.4 Evaluation of the Stochastic Response of Nonlinear Systems......Page 400
17.6 Conclusion......Page 401
References......Page 402
18.1 Introduction......Page 403
18.2 Loads as Processes: Upcrossings......Page 404
18.4 First Passage Probability......Page 405
18.5 Estimation of Upcrossing Rates......Page 406
18.6 Estimation of the Outcrossing Rate......Page 407
18.8 Time-Dependent Structural Reliability......Page 408
18.9.2 Gaussian Processes and Linear Limit State Functions......Page 409
18.9.4 Directional Simulation......Page 410
18.9.5 Ensembled Upcrossing Rate (EUR) Approach......Page 411
18.9.7 Summary of Solution Methods......Page 412
18.10 Load Combinations......Page 413
18.11 Some Remaining Research Problems......Page 415
References......Page 416
19.1 Introduction......Page 418
19.2.1 Basic Formulation......Page 420
19.2.2 Linear Models and Regression......Page 421
19.2.3 Analysis of Variance......Page 422
19.2.4 First- and Second-Order Polynomials......Page 425
19.2.5 Exponential Relationships......Page 426
19.3.1 Transformations......Page 427
19.3.2 Saturated Designs......Page 428
19.3.3 Redundant Designs......Page 429
19.3.4 Comparison......Page 430
19.4.2 Error Checking and Adaptation of the Response Surface......Page 431
19.5.1 Linear Response Surface......Page 432
19.5.2 Nonlinear Response Surface......Page 434
19.5.3 Nonlinear Finite Element Structure......Page 436
19.6 Recommendations......Page 438
References......Page 439
20.1 Introduction......Page 441
20.2.2 Inverse Probability Transformation......Page 443
20.2.5 Density Decomposition......Page 444
20.3.1 Gaussian Vectors......Page 445
20.3.2 NonGaussian Vectors......Page 446
20.4 Stochastic Fields (or Processes)......Page 448
20.4.1 One-Level Hierarchical Simulation Models......Page 451
20.4.2 Two-Level Hierarchical Simulation Models......Page 456
20.5.1 Sequential Importance Sampling (SIS)......Page 461
20.5.2 Dynamic Monte Carlo (DMC)......Page 462
20.5.3 Computing Tail Distribution Probabilities......Page 465
20.5.4 Employing Stochastic Linear PDE Solutions......Page 468
20.5.5 Incorporating Modeling Uncertainties......Page 470
20.6 Summary......Page 471
References......Page 472