What is included with this book?
Preface | p. v |
Competing Risk Modeling in Reliability | p. 1 |
Introduction | p. 1 |
Independent and Dependent Competing Risks | p. 3 |
Characterization of Possible Marginals | p. 5 |
Kolmogorov-Smirnov Test | p. 7 |
Conservatism of Independence | p. 8 |
The Bias of Independence | p. 9 |
Maintenance as a Censoring Mechanism | p. 10 |
Dependent copula model | p. 11 |
Random clipping | p. 11 |
Random signs | p. 12 |
LBL model | p. 12 |
Mixed exponential model | p. 13 |
Delay time model | p. 13 |
Loosening the Renewal Assumption | p. 13 |
Conclusion | p. 15 |
References | p. 15 |
Game-Theoretic and Reliability Methods in Counter-Terrorism and Security | p. 17 |
Introduction | p. 17 |
Applications of Reliability Analysis to Security | p. 18 |
Applications of Game Theory to Security | p. 19 |
Security as a game between defenders | p. 21 |
Combining Reliability Analysis and Game Theory | p. 23 |
Directions for Future Work | p. 24 |
Conclusion | p. 25 |
Acknowledgments | p. 25 |
References | p. 26 |
Regression Models for Reliability Given the Usage Accumulation History | p. 29 |
Introduction | p. 29 |
Definitions and notation | p. 30 |
Common lifetime regression models | p. 31 |
Other Approaches to Regression Model Building | p. 32 |
Models based on transfer functionals | p. 33 |
Models based on internal wear | p. 34 |
Collapsible Models | p. 36 |
Two-dimensional prediction problems | p. 36 |
Discussion | p. 38 |
Acknowledgments | p. 38 |
References | p. 38 |
Bayesian Methods for Assessing System Reliability: Models and Computation | p. 41 |
Challenges in Modern Reliability Analyses | p. 42 |
Three Important Examples | p. 42 |
Example 1: Reliability of a component based on biased sampling | p. 43 |
Example 2: System reliability based on partially informative tests | p. 44 |
Example 3: Integrated system reliability based on diverse data | p. 45 |
YADAS: a Statistical Modeling Environment | p. 46 |
Expressing arbitrary models | p. 46 |
Special algorithms | p. 48 |
Interfaces, present and future | p. 48 |
Examples Revisited | p. 49 |
Example 1 | p. 49 |
Example 2 | p. 49 |
Example 3 | p. 50 |
Discussion | p. 51 |
References | p. 54 |
Dynamic Modeling in Reliability and Survival Analysis | p. 55 |
Introduction | p. 55 |
Dynamic Models | p. 59 |
A dynamic reliability model | p. 60 |
A dynamic recurrent event model | p. 61 |
Some Probabilistic Properties | p. 63 |
Inference Methods | p. 64 |
Dynamic load-sharing model | p. 64 |
Dynamic recurrent event model | p. 65 |
An Application | p. 68 |
References | p. 70 |
End of Life Analysis | p. 73 |
The Urgency of WEEE | p. 74 |
The effect on reliability | p. 74 |
The implications for design | p. 75 |
Signature Analysis and Hierarchical Modeling | p. 75 |
The importance of function | p. 76 |
Wavelets and feature extraction | p. 76 |
A Case Study | p. 76 |
Function: preliminary FMEA and life tests | p. 77 |
Stapler motor: time domain | p. 77 |
Lifting motor: frequency domain | p. 78 |
The Development of Protocols and Inversion | p. 79 |
Acknowledgments | p. 86 |
References | p. 86 |
Reliability Analysis of a Dynamic Phased Mission System: Comparison of Two Approaches | p. 87 |
Introduction | p. 87 |
Test Case Definition | p. 91 |
Test Case Resolution | p. 93 |
Resolution with a Petri net | p. 93 |
Resolution with a BDMP | p. 96 |
Compared results | p. 97 |
Conclusions | p. 100 |
References | p. 103 |
Sensitivity Analysis of Accelerated Life Tests with Competing Failure Modes | p. 105 |
Introduction | p. 106 |
ALT and Competing Risks | p. 107 |
ALT and independent competing risks | p. 107 |
ALT and dependent competing risks | p. 109 |
Graphical Analysis of Motorettes Data | p. 110 |
A Copula Dependent ALT - Competing Risk Model | p. 112 |
Competing risks and copula | p. 112 |
Measures of association | p. 113 |
Archimedean copula | p. 114 |
Application on motor insulation data | p. 114 |
Conclusions | p. 116 |
References | p. 117 |
Estimating Mean Cumulative Functions From Truncated Automotive Warranty Data | p. 121 |
Introduction | p. 121 |
The Hu and Lawless Model | p. 123 |
Extensions of the Model | p. 124 |
"Time" is age case | p. 124 |
"Time" is miles case | p. 127 |
Example | p. 130 |
The "P-claims" dataset | p. 130 |
Examples for the "time" is age case | p. 131 |
Examples for the "time" is miles case | p. 133 |
Discussion | p. 133 |
References | p. 134 |
Tests for Some Statistical Hypotheses for Dependent Competing Risks-A Review | p. 137 |
Introduction | p. 137 |
Locally Most Powerful Rank Tests | p. 139 |
Tests for Bivariate Symmetry | p. 140 |
Censored Data | p. 144 |
Simulation Results | p. 145 |
Test for Independence of T and [delta] | p. 147 |
Testing H[subscript 0] against H'[subscript 1] | p. 148 |
Testing H[subscript 0] against H'[subscript 2] | p. 149 |
Testing H[subscript 0] against H'[subscript 3] | p. 150 |
Acknowledgments | p. 151 |
References | p. 151 |
Repair Efficiency Estimation in the ARI[subscript 1] Imperfect Repair Model | p. 153 |
Introduction | p. 153 |
Arithmetic Reduction of Intensity Model with Memory 1 | p. 154 |
Counting process theory | p. 154 |
Imperfect repair models | p. 155 |
Failure Process Behavior | p. 156 |
Minimal and maximal wear intensities | p. 156 |
Asymptotic intensity | p. 157 |
Second order term of the asymptotic expanding | p. 159 |
Repair Efficiency Estimation | p. 160 |
Maximum likelihood estimators | p. 161 |
Explicit estimators | p. 163 |
Empirical Results | p. 164 |
Finite number of observed failures | p. 164 |
Application to real maintenance data set and perspective | p. 165 |
Classical Convergence Theorems | p. 166 |
References | p. 167 |
On Repairable Components with Continuous Output | p. 169 |
Introduction | p. 169 |
Asymptotic Performance of Repairable Components | p. 171 |
Simple Systems | p. 173 |
Imperfect Repair | p. 174 |
Concluding Remarks | p. 175 |
References | p. 176 |
Effects of Uncertainties in Components on the Survival of Complex Systems with Given Dependencies | p. 177 |
Introduction | p. 177 |
System Reliability with Dependent Components | p. 179 |
Bounds on the Margins | p. 181 |
Uniform metric | p. 182 |
Quantiles | p. 183 |
Expectation | p. 184 |
Bayesian Approach | p. 186 |
Comparisons | p. 188 |
References | p. 188 |
Dynamic Management of Systems Undergoing Evolutionary Acquisition | p. 191 |
Setting | p. 192 |
Preamble: broad issues | p. 192 |
Testing | p. 194 |
Modeling an Evolutionary Step | p. 195 |
Model for development of Block b + 1 | p. 195 |
Introduction of design defects during development and testing | p. 196 |
Examples of mission success probabilities with random K[subscript 0] | p. 197 |
Acquisition of Block b + 1 | p. 198 |
Obsolescence of Block b and Block b + 1 | p. 199 |
The Decision Problem | p. 199 |
Examples | p. 200 |
Conclusion and Future Program | p. 203 |
References | p. 204 |
Reliability Analysis of Renewable Redundant Systems with Unreliable Monitoring and Switching | p. 205 |
Introduction | p. 205 |
Problem Statement | p. 206 |
Asymptotic Approach. General System Model | p. 207 |
Refined System Model and the FS Criterion | p. 207 |
Estimates of Reliability and Maintainability Indexes | p. 209 |
Examples | p. 210 |
Heuristic Approach. Approximate Method of Analysis of Renewal Duplicate System | p. 214 |
References | p. 218 |
Planning Models for Component-Based Software Offerings Under Uncertain Operational Profiles | p. 221 |
Introduction | p. 221 |
Mathematical Formulation as an Optimal Planning Problem | p. 223 |
Stochastic Optimal Reliability Allocation | p. 224 |
Derivation of the distribution for G[subscript 0] | p. 226 |
Solution implementation | p. 228 |
Examples | p. 228 |
Summary and Discussion | p. 230 |
Acknowledgments | p. 232 |
References | p. 233 |
Destructive Stockpile Reliability Assessments: A Semiparametric Estimation of Errors in Variables with Validation Sample Approach | p. 235 |
Introduction | p. 235 |
Preliminaries | p. 237 |
Estimation | p. 238 |
Proofs | p. 240 |
Regularity conditions | p. 240 |
Proof of Theorem 17.2 | p. 241 |
Acknowledgments | p. 244 |
References | p. 244 |
Flowgraph Models for Complex Multistate System Reliability | p. 247 |
Introduction | p. 247 |
Background on Flowgraph Models | p. 250 |
Flowgraph Data Analysis | p. 255 |
Numerical Example | p. 257 |
Conclusion | p. 260 |
References | p. 261 |
Interpretation of Condition Monitoring Data | p. 263 |
Introduction | p. 263 |
The Proportional Hazards Model | p. 265 |
Managing Risk: A CBM Optimization Tool | p. 270 |
Case Study Papers | p. 272 |
Food processing: use of vibration monitoring | p. 272 |
Coal mining: use of oil analysis | p. 273 |
Nuclear generating station | p. 274 |
Gearbox subject to tooth failure | p. 276 |
Future Research Plans | p. 277 |
References | p. 277 |
Nonproportional Semiparametric Regression Models for Censored Data | p. 279 |
Introduction | p. 279 |
Models and Estimation | p. 280 |
Accelerated failure time model | p. 280 |
Rank-based approach | p. 281 |
Least-squares approach | p. 284 |
Linear transformation models | p. 286 |
Remark | p. 289 |
Acknowledgments | p. 290 |
References | p. 290 |
Binary Representations of Multi-State Systems | p. 293 |
Introduction | p. 293 |
Basic Definitions | p. 294 |
Binary Representation of an MSS and Its Properties | p. 296 |
Examples of Application | p. 301 |
Conclusions | p. 305 |
Acknowledgments | p. 306 |
References | p. 306 |
Distribution-Free Continuous Bayesian Belief Nets | p. 309 |
Introduction | p. 309 |
Vines and Copulae | p. 311 |
Continuous bbns | p. 315 |
Example: Flight Crew Alertness Model | p. 318 |
Conclusions | p. 321 |
References | p. 321 |
Statistical Modeling and Inference for Component Failure Times Under Preventive Maintenance and Independent Censoring | p. 323 |
Introduction | p. 323 |
Notation, Definitions, and Basic Facts | p. 325 |
The Repair Alert Model | p. 326 |
Statistical Inference in the Repair Alert Model | p. 328 |
Independent censoring | p. 328 |
Datasets and preliminary graphical model checking | p. 329 |
Nonparametric estimation | p. 331 |
Parametric estimation | p. 332 |
Concluding Remarks | p. 335 |
References | p. 337 |
Importance Sampling for Dynamic Systems | p. 339 |
Introduction | p. 340 |
Problem Formulation. Reliability and Failure Probability | p. 341 |
Numerical Examples | p. 343 |
Linear oscillator excited by white noise | p. 343 |
Linear oscillator excited by colored noise | p. 348 |
Conclusions | p. 350 |
Acknowledgments | p. 351 |
References | p. 351 |
Leveraging Remote Diagnostics Data for Predictive Maintenance | p. 353 |
Introduction | p. 353 |
Accounting for the Accumulation of Wear | p. 354 |
Application to Inventory Management of Turbine Blades | p. 356 |
Developing an Optimal Solution | p. 357 |
Application | p. 360 |
Formulating a Generalized Life Regression Model | p. 361 |
Concluding Remarks | p. 362 |
Acknowledgments | p. 362 |
References | p. 362 |
From Artificial Intelligence to Dependability: Modeling and Analysis with Bayesian Networks | p. 365 |
Introduction | p. 366 |
Bayesian Networks | p. 366 |
Mapping Fault Trees to Bayesian Networks | p. 367 |
Case Studies: The Digicon Gas Turbine Controller | p. 368 |
Modeling Issues | p. 371 |
Probabilistic gates: common cause failures | p. 372 |
Probabilistic gates: coverage | p. 372 |
Multi-state variables | p. 373 |
Sequentially dependent failures | p. 374 |
Analysis Issues | p. 375 |
Analysis example | p. 376 |
Modeling parameter uncertainty in BN model | p. 378 |
Conclusions and Current Research | p. 380 |
References | p. 380 |
Reliability Computation for Usage-Based Testing | p. 383 |
Motivation | p. 383 |
Characterizing Use | p. 384 |
Computing Reliability | p. 387 |
Models | p. 387 |
Arc reliabilities | p. 388 |
Trajectory failure rate | p. 389 |
Similarity to Expected Use | p. 390 |
Conclusion | p. 392 |
References | p. 392 |
K-Mart Stochastic Modeling Using Iterated Total Time on Test Transforms | p. 395 |
Introduction | p. 395 |
Generalized Convexity, Iterated TTT, K-Mart | p. 397 |
Mixture Models | p. 400 |
A Binomial Example | p. 403 |
Construction of "Most Identical" Distribution | p. 407 |
References | p. 409 |
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