Pham Recent Advances in Reliability and Quality in Design

1. Auflage 2008
ISBN: 978-1-84800-113-8
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 524 Seiten

Reihe: Engineering (R0)

This book presents the latest theories and methods of reliability and quality, with emphasis on reliability and quality in design and modelling. Each chapter is written by active researchers and professionals with international reputations, providing material which bridges the gap between theory and practice to trigger new practices and research challenges. The book therefore provides a state-of-the-art survey of reliability and quality in design and practices.

Dr Hoang Pham is a professor in the Department of Industrial and Systems Engineering at Rutgers, The State University of New Jersey. Before joining Rutgers, he was a senior engineering specialist at the Boeing Company, Seattle, and the Idaho National Engineering Laboratory, Idaho Falls. His research interests include software reliability, system reliability modeling, maintenance, fault-tolerant computing, and biological systemability-risk assessment.

Autoren/Hrsg.

Pham, Hoang

Inhaltsverzeichnis

1;Preface;7
2;Contents;11
3;Contributors;22
4;Part I System Reliability Computing;25
4.1;Central Limit Theorem for a Family of Reliability Measures;26
4.1.1;1.1 Introduction;26
4.1.2;1.2 Fuzzy Sets Concepts;27
4.1.3;1.3 A Central Limit Theorem for Gauge Measures and Related Results;37
4.1.4;1.4 Further Examples and an Application;46
4.1.5;1.5 Conclusion;52
4.1.6;References;53
4.2;Modeling and Reliability Evaluation of Multi-state k-out-of-n Systems;54
4.2.1;2.1 Introduction;54
4.2.2;2.2 Multi-state k-out-of-n System Models;57
4.2.3;2.3 Reliability Evaluation of Multi-state k-out-of-n Systems;62
4.2.4;2.4 Conclusions;77
4.2.5;References;77
4.2.6;Further Reading;79
4.3;On Weighted Least Squares Estimation for the Parameters of Weibull Distribution;80
4.3.1;3.1 Introduction;80
4.3.2;3.2 Basic Concepts in Lifetime Data Analysis;82
4.3.3;3.3 Common Estimation Methods forWeibull Distribution;84
4.3.4;3.4 Weighted Least Squares Estimation Methods and RelatedWork;88
4.3.5;3.5 An Improved Method for CalculatingWeights;92
4.3.6;3.6 Discussions;101
4.3.7;3.7 Conclusions;103
4.3.8;References;103
4.3.9;Further Reading;104
4.3.10;Appendix;104
4.4;Periodic and Sequential Imperfect Preventive Maintenance Policies for Cumulative Damage Models;108
4.4.1;4.1 Introduction;108
4.4.2;4.2 Periodic PM;110
4.4.3;4.3 Sequential PM;113
4.4.4;4.4 PM for a Finite Interval;117
4.4.5;4.5 Conclusions;120
4.4.6;References;121
4.5;Some Alternative Approaches to System Reliability Modeling;124
4.5.1;5.1 Introduction;124
4.5.2;5.2 A New Bivariate Probability Densities Construction;126
4.5.3;5.3 Multivariate Extensions of the Bivariate Models;131
4.5.4;5.4 A Comparison with Freund, Marshall and Olkin, and some Other Models;134
4.5.5;5.5 The Transformation Method for the pdfs Construction;137
4.5.6;5.6 Extension of the Random Vector Models to Stochastic Processes;139
4.5.7;5.7 Application of k-Markovian Stochastic Processes;141
4.5.8;5.8 Maintenance Models;145
4.5.9;5.9 Additional Remarks;151
4.5.10;5.10 Some Analytic Examples;153
4.5.11;References;157
4.5.12;Further Reading;158
4.6;The Optimal Burn-in: State of the Art and New Advances for Cost Function Formulation;160
4.6.1;6.1 Introduction;160
4.6.2;6.2 State-of-art on Optimal Burn-in Research;161
4.6.3;6.3 Development of “After Burn-in Failure Treatment” (AFT) Cost Model;191
4.6.4;6.4 Conclusions;203
4.6.5;References;204
5;Part II Reliability Engineering in Design;206
5.1;Optimum Threshold Level of Degrading Systems Based on Sensor Observation;208
5.1.1;7.1 Introduction;209
5.1.2;7.2 Gamma Process Degradation Model;210
5.1.3;7.3 Imperfect Maintenance Model;211
5.1.4;7.4 Sensor Errors and Accuracy;213
5.1.5;7.5 Uptime Modeling;216
5.1.6;7.6 Threshold Level: System AvailabilityMaximization;217
5.1.7;7.7 Threshold Level: Maintenance Cost Minimization;219
5.1.8;7.8 Conclusions;221
5.1.9;References;221
5.2;Weibull Data Analysis with Few or no Failures;224
5.2.1;8.1 Introduction;224
5.2.2;8.2 Theory;225
5.2.3;8.3 Examples;226
5.2.4;8.4 Simulation Study with Only Three Failures;229
5.2.5;8.5 Conclusions;232
5.2.6;References;233
5.3;A Load-weighted Statistical Average Model of Fatigue Reliability;234
5.3.1;9.1 Introduction;234
5.3.2;9.2 Statistical Average Interpretation of SSI Model;236
5.3.3;9.3 A Statistical Load-weighted Average Model of Fatigue Reliability;238
5.3.4;9.4 Fatigue Life Distribution Under Constant Amplitude Cyclic Stress and Fatigue Reliability Calculation;241
5.3.5;9.5 Examples of Application;243
5.3.6;9.6 Conclusions;246
5.3.7;References;247
5.4;Markovian Performance Evaluation for Software System Availability with Processing Time Limit;248
5.4.1;10.1 Introduction;248
5.4.2;10.2 Markovian Software AvailabilityModel;249
5.4.3;10.3 Model Analysis;252
5.4.4;10.4 Derivation of Software Performance Measures;255
5.4.5;10.5 Numerical Examples;257
5.4.6;10.6 Concluding Remarks;260
5.4.7;References;261
5.5;Failure Probability Estimation of Long Pipeline;262
5.5.1;11.1 Introduction;262
5.5.2;11.2 Segment Partition and System Strength Distribution;263
5.5.3;11.3 Pipeline Failure Probability Estimation and Failure Dependence Analysis;267
5.5.4;11.4 Pipeline Failure Probability Estimation;268
5.5.5;11.5 Upper Limit of Large-scale Series System Failure Probability;269
5.5.6;11.6 Pipeline Reliability Under Randomly Repeated Load;271
5.5.7;11.7 Conclusion;273
5.5.8;References;274
6;Part III Software Reliability and Testing;276
6.1;Software Fault Imputation in Noisy and Incomplete Measurement Data;278
6.1.1;12.1 Introduction;278
6.1.2;12.2 Empirical Datasets;279
6.1.3;12.3 Imputation Techniques;281
6.1.4;12.4 Missing Data Mechanisms;284
6.1.5;12.5 Experimental Design;285
6.1.6;12.6 Statistical Analysis;287
6.1.7;12.7 Conclusion;294
6.1.8;References;296
6.2;A Linearized Growth Curve Model for Software Reliability Data Analysis;298
6.2.1;13.1 Introduction;298
6.2.2;13.2 Generalization of Growth Curve Models;299
6.2.3;13.3 Parameter Estimation;303
6.2.4;13.4 Examples of Data Analysis and Discussion;304
6.2.5;13.5 Concluding Remarks;312
6.2.6;References;313
6.3;Software Reliability Model Considering Time- delay Fault Removal;314
6.3.1;14.1 Introduction;314
6.3.2;14.2 Model Formulation;316
6.3.3;14.3 Numerical Examples;321
6.3.4;14.4 Concluding Remarks;328
6.3.5;References;328
6.3.6;Further Reading;330
6.4;Heuristic Component Placement for Maximizing Software Reliability;332
6.4.1;15.1 Introduction;332
6.4.2;15.2 Overview;333
6.4.3;15.3 Analysis and Optimization Methodologies;336
6.4.4;15.4 Illustrations;340
6.4.5;15.5 Related Research;351
6.4.6;15.6 Conclusions and Future Research;351
6.4.7;References;352
6.5;Software Reliability Growth Models Based on Component Characteristics;354
6.5.1;16.1 Introduction;354
6.5.2;16.2 Module Composition;355
6.5.3;16.3 Software Reliability Growth Modeling;357
6.5.4;16.4 Numerical Examples for Software Reliability Analysis;361
6.5.5;16.5 Concluding Remarks;364
6.5.6;References;365
7;Part IV Quality Engineering in Design;366
7.1;Statistical Analysis of Appearance Quality for Automotive Rubber Products;368
7.1.1;17.1 Introduction;368
7.1.2;17.2 Description of Product and Defect Phenomenon;369
7.1.3;17.3 Identification of Bloom Phenomenon;370
7.1.4;17.4 Orthogonal Arrays;370
7.1.5;17.5 Analysis of Swell;372
7.1.6;17.6 Analysis of;374
7.1.7;17.7 Discriminant Analysis for Swell Measures;377
7.1.8;17.8 Multiple Regression Analysis for CS Measures;378
7.1.9;17.9 Concluding Remarks;383
7.1.10;References;383
7.2;Present Worth Design of Engineering Systems with Degrading Components;384
7.2.1;18.1 Introduction;384
7.2.2;18.2 Modeling of Time-variant Systems;386
7.2.3;18.3 Cumulative Distribution Function Modeling;388
7.2.4;18.4 Formulation of Economic Design Problems;389
7.2.5;18.5 Case Study;391
7.2.6;18.6 Conclusions;396
7.2.7;References;397
7.3;Economic-statistical Design of a Logarithmic Transformed S2 EWMA Chart;398
7.3.1;19.1 Introduction;398
7.3.2;19.2 Literature Review;399
7.3.3;19.3 The Logarithmic Transformed S2 EWMA Chart;402
7.3.4;19.4 The Economic Design of the S2 EWMA;407
7.3.5;19.5 The Economic Statistical Design of the S2 EWMA: a Numerical Analysis;415
7.3.6;19.6 Conclusions;426
7.3.7;References;427
7.4;Risk Management Techniques for Quality Software Development;430
7.4.1;20.1 Introduction;430
7.4.2;20.2 Project Risk Management;432
7.4.3;20.3 Project Effect Analysis;438
7.4.4;20.4 Conclusions;443
7.4.5;References;443
8;Part V Application in Engineering Design;444
8.1;Recent Advances in Data Mining for Categorizing Text Records;446
8.1.1;21.1 Introduction;446
8.1.2;21.2 Text Mining in Practice;448
8.1.3;21.3 Background in Data Mining;450
8.1.4;21.4 State-of-the-art in Data Mining;453
8.1.5;21.5 Research Challenges;461
8.1.6;References;461
8.1.7;Further Reading;462
8.2;Quality in Design: User-oriented Design of Public Toilets for Visually Impaired People;464
8.2.1;22.1 Difficulties and Consequences for VIP in Accessing Public Environments;464
8.2.2;22.2 Deficiencies in Public Toilets for VIP;466
8.2.3;22.3 Studies on Accessibility of Public Toilets for VIP;467
8.2.4;22.4 Key Areas of Design Quality for Consideration;468
8.2.5;22.5 FISH: Better Designs of Public Toilets for VIP;472
8.2.6;22.6 Conclusions;485
8.2.7;References;486
8.3;Assurance Cases for Reliability: Reducing Risks to Strengthen ROI for SCADA Systems;488
8.3.1;23.1 Introduction;488
8.3.2;23.2 Approach;489
8.3.3;23.3 SCADA Security and RAM Issues – An Overview;490
8.3.4;23.4 Risk Identification;491
8.3.5;23.5 Mapping Technical Risks into Business Risks;496
8.3.6;23.6 Risk Assessment;504
8.3.7;23.7 Goal-based Assurance Case Approach;508
8.3.8;23.8 Summary and Conclusion;511
8.3.9;References;512
8.4;Detecting Driver’s Emotion: A Step Toward Emotion- based Reliability Engineering;514
8.4.1;24.1 Background;514
8.4.2;24.2 Emotion;516
8.4.3;24.3 Observation of Actual Driving;516
8.4.4;24.4 Experiments Using Driving Simulator;517
8.4.5;24.5 Facial Emotional Expression;518
8.4.6;24.6 Detection of Fatigue;524
8.4.7;24.7 Detection of Dangerous Actions;526
8.4.8;24.8 Detection of Emotion from Voice;527
8.4.9;References;529
8.5;Mortality Modeling Perspectives;532
8.5.1;25.1 Introduction;532
8.5.2;25.2 Literature Discussions;533
8.5.3;25.3 Mortality Modeling;535
8.5.4;References;537
8.5.5;Further Reading;539
9;About the Editor;540
10;Index;542