Springer Series in Reliability Engineering

Series Editor

Professor Hoang PhamDepartment of Industrial and Systems EngineeringRutgers, The State University of New Jersey96 Frelinghuysen RoadPiscataway, NJ 08854-8018USA

Other titles in this series

The Universal Generating Function in Reliability Analysis and OptimizationGregory Levitin

Warranty Management and Product ManufactureD.N.P Murthy and Wallace R. Blischke

Maintenance Theory of ReliabilityToshio Nakagawa

System Software ReliabilityHoang Pham

Reliability and Optimal MaintenanceHongzhou Wang and Hoang Pham

Applied Reliability and QualityB.S. Dhillon

Shock and Damage Models in Reliability TheoryToshio Nakagawa

Risk ManagementTerje Aven and Jan Erik Vinnem

Satisfying Safety Goals by Probabilistic Risk AssessmentHiromitsu Kumamoto

Offshore Risk Assessment (2nd Edition)Jan Erik Vinnem

The Maintenance Management FrameworkAdolfo Crespo Márquez

Human Reliability and Error in Transportation SystemsB.S. Dhillon

Complex System Maintenance HandbookKhairy A.H. Kobbacy and D.N. Prabhakar Murthy

Hoang PhamEditor

Recent Advancesin Reliabilityand Quality in Design

123

Hoang Pham, PhDDepartment of Industrial and Systems EngineeringRutgers, The State University of New Jersey96 Frelinghuysen RoadPiscataway, NJ 08854-8018USA

ISBN 978-1-84800-112-1 e-ISBN 978-1-84800-113-8

DOI 10.1007/978-1-84800-113-8

Springer Series in Reliability Engineering ISSN 1614-7839

British Library Cataloguing in Publication DataRecent advances in reliability and quality in design. -

(Springer series in reliability engineering)1. Reliability (Engineering)I. Pham, Hoang620’.00452

ISBN-13: 9781848001121

Library of Congress Control Number: 2008923784

© 2008 Springer-Verlag London Limited

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This book is dedicated toDr. Thad Regulinski on his 80th birthdayfor his many years of contributionsin Reliability Engineering and profession!

Preface

Growing international competition has increased the need for all engineers and de-signers to ensure the level of quality and reliability of their products before release,and for all manufacturers to produce products at their best reliability level at thelowest cost. This implies that the interest in reliability and quality will continue togrow for many years to come.

This book comprises 25 chapters, organized in five parts: System ReliabilityComputing, Reliability Engineering in Design, Software Reliability and Testing,Quality Engineering in Design, and Applications in Engineering Design. It aims topresent the latest theories and methods of reliability and quality, with emphasis onsystems design, models and applications. The subjects covered include reliabilityengineering, maintenance, quality in design, failure analysis, robust design, soft-ware reliability and engineering, engineering reliability in design, software devel-opment process and improvement, reliability computing, software measurements,software cost effectiveness, applications in reliability design, stress-strength proba-bilistic, statistical process control, stochastic process modeling, repairable systems,safety analysis, accelerated life modeling, probabilistic modeling and risk analysis.Each chapter will be written by active researchers and/or experienced practition-ers with international reputations in the field and with the hope of bridging the gapbetween theory and practice in reliability and quality in design. Authors of manyoutstanding papers from the 12th ISSAT Conference Proceedings of the Interna-tional Conference on Reliability and Quality in Design (2006) have been invited toexpand their conference papers for contribution as chapters to this book.

The book consists of five parts. Part I of the book contains five papers, dealswith different aspects of System Reliability Computing. Chapter 1 by Zeephongsekuldescribes in detail the characteristic of central limit theorem for a reliability mea-sure, called gauge measure, which is based on a marked point process. It also dis-cusses directions of applications related to this new reliability measure. Chapter 2by Tian, Li, and Zuo discusses a recent advance in the modeling and reliability eval-uation of multi-state k out of n systems and its applications in engineering. Chap-ter 3 by Zhang, Xie, and Tang discusses a method for the parameter estimation ofWeibull distribution when there is no censoring using weighted least squares estima-

vii

viii Preface

tion. They also present a simple approximation formula to calculate the weights fora small sample of size. Chapter 4 by Nakagawa and Mizutani discusses several char-acteristics of periodic cumulative damage models where the total damage is additive.The derivations of obtaining the optimum replacement policies along with numeri-cal examples are also discussed. Chapter 5 by Filus and Filus presents an overviewand the development concepts of stochastic reliability modeling approaches. Someanalytical description and application of stochastic dependences such as condition-ing method and transformations method are also discussed. Chapter 6 by Liu andMazzuchi discusses a comprehensive literature review on the various burn-in as-pects respect to cost functions. The authors also discuss various cost optimizationmodels and their warranty policies considering the concept of “per-item-output”.

Part II of the book contains five papers, and focuses on Reliability Engineeringin Design.

Chapter 7 by Elsayed and Zhang presents a predictive maintenance model ad-dressing multiple imperfect maintenance actions and optimization procedures todetermine the optimum system maintenance threshold level that achieves the max-imum system availability. Chapter 8 by Lu and Wang presents a method to esti-mate the reliability and its confidence limits for the Weibull distribution, when thereare only few or no failure data available. The Monte Carlo simulation techniquewith only three failure samples was also discussed to obtain the estimates of a two-parameter Weibull distribution. Chapter 9 by Xie and Wang presents an extendedstress-strength interference analysis method to calculate the fatigue reliability un-der constant cyclic load with uncertainty in stress amplitude. For a specified cyclicload amplitude distribution, fatigue reliability can be calculated using the statisti-cal average of the probabilities. Applications of the methods are also discussed toshow the effect of load uncertainty on reliability analysis. Chapter 10 by Fukuda,Tokuno, and Yamada presents a method to evaluate the performance of the softwaresystems considering real-time properties including time-dependent debugging ac-tivities using Markov process. Chapter 11 by Xie, Wang, Hao and Zhang providesa general review of the load-component strength interference relationship and thenpresents a time-dependent strength function to estimate the failure probability ofseries pipeline systems under randomly multiple load actions.

Part III of the book contains five papers, focuses on Software Reliability andTesting.

Chapter 12 by Folleco, Khoshgoftaar and Van Hulse discusses the impact of noisebased on the incomplete measurement data on the evaluation of software quality im-putation techniques including Bayesian multiple imputation, nearest neighbor impu-tation, decision tree imputation, and regression imputation. Chapter 13 by Kimurapresents a linearized growth curve model and its parameter estimation using themethod of two-parameter numerical differentiation. Chapter 14 by Hwang and Phamdiscusses a generalized time-delay software reliability model addressing the time re-quired to identify and prioritize the detected faults before removing them optimalpolicies using the method of steps. Numerical examples based on software failuredata are presented to illustrate the use of the proposed model when it is applied inpractice. Chapter 15 by Lipton and Gokhale presents architecture-based software

Preface ix

reliability analysis and optimization methods for software systems addressing inter-face failures on application reliability using simulated annealing approach. Chap-ter 16 by Fujiwara, Inoue and Yamada discusses various software reliability growthmodels considering the time-dependent behavior of the fault-detection rate func-tions and the characteristics of module composition of the software system. Severalapplications also discussed to illustrate the methods.

Part IV of the book contains four papers, focuses on Quality Engineering inDesign.

Chapter 17 by Yamada and Takahashi presents a description of rubber productand defect phenomena and discusses several design of experiments based on qual-ity engineering approaches to identify the causes as well as enhance the product’squality and the process productivity. Chapter 18 by Son and Savage discusses anintegrated mean and tolerance economic design model consisting of the productioncost and the expected loss of quality cost over a planned horizon at the customer’sdiscount rate based on present worth of loss of quality. They also demonstrate themethods using an application in automotive industry.

Chapter 19 by Castagliola, Celano and Fichera discusses a logarithmic trans-formed EWMA chart that monitors a statistic that depends on the sample varianceand presents sensitivity analysis of the economic-statistical design to the implemen-tation of a S2 Shewhart chart. Chapter 20 by Fukushima and Yamada aims to preventproject failures by developing the risk management methods based on real-world ex-perience and software development practices. It also analyzes the effects of projectmanagement factors using the multiple linear regression technique.

Part V of the book contains five papers, on Applications in Engineering Design.Chapter 21 by Wanpracha, Pham, Hwang, Liang and Pham discusses the state-

of-the-art approaches such as support vector machine, natural language processing,classification regression tree etc. in data mining that may be applicable to analyzingcomplex categorizing text records. The chapter also discusses several research chal-lenges and directions in analyzing text records and mining. Chapter 22 by Siu brieflydiscusses the needs of visually impaired people in using public toilets and then iden-tifies several key areas that worth to consider in designing the facilities, using theconcept friendly, informative, safe, and hygienic. Chapter 23 by Miller and Guptadiscusses assurance cases for critical infrastructures with a concentration on reliabil-ity and safety for Supervisory, Control, and Data Acquisition systems and presentsa risk management structure based on a goal-based assurance approach to improvethe return on investment. Chapter 24 by Fukuda discusses various detecting driver’semotion perspectives and context-dependent approaches in terms of human errorsdue to the rapid and frequent changes in real-world environments. Chapter 25 byPham discusses some recent research and modeling in the area of aging and mortal-ity modeling in demography. The chapter also presents several common distributionfunctions and the force of mortality functions that used in the field.

All the chapters are written by 50 leading experts in the field in academia andindustry. I am deeply indebted and wish to thank all of them for their contributionsand cooperation. Thanks are also due to the Springer staff for their editorial work.I hope that the readers including engineers, teachers, scientists, postgraduates, re-

x Preface

searchers, and practitioners in the areas of both engineering and applied science,will find this book a state-of-the-references survey and a valuable resource for un-derstanding the latest developments in reliability and quality and its applications inengineering design.

Hoang PhamPiscataway, New Jersey

June 2007

Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxii

Part I System Reliability Computing

1 Central Limit Theorem for a Family of Reliability MeasuresPanlop Zeephongsekul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Fuzzy Sets Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.1 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2.2 Fuzzy Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2.3 Point Process with Random Fuzzy Marks

and Corresponding Gauge Measure . . . . . . . . . . . . . . . . . . . . . 91.2.4 Normal Fuzzy Random Variables . . . . . . . . . . . . . . . . . . . . . . . 11

1.3 A Central Limit Theorem for Gauge Measuresand Related Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.3.1 Central Limit Theorems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.3.2 Asymptotic Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4 Further Examples and an Application . . . . . . . . . . . . . . . . . . . . . . . . . . 231.4.1 An Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2 Modeling and Reliability Evaluationof Multi-state k-out-of-n SystemsZhigang Tian, Wei Li, Ming J. Zuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.1.1 Binary k-out-of-n Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.1.2 Multi-state Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.1.3 Overview of Multi-state k-out-of-n System Modeling

and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2 Multi-state k-out-of-n System Models . . . . . . . . . . . . . . . . . . . . . . . . . 34

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2.2.1 Multi-state k-out-of-n:G System Model by Huang et al. . . . . 342.2.2 Multi-state k-out-of-n System Model by Tian et al. . . . . . . . . 352.2.3 Multi-state Weighted k-out-of-n System Model . . . . . . . . . . . 37

2.3 Reliability Evaluation of Multi-state k-out-of-n Systems . . . . . . . . . . 392.3.1 Fundamental Elements of Recursive Algorithms . . . . . . . . . . 402.3.2 Reliability Evaluation of the Multi-state k-out-of-n Model

Defined by Huang et al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402.3.3 Reliability Evaluation of the Multi-state k-out-of-n Model

Defined by Tian et al. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442.3.4 Reliability Evaluation of Multi-state Weighted k-out-of-n

Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3 On Weighted Least Squares Estimationfor the Parameters of Weibull DistributionLifang Zhang, M. Xie, L.C. Tang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.2 Basic Concepts in Lifetime Data Analysis . . . . . . . . . . . . . . . . . . . . . . 593.3 Common Estimation Methods for Weibull Distribution . . . . . . . . . . . 61

3.3.1 Weibull Probability Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.3.2 Least Squares Estimation Method . . . . . . . . . . . . . . . . . . . . . . 633.3.3 Maximum Likelihood Estimation Method . . . . . . . . . . . . . . . . 643.3.4 Comparisons of the Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.4 Weighted Least Squares Estimation Methodsand Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653.4.1 Estimating Equation of WLSE . . . . . . . . . . . . . . . . . . . . . . . . . 653.4.2 Calculation of Weights and Assumptions . . . . . . . . . . . . . . . . 663.4.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.5 An Improved Method for Calculating Weights . . . . . . . . . . . . . . . . . . 693.5.1 Calculation for ‘Best’ Weights . . . . . . . . . . . . . . . . . . . . . . . . . 693.5.2 An Approximation for ‘Best’ Weights for Small

and Complete Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.5.3 Application Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.5.4 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753.5.5 Monte Carlo Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.6 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4 Periodic and Sequential Imperfect Preventive Maintenance Policiesfor Cumulative Damage ModelsToshio Nakagawa, Satoshi Mizutani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854.2 Periodic PM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

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4.3 Sequential PM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904.4 PM for a Finite Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5 Some Alternative Approaches to System Reliability ModelingJerzy K. Filus, Lidia Z. Filus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.1.1 New Kinds of Stochastic Dependences . . . . . . . . . . . . . . . . . . 1015.1.2 Joint Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . 1025.1.3 Determination of pdfs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.1.4 Application of Stochastic Dependences . . . . . . . . . . . . . . . . . . 103

5.2 A New Bivariate Probability Densities Construction . . . . . . . . . . . . . 1035.2.1 Modeling of Component Lifetime . . . . . . . . . . . . . . . . . . . . . . 1035.2.2 Choice of Subclass of Continuous Functions . . . . . . . . . . . . . 106

5.3 Multivariate Extensions of the Bivariate Models . . . . . . . . . . . . . . . . . 1085.4 A Comparison with Freund, Marshall and Olkin,

and some Other Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1115.4.1 The Freund Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1115.4.2 The Marshall and Olkin Models . . . . . . . . . . . . . . . . . . . . . . . . 1115.4.3 Classification of Stochastic Dependency Models . . . . . . . . . . 1125.4.4 Physical Impacts Outside the System . . . . . . . . . . . . . . . . . . . . 1125.4.5 “Third Type” Stochastic Dependence Models . . . . . . . . . . . . . 112

5.5 The Transformation Method for the pdfs Construction . . . . . . . . . . . . 1145.5.1 Direct Transformations of Random Vectors . . . . . . . . . . . . . . 1145.5.2 On the Role of the Pseudoaffine and Pseudopower

Transformations in Statistical Analysis and Sampling . . . . . . 1155.6 Extension of the Random Vector Models to Stochastic Processes . . . 116

5.6.1 Discrete Time Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.6.2 Stochastic Processes Memory . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.7 Application of k-Markovian Stochastic Processes . . . . . . . . . . . . . . . . 1185.7.1 Finite Dimensional Pseudoaffine Transformations . . . . . . . . . 1195.7.2 Markovian Pseudonormal Processes . . . . . . . . . . . . . . . . . . . . 1195.7.3 k-Markovianity for Pseudoaffine Transformations . . . . . . . . . 120

5.8 Maintenance Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1225.8.1 Reliability and Maintenance of Systems . . . . . . . . . . . . . . . . . 1225.8.2 Aging Systems Repaired at Each Failure . . . . . . . . . . . . . . . . . 1245.8.3 “Forgetting Factors” Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

5.9 Additional Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285.9.1 Extention of Class of Pseudoaffine Transformations . . . . . . . 1285.9.2 Extension of n-variate pdf Classes . . . . . . . . . . . . . . . . . . . . . . 1295.9.3 Extended Applications of Stochastic Dependences . . . . . . . . 1295.9.4 Multivariate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

5.10 Some Analytic Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305.10.1 Pseudolinear Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . 130

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5.10.2 Further Analytic Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . 1315.10.3 Simplification of Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . 1325.10.4 An Example of a Non-symmetric Pseudonormal Class

of pdfs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

6 The Optimal Burn-in: State of the Art and New Advancesfor Cost Function FormulationXin Liu, Thomas A. Mazzuchi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1376.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1376.2 State-of-art on Optimal Burn-in Research . . . . . . . . . . . . . . . . . . . . . . 138

6.2.1 Failure Time Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1386.2.2 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1456.2.3 Model the Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1466.2.4 Model Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

6.3 Development of “After Burn-in Failure Treatment” (AFT) CostModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1686.3.1 Why the New AFT Model? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1686.3.2 New Cost Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . 1696.3.3 Properties and Optimization of the New Model . . . . . . . . . . . 1716.3.4 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1746.3.5 Application of AFT Model to Updating Strategy Policy

Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1786.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

Part II Reliability Engineering in Design

7 Optimum Threshold Level of Degrading Systems Basedon Sensor ObservationElsayed A. Elsayed, Hao Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1857.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1867.2 Gamma Process Degradation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 1877.3 Imperfect Maintenance Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

7.3.1 Maintenance Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1887.3.2 Imperfect Maintenance Model . . . . . . . . . . . . . . . . . . . . . . . . . 1897.3.3 Modeling Maintenance Time . . . . . . . . . . . . . . . . . . . . . . . . . . 190

7.4 Sensor Errors and Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1907.5 Uptime Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1937.6 Threshold Level: System Availability Maximization . . . . . . . . . . . . . 194

7.6.1 Formulation of the Availability Maximization Problem . . . . . 1947.6.2 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

7.7 Threshold Level: Maintenance Cost Minimization . . . . . . . . . . . . . . . 1967.7.1 Formulation of the Cost Minimization Problem . . . . . . . . . . . 1967.7.2 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

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7.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

8 Weibull Data Analysis with Few or no FailuresMing-Wei Lu, Cheng Julius Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2018.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2018.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

8.2.1 Nelson’s Method [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2028.2.2 Extended Test Method [3] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

8.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2038.4 Simulation Study with Only Three Failures . . . . . . . . . . . . . . . . . . . . . 206

8.4.1 Weibull Parameter Estimation Method . . . . . . . . . . . . . . . . . . . 2088.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

9 A Load-weighted Statistical Average Model of Fatigue ReliabilityLiyang Xie, Zheng Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2119.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2119.2 Statistical Average Interpretation of SSI Model . . . . . . . . . . . . . . . . . . 2139.3 A Statistical Load-weighted Average Model of Fatigue Reliability . . 2159.4 Fatigue Life Distribution Under Constant Amplitude Cyclic Stress

and Fatigue Reliability Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2189.5 Examples of Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2209.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

10 Markovian Performance Evaluationfor Software System Availabilitywith Processing Time LimitMasamitsu Fukuda, Koichi Tokuno, Shigeru Yamada . . . . . . . . . . . . . . . . . . 22510.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22510.2 Markovian Software Availability Model . . . . . . . . . . . . . . . . . . . . . . . . 226

10.2.1 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22610.2.2 Software Availability Measures . . . . . . . . . . . . . . . . . . . . . . . . 227

10.3 Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22910.4 Derivation of Software Performance Measures . . . . . . . . . . . . . . . . . . 23210.5 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23410.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238

11 Failure Probability Estimation of Long PipelineLiyang Xie, Zheng Wang, Guangbo Hao, Mingchuan Zhang . . . . . . . . . . . . 23911.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23911.2 Segment Partition and System Strength Distribution . . . . . . . . . . . . . . 24011.3 Pipeline Failure Probability Estimation

and Failure Dependence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

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11.4 Pipeline Failure Probability Estimation . . . . . . . . . . . . . . . . . . . . . . . . 24511.5 Upper Limit of Large-scale Series System Failure Probability . . . . . 24611.6 Pipeline Reliability Under Randomly Repeated Load . . . . . . . . . . . . . 24811.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

Part III Software Reliability and Testing

12 Software Fault Imputation in Noisyand Incomplete Measurement DataAndres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse . . . . . . . . . . . . . . 25512.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25512.2 Empirical Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256

12.2.1 CCCS Dataset Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25612.2.2 Inherent and Simulated Noise . . . . . . . . . . . . . . . . . . . . . . . . . . 25712.2.3 Relatively Clean CCCS Dataset . . . . . . . . . . . . . . . . . . . . . . . . 258

12.3 Imputation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25812.3.1 Regression Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25812.3.2 REPTree Decision Tree Imputation . . . . . . . . . . . . . . . . . . . . . 25912.3.3 Nearest Neighbor Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . 25912.3.4 Mean Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25912.3.5 Bayesian Multiple Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . 259

12.4 Missing Data Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26112.5 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262

12.5.1 Injection of Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26212.5.2 BMI Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26312.5.3 Imputation Performance Metric . . . . . . . . . . . . . . . . . . . . . . . . 263

12.6 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26412.6.1 Imputation Average Absolute Errors (aae) . . . . . . . . . . . . . . . 26412.6.2 Three-way ANOVA: Randomized Complete Block Design 26612.6.3 Multiple Pairwise Comparisons . . . . . . . . . . . . . . . . . . . . . . . . 26712.6.4 Noise Impact on Remaining (Non-noisy) Instances . . . . . . . . 269

12.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

13 A Linearized Growth Curve Modelfor Software Reliability Data AnalysisMitsuhiro Kimura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27513.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27513.2 Generalization of Growth Curve Models . . . . . . . . . . . . . . . . . . . . . . . 276

13.2.1 Two-parameter Numerical Differentiation Method . . . . . . . . . 27713.2.2 Linearized Growth Curve Model . . . . . . . . . . . . . . . . . . . . . . . 278

13.3 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28013.4 Examples of Data Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . 281

13.4.1 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

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13.4.2 Curve-fitting Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28713.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290

14 Software Reliability Model Considering Time-delay Fault RemovalSeheon Hwang, Hoang Pham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29114.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29114.2 Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293

14.2.1 Time-delay Fault Removal Model . . . . . . . . . . . . . . . . . . . . . . 29414.3 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298

14.3.1 General Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29814.3.2 Analysis of Performance of Models for Fitting Failure Data 30114.3.3 Analysis of Performance for Predicting Future Failure . . . . . 304

14.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

15 Heuristic Component Placementfor Maximizing Software ReliabilityMichael W. Lipton, Swapna S. Gokhale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30915.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30915.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

15.2.1 Discrete Time Markov Chains (DTMCs) . . . . . . . . . . . . . . . . . 31015.2.2 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

15.3 Analysis and Optimization Methodologies . . . . . . . . . . . . . . . . . . . . . . 31315.3.1 Reliability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31315.3.2 Reliability Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

15.4 Illustrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31715.4.1 Description of Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31715.4.2 Analysis Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32015.4.3 Optimization Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322

15.5 Related Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32815.6 Conclusions and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

16 Software Reliability Growth ModelsBased on Component CharacteristicsTakaji Fujiwara, Shinji Inoue, Shigeru Yamada . . . . . . . . . . . . . . . . . . . . . . 33116.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33116.2 Module Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33216.3 Software Reliability Growth Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 334

16.3.1 Basic SRGM Based on Component Characteristics . . . . . . . . 33416.3.2 Generalization of BCC-SRGM . . . . . . . . . . . . . . . . . . . . . . . . . 33616.3.3 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338

16.4 Numerical Examples for Software Reliability Analysis . . . . . . . . . . . 33816.4.1 Estimation of Model Parameters . . . . . . . . . . . . . . . . . . . . . . . . 338

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16.4.2 Goodness-of-fit Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . 33916.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342

Part IV Quality Engineering in Design

17 Statistical Analysis of Appearance Qualityfor Automotive Rubber ProductsShigeru Yamada, Kenji Takahashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34517.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34517.2 Description of Product and Defect Phenomenon . . . . . . . . . . . . . . . . . 34617.3 Identification of Bloom Phenomenon . . . . . . . . . . . . . . . . . . . . . . . . . . 34717.4 Orthogonal Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34717.5 Analysis of Swell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349

17.5.1 Cumulative Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34917.5.2 Process Average and 95% Confidence Limits . . . . . . . . . . . . . 350

17.6 Analysis of CS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35117.6.1 Logit Transformation and Data Analysis . . . . . . . . . . . . . . . . . 35117.6.2 Process Average and 95% Confidence Limits . . . . . . . . . . . . . 35317.6.3 Process Average and 95% Confidence Limits

Under Simultaneous Optimal Conditions of Swell and CS . . 35317.7 Discriminant Analysis for Swell Measures . . . . . . . . . . . . . . . . . . . . . . 35417.8 Multiple Regression Analysis for CS Measures . . . . . . . . . . . . . . . . . . 35517.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360

18 Present Worth Design of Engineering Systemswith Degrading ComponentsYoung Kap Son, Gordon J. Savage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36118.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36118.2 Modeling of Time-variant Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363

18.2.1 Component Degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36318.2.2 Time-variant Limit-state Functions . . . . . . . . . . . . . . . . . . . . . 364

18.3 Cumulative Distribution Function Modeling . . . . . . . . . . . . . . . . . . . . 36518.4 Formulation of Economic Design Problems . . . . . . . . . . . . . . . . . . . . . 366

18.4.1 Present Worth Evaluation of Design . . . . . . . . . . . . . . . . . . . . . 36618.4.2 Formulation of Economic Design Problems . . . . . . . . . . . . . . 367

18.5 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36818.5.1 Initial Design and CDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37018.5.2 New Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372

18.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374

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19 Economic-statistical Design of a Logarithmic Transformed S2

EWMA ChartP. Castagliola, G. Celano, S. Fichera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37519.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37519.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37619.3 The Logarithmic Transformed S2 EWMA Chart . . . . . . . . . . . . . . . . . 37919.4 The Economic Design of the S2 EWMA . . . . . . . . . . . . . . . . . . . . . . . 384

19.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38419.4.2 Formulation of the Mathematical Model . . . . . . . . . . . . . . . . . 38519.4.3 Computation of the ARLs for the S2 EWMA . . . . . . . . . . . . . 38919.4.4 Formulation of the Constrained Optimization Problem . . . . . 391

19.5 The Economic Statistical Design of the S2 EWMA:a Numerical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39219.5.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39219.5.2 Evaluation of the Cost Savings vs. the S2 Shewhart . . . . . . . . 39319.5.3 A Sensitivity Analysis on the Design Parameters

of the S2 EWMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39919.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

20 Risk Management Techniques for Quality Software Developmentand Its Quantitative EvaluationToshihiko Fukushima, Shigeru Yamada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40720.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40720.2 Project Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

20.2.1 Project Risks in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40920.2.2 Risk Management Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . 41020.2.3 Risk Identification and Quantification . . . . . . . . . . . . . . . . . . . 41220.2.4 Risk Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41220.2.5 Risk Monitoring and Control . . . . . . . . . . . . . . . . . . . . . . . . . . 413

20.3 Project Effect Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41520.3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41520.3.2 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41620.3.3 Multiple Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41620.3.4 Effectiveness Evaluation of Management Factor . . . . . . . . . . 419

20.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420

Part V Application in Engineering Design

21 Recent Advances in Data Mining for Categorizing Text RecordsW. Chaovalitwongse, H. Pham, S. Hwang, Z. Liang, C.H. Pham . . . . . . . . 42321.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42321.2 Text Mining in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425

21.2.1 Product Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . 425

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21.2.2 Customer Service and Product Diagnosis . . . . . . . . . . . . . . . . 42621.2.3 Improved Healthcare Quality

with Electronic Medical Records . . . . . . . . . . . . . . . . . . . . . . . 42721.3 Background in Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427

21.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42821.3.2 Information and Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . 42821.3.3 Data Mining Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429

21.4 State-of-the-art in Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43021.4.1 Basic Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43121.4.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43121.4.3 Nearest Neighbor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43321.4.4 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43321.4.5 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43521.4.6 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43521.4.7 Rule Induction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43621.4.8 Log-linear Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43721.4.9 Multidimensional Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437

21.5 Research Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438

22 Quality in Design: User-oriented Design of Public Toiletsfor Visually Impaired PeopleKin Wai Michael Siu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44122.1 Difficulties and Consequences

for VIP in Accessing Public Environments . . . . . . . . . . . . . . . . . . . . . 44122.2 Deficiencies in Public Toilets for VIP . . . . . . . . . . . . . . . . . . . . . . . . . . 44322.3 Studies on Accessibility of Public Toilets for VIP . . . . . . . . . . . . . . . . 44422.4 Key Areas of Design Quality for Consideration . . . . . . . . . . . . . . . . . 44522.5 FISH: Better Designs of Public Toilets for VIP . . . . . . . . . . . . . . . . . . 449

22.5.1 Friendly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44922.5.2 Informative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45422.5.3 Safe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45722.5.4 Hygienic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460

22.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463

23 Assurance Cases for Reliability:Reducing Risks to Strengthen ROI for SCADA SystemsAnn Miller, Rashi Gupta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46523.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46523.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46623.3 SCADA Security and RAM Issues – An Overview . . . . . . . . . . . . . . . 46723.4 Risk Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468

23.4.1 Security and RAM Risks Associated with SCADA Systems 46823.4.2 Business Risks Associated with SCADA Systems . . . . . . . . . 472

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23.5 Mapping Technical Risks into Business Risks . . . . . . . . . . . . . . . . . . . 47323.5.1 Mapping Security Risks into Business Risks . . . . . . . . . . . . . 47323.5.2 Mapping RAM Risks into Business Risks . . . . . . . . . . . . . . . . 473

23.6 Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48123.6.1 Risk Assessment Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48123.6.2 Risk Severity Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484

23.7 Goal-based Assurance Case Approach . . . . . . . . . . . . . . . . . . . . . . . . . 48523.7.1 Security-enhanced ROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48523.7.2 RAM-enhanced ROI by Using RAM Cases . . . . . . . . . . . . . . 485

23.8 Summary and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

24 Detecting Driver’s Emotion:A Step Toward Emotion-based Reliability EngineeringShuichi Fukuda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49124.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49124.2 Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

24.2.1 Primary or Basic Emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49324.2.2 Detecting Driver’s Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

24.3 Observation of Actual Driving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49324.4 Experiments Using Driving Simulator . . . . . . . . . . . . . . . . . . . . . . . . . 49424.5 Facial Emotional Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

24.5.1 Developing a Simpler Technique to Detect Facial Emotion 49524.5.2 Detection of Emotion from Real Face . . . . . . . . . . . . . . . . . . . 498

24.6 Detection of Fatigue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50124.7 Detection of Dangerous Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50324.8 Detection of Emotion from Voice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

24.8.1 Detection of Anger and Fatigue from Voice . . . . . . . . . . . . . . 505References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506

25 Mortality Modeling PerspectivesHoang Pham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50925.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50925.2 Literature Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51025.3 Mortality Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514

About the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519

Contributors

P. Castagliola Institut Universitaire de Technologie de Nantes, FranceGiovanni Celano University of Catania, ItalyW. Chaovalitwongse Rutgers University, USAElsayed A. Elsayed Rutgers University, USAS. Fichera University of Catania, ItalyJerzy K. Filus Oakton Community College, USALidia Z. Filus Northeastern Illinois University, USAAndres Folleco Florida Atlantic University, USATakaji Fujiwara Fujitsu Peripherals Limited, JapanMasamitsu Fukuda Tottori University, JapanShuichi Fukuda Tokyo Metropolitan Institute of Technology, JapanToshihiko Fukushima Nissin Systems Co., Ltd., JapanSwapna S. Gokhale University of Connecticut, USARashi Gupta University of Missouri, USAGuangbo Hao Northeastern University, ChinaJason Van Hulse Florida Atlantic University, USASeheon Hwang Rutgers University, USAShinji Inoue Tottori University, JapanTaghi M. Khoshgoftaar Florida Atlantic University, USAMitsuhiro Kimura Hosei University, JapanWei Li University of Alberta, CanadaZ. Liang Rutgers University, USAMichael W. Lipton IBM Corporation, USAXin Liu Delft University of Technology, The NetherlandsMing-Wei Lu Daimler Chrysler Corporation, USAThomas A. Mazzuchi The George Washington University, USAAnn Miller University of Missouri, USASatoshi Mizutani Aichi Institute of Technology, JapanToshio Nakagawa Aichi Institute of Technology, JapanChristopher Hoang Pham Cisco Systems Inc. & San Jose State University

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xxiv Contributors

Hoang Pham Rutgers University, USAGordon J. Savage University of Waterloo, CanadaKin Wai Michael Siu The Hong Kong Polytechnic University, Hong KongYoung Kap Son University of Waterloo, CanadaKenji Takahashi Tottori University, JapanL.C. Tang National University of Singapore, SingaporeZhigang Tian University of Alberta, CanadaKoichi Tokuno Tottori University, JapanCheng Julius Wang Daimler Chrysler Corporation, USAZheng Wang Northeastern University, ChinaArt Wanpracha Rutgers University, USALiyang Xie Northeastern University, ChinaM. Xie National University of Singapore, SingaporeShigeru Yamada Tottori University, JapanLifang Zhang National University of Singapore, SingaporeMingchuan Zhang Northeastern University, ChinaHao Zhang Rutgers University, USAPanlop Zeephongsekul RMIT University, AustraliaMing J. Zuo University of Alberta, Canada