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Design and Manufacturing Practices for Performability Engineering

Edited by Sanjay K Chaturvedi, Heeralal Gargama and Rajiv N Rai
Series: Performability Engineering Series
Copyright: 2025   |   Expected Pub Date:2025/09/30
ISBN: 9781394345700  |  Hardcover  |  
444 pages

One Line Description
"As technology continues to reshape the world, this book stands as a testament to the importance of maintaining the highest standards of performability engineering in the pursuit of progress. I expect that this book will inspire the next generation of innovators and problem solvers to tackle the challenges and opportunities of today and tomorrow, ensuring a future where technology serves humanity with utmost dependability and safety”.
Professor Way Kuo in the Foreword to Design and Manufacturing Practices for Performability Engineering

Audience
Researchers and educators of reliability engineering, electrical, computer science, electronics, and communication engineering with their associated allied areas. Industry analysts and design engineers of engineering systems will also find this book valuable.

Description
There are several aspects involved when evaluating a system’s performance, such as reliability, cost, quality, safety, maintainability, risks, and performance-related characteristics. Performability engineering provides a unified framework for integrating these aspects in a quantified manner, enabling informed decisions
about a system. However, this field faces the daunting task of unifying diversified disciplines and theories that address issues such as quality, reliability, availability, maintainability, and safety (QRAMS), as well as engineering characteristics, statistical data analysis, multi-criteria decision-making, and applications of deep and machine learning. This book documents the latest ideas presented by world leaders in the QRAMS domain. Through diverse chapters, this volume represents the vitality of QRAMS in performability engineering. Design and Manufacturing Practices for Performability Engineering serves as a useful resource for practicing engineers and researchers pursuing this challenging and relevant area for sustainable development.
Readers will find the book:
• Describes network reliability measures and algorithms to evaluate them in an easily understandable manner;
• Introduces the notion of global cuts and their applications to evaluate reliability measures and flow network reliability, and provides a general framework for reliability evaluation;
• Serves as a valuable reference and primer in the area of network reliability;
• Offers up-to-date research in the field of network reliability.

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Author / Editor Details
Sanjay K. Chaturvedi, PhD is a professor and former head of the Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology, Kharagpur (WB), India with 33 years of teaching and research experience. He’s executed several research and consultancy projects in the QRAMS domain sponsored by private and government organizations. He has published multiple journals articles and three books. His research interests include reliability modeling, analysis, and data, repairable systems, and maintenance.

Heeralal Gargama, PhD is an assistant professor at the Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology, Kharagpur (WB), India since 2021. He has worked in the private industry performing Reliability Availability Maintainability and Safety (RAMS) studies and implementing various projects in the railway, defense, commercial aerospace equipment, and automotive industries. His research focuses on railway RAMS, functional safety, physics of failure, and system modeling and analysis.

Rajiv N. Rai, PhD is an associate professor at the Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology, Kharagpur (WB), India with over 29 years of experience in the field of reliability, quality, and maintenance engineering. He has 23 years of industrial experience with the Indian Air Force, working at all levels of maintenance, repair, and overhaul of aircraft, aeroengines, and their components. His research interests include repairable systems reliability analysis, quality engineering and management, and reliability and maintenance engineering.

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Table of Contents
Foreword
Preface
Acknowledgment
1. Mathematical and Physical Reality of Reliability
Jezdimir Knezevic
1.1 Introduction
1.2 Experiencing Physical Reality of Reliability
1.2.1 Joining the Rallying Community
1.2.2 Experiencing First Rallying Reality of Reliability
1.2.3 Understanding the First Rallying Reality of Reliability
1.2.4 Summary of Experienced Physical Reliability Reality
1.3 Mathematical Reality of Reliability
1.3.1 Mathematical Reality of Reliability of a Component
1.3.2 Mathematical Reality of Reliability of a System
1.3.3 Physical Meanings of Mathematical Reality of Reliability
1.3.3.1 Mathematical Reality: Probability of Design and Production Error of Components and Systems is Equal to Zero
1.3.3.2 Mathematical Reality: Probability of Transportation, Storage and Installation
Induced Failures is Equal to Zero
1.3.3.3 Mathematical Reality: Probability of Component Dependencies Within a System in Equal to Zero
1.3.3.4 Mathematical Reality: Probability of Maintenance Induced Failures (Inspections, Repair, Diagnostics, Cleaning, Etc.) is Equal to Zero
1.3.3.5 Mathematical Reality: Probability of Discontinuous Operation of the System
and Components is Equal to Zero
1.3.3.6 Mathematical Reality: Probability of Variable Operational Scenario (Load, Stress, Temperature, Pressure, Etc.) Impacting Reliability is Equal to Zero
1.3.3.7 Mathematical Reality: Probability of a Location in Space (GPS or Stellar Co-Ordinates) Impacting Reliability is Equal to Zero
1.3.3.8 Mathematical Reality: Probability of a Human Actions Impacting Reliability is Equal to Zero
1.3.3.9 Mathematical Reality: Probability of Calendar Time (Seasons Do Not Exist) Impacting Reliability is Equal to Zero
1.3.4 Concluding Remarks Regarding Mathematical Reality of Reliability Function
1.4 Studying Physical Reality of Reliability
1.4.1 Mathematical Reality: Probability of Design and Production Error of Components and Systems is Equal to Zero
1.4.2 Observed Physical Reality of Reliability in 2020 – MIRCE Akademy Functionability Archive
1.4.3 Observed Physical Reality of Reliability in 2021 – MIRCE Akademy Functionability Archive
1.4.4 Observed Physical Reality of Reliability in 2022 – MIRCE Akademy Functionability Archive
1.5 Closing Remarks Regarding Observed Physical Reality of Reliability
1.5.1 Comparison Between Mathematical and Physical Reality of Reliability
1.6 Closing Questions
1.7 Personal Message from the Author
References
Appendix 1.1
2. Models and Solutions for Practical Reliability and Availability Assessment
K. Trivedi and A. Bobbio
2.1 Introduction
2.2 Non-State-Space Methods
2.3 State-Space-Based Methods
2.4 Multi-Level Models
2.5 Conclusions
References
3. Reliability Prediction of Artificial Hip Joints
E. A. Elsayed and Danlei Zhang
3.1 Introduction
3.2 Archard Law Wear Modeling
3.2.1 Wear Factor Estimation
3.2.2 Sliding Distance Estimation
3.2.3 Contact Pressure Estimation
3.2.4 Volumetric Wear Estimation
3.2.5 Archard Law-Based Wear Model Validation
3.3 Physics-Based Stochastic Wear Degradation Modeling
3.3.1 Validation of the Gamma Process for the Physics-Based Stochastic Wear Degradation Modeling
3.3.2 Parameter Estimation for the Wear Degradation Model
3.3.3 Time to Failure Distribution and Reliability Prediction
3.3.4 Physics-Based Wear Degradation Model Validation
3.4 Effect of Hip Implant Materials, Geometry and Patient’s Characteristics on the Wear Volume
3.4.1 Effect of Implant Materials
3.4.2 Effect of Hip Implant Geometry
3.4.3 Effect of Patient Activity Level
3.4.4 Effect of Patient Weight Changes
3.4.5 Conclusions
References
4. Principles and Philosophy for an Integrated and Distributed Approach for Reliability and Extensions to Other Qualities
Kailash [Kal] Kapur, P.E.
4.1 What is Quality?
4.1.1 Principle Centered Quality
4.2 Reliability
4.3 Other Qualities
4.3.1 Maintainability
4.3.2 Safety
4.3.3 Security
4.3.4 Robustness
4.3.5 Resilience
4.3.6 Integration of Time Oriented Qualities
4.4 Advances Beyond Binary States (Success/Failure)
4.4.1 Reliability Measures Based on Cumulative Customer Experience with the System
4.4.2 Some Comments on Role of Probability and Statistics
4.5 From Feedback to Prognostics to Feedforward
4.6 Prognostics and Feedforward Control
References
5. An Analytic Toolbox for Optimizing Condition Based Maintenance (CBM) Decisions
Andrew K. S. Jardine
5.1 Condition Monitoring: Then and Now
5.2 Condition Monitoring: Analogy with Heart Attack
5.3 Condition Monitoring ‘‘Classical” Approach Vs Proportional Hazards Model (PHM)
5.3.1 Which Measurements
5.3.2 Optimal Limits
5.3.3 Effect of Age
5.3.4 Predictions
5.3.5 Consequence of Failure
5.4 Another Approach to Overcome these Limitations
5.5 Early Work with the Proportional-Hazards Model (PHM)
5.6 Estimated Hazard Rate at Failure
5.7 EXAKT Optimal Decision – A New “Control Chart”
5.8 Optimizing CBM Decisions: EXAKT
5.8.1 Hazard
5.8.2 Transition Probability Matrix
5.8.3 Economics
5.9 Some Case Studies
5.10 University/Industry Collaboration
5.11 Acknowledgement to Companies Who Funded the Research Team Who Developed the CBM Optimization Software
References
6. Degradation Modeling with Imperfect Maintenance
Olivier Gaudoin
6.1 Introduction
6.2 Statistical Inference for a Wiener-Based Degradation Model with Imperfect Maintenance Actions Under Different Observation Schemes
6.2.1 Notations and Assumptions
6.2.2 The Model
6.2.3 Statistical Inference
6.2.3.1 First Observation Scheme
6.2.3.2 Second Observation Scheme
6.2.3.3 Third Observation Scheme
6.2.3.4 Fourth Observation Scheme
6.2.4 Remarks
6.3 Modeling Multivariate Degradation Processes with Time-Variant Covariates and Imperfect Maintenance Effects
6.3.1 Features of the Model
6.3.2 Piecewise Constant Covariates
6.3.3 The Multivariate Degradation Process
6.3.4 Imperfect Maintenance
6.4 Conclusion
References
7. Asset Maintenance in Railway: Powered by New Technology and Driven by Sustainability
Uday Kumar
7.1 Introduction and Background
7.2 RAMS & PHM
7.3 New Technology for Railway Maintenance
7.4 Automation, Robotics and AI in Railway
7.4.1 Automation of Data Acquisition
7.4.2 Automation of Information Extraction
7.4.3 Automation of Maintenance Task Planning, Scheduling and Execution
7.4.4 Digital Twins
7.4.5 Maintenance in Metaverse
7.5 Some Examples of Industrial Projects
7.5.1 AI Factory for Railway
7.5.2 Differential Eddy Current Sensor System for Detection of Missing Rail Fasteners
7.5.3 Assessment of Track Geometry Condition
7.5.4 PHM of Railway Catenary
7.5.5 The Digital Railway Switches
7.5.6 Fr8 RAIL – Predictive Maintenance for Rolling Stocks
7.6 Maintenance and Sustainability
7.6.1 Life Extension of Ageing Railway Asset
7.6.2 Energy Efficiency
7.6.3 Risk Mitigation
7.7 Challenges Associated with Application of Emerging Technologies
7.8 Concluding Remarks
References
8. ISO 14001 History and Applications
Roderick A. Munro
8.1 Need for EMS – Help to Prevent Environmental Disasters
8.2 India’s Governmental Alignments with the ISO
8.3 Sustainability Goal
8.4 History of ISO & Environmental Standards
8.5 ISO 14000
8.6 ISO Oversight Process
8.7 ISO 14001:2015 – Structure
Bibliography
8.8 ISO 14001:2015 – Requirements – Shall’s
8.9 ISO 14001:2015 – Risk & Opportunities
8.10 ISO 14001:2015 – Aspects & Impacts
8.11 ISO 14001:2015 – Life Cycle
8.12 Linkage to Other ISO Management System Standards
8.13 Potential Environmental Updates Based on Thoughts for ISO 9001:2025
References
9. Process Failure Mode and Effects Analysis (PFMEA) with Fuzzy ANP-MARCOS-Based Approach for Manufacturing Process Quality Assessment
Anwesa Kar and Rajiv Nandan Rai
9.1 Introduction
9.2 Literature Review
9.2.1 Quality Metrics for the Manufacturing Process
9.2.2 Literature Related to Fuzzy ANP
9.2.3 Literature Related to Fuzzy MARCOS
9.2.4 Research Deliverables
9.3 Methodology
9.3.1 Phase 1: PFMEA Method
9.3.2 Phase 2: Fuzzy ANP Method
9.3.3 Phase 3: Fuzzy MARCOS Technique
9.4 Case Study
9.4.1 Identification of KQC and its Influencing Factors through PFMEA
9.4.2 Estimation of Factors’ Weights through Fuzzy ANP
9.4.3 Ranking of Cases through Fuzzy MARCOS
9.5 Results and Discussions
9.5.1 Sensitivity Analysis
9.5.2 Comparative Analysis
9.6 Summary & Conclusion
References
10. Advanced Neural Networks for Estimation of All-Terminal Network Reliability
Alex Davila-Frias and Om Prakash Yadav
10.1 CNN-Based Network Reliability Estimation
10.1.1 Overview
10.1.2 The CNN Proposal
10.1.3 CNN Structure
10.1.4 The Case Study
10.1.4.1 Dataset
10.1.4.2 The Hyperparameters
10.1.4.3 Chosen CNN
10.1.4.4 The Cross-Validation
10.1.4.5 Computation Time
10.1.5 Discussion
10.2 All-Terminal Estimation of Network Reliability Considering Degradation with Bayesian Methods, Monte Carlo, and Deep Neural Networks
10.2.1 Overview
10.2.2 The Methodology
10.2.2.1 Reliability Evaluation of Links and Nodes with Degradation Models
10.2.2.2 Estimating All-Terminal Network Reliability with Monte Carlo Approach
10.2.2.3 Model with Deep Neural Network
10.2.2.4 Updating of Parameters
10.2.2.5 The Framework
10.2.3 The Case Study
10.2.3.1 Links Degradation Modeling
10.2.3.2 Nodes Degradation Modeling
10.2.3.3 MC-DNN Model
10.2.3.4 Estimation of Reliability for the Network
10.2.3.5 Updating the Reliability Estimation
10.2.4 Conclusion
References
11. Power Converter Fault Classification Using Multi-Sensor Fusion and 1D-CNN Approach
Sanjay K. Chaturvedi, Akanksha Chaturvedi and Monalisa Sarma
11.1 Introduction
11.2 Related Work
11.3 Proposed Fault Diagnosis Approach Based on 1D-CNN
11.3.1 Preliminary
11.3.1.1 SEPIC Configuration
11.3.1.2 Hard Faults Visualization in Output Voltage
11.3.1.3 Multi-Sensor Data for Fault Classification
11.3.1.4 Convolutional Neural Network
11.3.2 Steps of Proposed Fault Diagnosis Approach Using CNN
11.4 Results
11.4.1 Fault Classification Under Constant Input Voltage
11.4.2 Fault Classification Under Variable Operating Condition
11.4.3 Fault Classification Under Presence of Noise
11.4.4 Comparison with Another DL Model
11.5 Conclusion
References
12. Assessment of System Reliability Using Quantum Computers: A Primer
Indranil Hazra, Gabriel San Martín Silva and Enrique López Droguett
12.1 Introduction
12.2 Essentials of Quantum Computing
12.2.1 Classical vs. Quantum Computing
12.2.2 Single Qubit and Multi-Qubit Systems
12.2.3 Quantum Gates and Operations
12.3 Quantum Circuits for Fault Trees
12.3.1 Basic Logic Gates for FTA
12.3.2 Quantum Gates for Fault Tree Conversion
12.3.3 Constructing QFT Circuits
12.4 Case Study: Engine Cooling and Control System
12.4.1 System Description and Fault Tree
12.4.2 Quantum Circuit for the ECCS Fault Tree
12.4.3 Results and Discussion
12.5 Summary and Conclusion
References
13. Safety Integrity Allocation for Railway Systems
Heeralal Gargama and Ajeet Kumar
13.1 Introduction
13.2 Risk Assessment and Hazard Control Process
13.3 Apportionment of Safety Integrity Requirements
13.3.1 Event Tree Analysis (ETA)
13.3.2 Fault Tree Analysis (FTA)
13.3.3 Risk Graph Method
13.3.4 Risk Matrix
13.4 Conclusion
References
14. A New Approach to Economic Development: Implications for India’s Emergence as a Global Manufacturing Hub
Hwy-Chang Moon, Wenyan Yin and Dilong Huang
14.1 Introduction
14.2 South Korea’s Remarkable Economic Development
14.3 A New Approach to South Korea’s Economic Growth
14.4 Implications for India’s Development of Manufacturing Sector
14.4.1 “Make in India” Initiative
14.4.2 Linking India to MNCs’ Global Value Chains (GVCs)
14.4.3 Four Strategic Factors for Improving Business Environment for Attracting FDI in the Manufacturing Sector
14.4.3.1 Cheap and Productive Labor
14.4.3.2 Better than Competitors
14.4.3.3 Competitive Industry Cluster
14.4.3.4 Highly Dedicated Labor Force
14.5 Implications for India and Conclusion
References
15. Challenges in Applying Reliability Engineering in Product Development
Dr. Dhananjay Kumar
15.1 Introduction
15.2 Product Lifecycle and Reliability Engineering
15.3 Main Tasks of a Reliability Professional
15.4 RAM&T Plan
15.5 RAM&T Requirements
15.6 RAM&T Prediction and Uncertainties
15.7 Components Deratings
15.8 Analytical Evidence for RAM&T
15.9 Physical Evidence
15.10 Manufacturing Reliability
15.11 In-Life Performance Monitoring
15.12 End of Life Declaration
15.13 Conclusion
References
16. Challenges and Research Opportunities for Reliability Engineering with Evolving Industry
Pravin Kadekodi
16.1 Introduction
16.2 Technology Trends
16.2.1 Electrification
16.2.2 Energy Transition
16.2.3 Digitalization
16.2.4 Advance Manufacturing
16.2.5 Summary of this Section
16.3 Organization’s Expectation from Reliability Engineering Function
16.3.1 Cost
16.3.2 Speed
16.3.3 Quality
16.3.4 Summary of this Section
16.4 Combine View of External and Internal Challenges
16.5 Latest Advancements in Reliability Engineering Methods and the Opportunities for Meeting the Challenges
16.5.1 Prognostics and Health Management
16.5.2 Software Reliability
16.5.3 Connected and Network Systems Reliability
16.6 Summary
References
Index

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