LEADER 01990oas 2200709 a 450 001 9910145191503321 005 20250911213016.0 011 $a1873-5789 035 $a(DE-599)ZDB1466385-5 035 $a(DE-599)3187645-6 035 $a(OCoLC)39284327 035 $a(CONSER) 2007233874 035 $a(CKB)954925541188 035 $a(EXLCZ)99954925541188 100 $a19980615b19842000 sy 101 0 $aeng 135 $aurmnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe journal of logic programming 210 $aNew York $cElsevier Science 300 $aRefereed/Peer-reviewed 311 08$a0743-1066 606 $aLogic programming$vPeriodicals 606 $aComputer programming$vPeriodicals 606 $aProgrammation logique$xPe?riodiques 606 $aProgrammation (Informatique)$xPe?riodiques 606 $aProgrammation logique$vPe?riodiques 606 $aProgrammation (Informatique)$vPe?riodiques 606 $aComputer programming$2fast$3(OCoLC)fst00872390 606 $aLogic programming$2fast$3(OCoLC)fst01002056 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 615 0$aLogic programming 615 0$aComputer programming 615 6$aProgrammation logique$xPe?riodiques. 615 6$aProgrammation (Informatique)$xPe?riodiques. 615 6$aProgrammation logique 615 6$aProgrammation (Informatique) 615 7$aComputer programming. 615 7$aLogic programming. 801 0$bOH1 801 1$bOH1 801 2$bOCL 801 2$bOCLCQ 801 2$bGUA 801 2$bDLC 801 2$bU9S 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCF 801 2$bOCLCO 801 2$bOCLCQ 801 2$bU3W 801 2$bOCLCO 801 2$bOCLCQ 906 $aJOURNAL 912 $a9910145191503321 996 $aJournal of logic programming$9104538 997 $aUNINA LEADER 13137nam 22006253 450 001 9911021978703321 005 20251217003309.0 010 $a1-394-34573-9 010 $a1-394-34572-0 024 7 $a10.1002/9781394345731 024 8 $aCIPO000282515 035 $a(MiAaPQ)EBC32257383 035 $a(Au-PeEL)EBL32257383 035 $a(CKB)40158915200041 035 $a(OCoLC)1531973699 035 $a(CaSebORM)9781394345700 035 $a(OCoLC)1531951587 035 $a(OCoLC-P)1531951587 035 $a(EXLCZ)9940158915200041 100 $a20250812d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDesign and Manufacturing Practices for Performability Engineering 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2025. 215 $a1 online resource (449 pages) 225 1 $aPerformability Engineering Series 311 08$a1-394-34570-4 327 $aCover -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgment -- Chapter 1 Mathematical and Physical Reality of Reliability -- 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. 327 $a1.3.4 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 -- Chapter 2 Models and Solutions for Practical Reliability and Availability Assessment -- 2.1 Introduction -- 2.2 Non-State-Space Methods -- 2.3 State-Space-Based Methods -- 2.4 Multi-Level Models -- 2.5 Conclusions -- References -- Chapter 3 Reliability Prediction of Artificial Hip Joints -- 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. 327 $a3.4.4 Effect of Patient Weight Changes -- 3.4.5 Conclusions -- References -- Chapter 4 Principles and Philosophy for an Integrated and Distributed Approach for Reliability and Extensions to Other Qualities -- 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 -- Chapter 5 An Analytic Toolbox for Optimizing Condition Based Maintenance (CBM) Decisions -- 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 -- Chapter 6 Degradation Modeling with Imperfect Maintenance -- 6.1 Introduction. 327 $a6.2 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 -- Chapter 7 Asset Maintenance in Railway: Powered by New Technology and Driven by Sustainability -- 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 -- Chapter 8 ISO 14001 History and Applications -- 8.1 Need for EMS - Help to Prevent Environmental Disasters. 327 $a8.2 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 -- Chapter 9 Process Failure Mode and Effects Analysis (PFMEA) with Fuzzy ANP-MARCOS-Based Approach for Manufacturing Process Quality Assessment -- 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 -- Chapter 10 Advanced Neural Networks for Estimation of All-Terminal Network Reliability -- 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. 327 $a10.2 All-Terminal Estimation of Network Reliability Considering Degradation with Bayesian Methods, Monte Carlo, and Deep Neural Networks. 330 $a"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 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: Comprehensively covers a wide range of topics in the area of QRAMs; Provides in-depth explanations of best practices in various elements of Performability Engineering; Explores expert insights and real-world scenarios to demonstrate the many applications of QRAMs. 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. 410 0$aPerformability Engineering Series 606 $aReliability (Engineering) 606 $aEngineering design 615 0$aReliability (Engineering) 615 0$aEngineering design. 676 $a620/.004 700 $aChaturvedi$b Sanjay K$01593010 701 $aGargama$b Heeralal$01845427 701 $aRai$b Rajiv Nandan$01691456 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911021978703321 996 $aDesign and Manufacturing Practices for Performability Engineering$94477383 997 $aUNINA