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ASTM standardization news
| ASTM standardization news |
| Pubbl/distr/stampa | [Philadelphia, PA], : [publisher not identified] |
| Descrizione fisica | 1 online resource |
| Disciplina | 620/.004 |
| Soggetto topico |
Standards, Engineering
Standardization Research Science Technology |
| Soggetto genere / forma | Periodicals. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996205162303316 |
| [Philadelphia, PA], : [publisher not identified] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
ASTM standardization news
| ASTM standardization news |
| Pubbl/distr/stampa | [Philadelphia, PA], : [publisher not identified] |
| Descrizione fisica | 1 online resource |
| Disciplina | 620/.004 |
| Soggetto topico |
Standards, Engineering
Standardization Research Science Technology |
| Soggetto genere / forma |
Periodical
Periodicals. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910144845803321 |
| [Philadelphia, PA], : [publisher not identified] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Design and Manufacturing Practices for Performability Engineering
| Design and Manufacturing Practices for Performability Engineering |
| Autore | Chaturvedi Sanjay K |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (449 pages) |
| Disciplina | 620/.004 |
| Altri autori (Persone) |
GargamaHeeralal
RaiRajiv Nandan |
| Collana | Performability Engineering Series |
| Soggetto topico |
Reliability (Engineering)
Engineering design |
| ISBN |
1-394-34573-9
1-394-34572-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- 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.
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 -- 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. 3.4.4 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. 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 -- 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. 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 -- 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. 10.2 All-Terminal Estimation of Network Reliability Considering Degradation with Bayesian Methods, Monte Carlo, and Deep Neural Networks. |
| Record Nr. | UNINA-9911021978703321 |
Chaturvedi Sanjay K
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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