Predictive maintenance in smart factories : architectures, methodologies, and use-cases / / Tania Cerquitelli [and five others], editors

• Pubblicazione: Singapore : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (239 pages)
• Disciplina: 629.8
• Soggetto topico: Predictive control
• Persona (resp. second.): CerquitelliTania
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- Contributors -- Acronyms -- Methodologies and Enabling Technologies -- Industrial Digitisation and Maintenance: Present and Future -- 1 Introduction -- 2 Literature Review -- 2.1 Maintenance Approaches -- 2.2 Data Management Architectures -- 2.3 Data-Driven Analytics: Methodologies and Algorithms -- 2.4 Integrated Solutions -- 3 Key Challenges Identified -- 4 Outlook -- 4.1 The SERENA Vision -- References -- A Hybrid Cloud-to-Edge Predictive Maintenance Platform -- 1 Introduction -- 2 Related Works -- 2.1 Predictive Maintenance in Industry 4.0 -- 2.2 Cloud and Edge Computing -- 2.3 Predictive Maintenance Technologies -- 3 SERENA Design -- 3.1 Intermodal Collaboration -- 3.2 Data Management -- 3.3 Service Orchestration -- 3.4 Security -- 4 SERENA Architecture -- 4.1 Core Services -- 4.2 Peripheral Services -- 4.3 Provisioning and Deployment -- 5 Conclusions -- References -- Data-Driven Predictive Maintenance: A Methodology Primer -- 1 Introduction -- 2 Predictive Maintenance: Main Concepts and Key Standards -- 2.1 The European Standard CEN-EN 13306 -- 2.2 Guidelines and Requirements for Automated Maintenance Software -- 2.3 Guidelines for Automated Condition Monitoring System and Diagnostics -- 2.4 Guidelines for Prognosis -- 3 Data-Driven Methodologies -- 3.1 Data-Driven Modelling and Physics-Based Modelling -- 3.2 Predictive and Descriptive Data Analytics -- 3.3 Model Building, Deployment, and Updating -- 3.4 RUL Estimation: Key Concepts -- 4 The SERENA Data-Driven Pipeline -- 4.1 Data Preparation -- 4.2 Semi-supervised Data Labelling -- 4.3 Predictive Analytics -- 4.4 Self Assessment of Model Drift -- 4.5 RUL Estimation -- 5 Data Formats and Models -- 5.1 A Linked Data Approach -- 5.2 MIMOSA -- 6 Data Analytics Architecture -- 6.1 Technologies and Tools for Analytics Services -- 6.2 Edge Analytics Applications. ; 7 Discussion and Future Research Directions -- References -- Services to Facilitate Predictive Maintenance in Industry4.0 -- 1 Introduction -- 2 Literature Review -- 3 Predictive Maintenance Services in SERENA -- 3.1 Physics-Based and Hybrid Modelling (O& -- M Analytics Toolbox) -- 3.2 Data-Driven Analytics -- 3.3 Maintenance Aware Scheduling -- 3.4 Operator Support Services -- 4 Conclusion -- References -- Industrial Use-cases -- Predictive Analytics in Robotic Industry -- 1 Introduction -- 2 SERENA Contribution -- 2.1 Architecture -- 2.2 Approach -- 2.3 Results -- 2.4 System Deployment -- 3 Discussion -- 4 Outlook -- References -- Remote Condition Monitoring and Control for Maintenance Activities in Coordinate Measurement Machine: A Data-Driven Approach for the Metrology Engineering Industry -- 1 TRIMEK Use Case -- 1.1 Introduction -- 1.2 Coordinate Measuring Machine (CMM) -- 1.3 CMM Subsystem Under Study -- 1.4 Main Objective of the Test Bed -- 1.5 Needs and Motivation -- 1.6 Expected Contribution of SERENA -- 2 SERENA System in TRIMEK Use Case -- 2.1 Remote Factory Condition and Control -- 2.2 AI Condition-Based Maintenance and Planning Techniques -- 2.3 AR-Based Technologies for Remote Assistance and Human Operator Support -- 2.4 Cloud-Based Platform for Versatile Remote Diagnostics -- 3 Results -- 4 Conclusion -- References -- Estimating Remaining Useful Life: A Data-Driven Methodology for the White Goods Industry -- 1 Introduction -- 2 The White Goods Industry: Use Case Description -- 2.1 Industrial Process -- 2.2 Preventive Maintenance: Current Status and Requirements -- 3 Literature Review -- 4 The SERENA Data Analytics Pipeline -- 4.1 Data Sources -- 4.2 Preprocessing -- 4.3 Time-Window Degradation Analysis -- 4.4 Model Building and RUL Prediction -- 4.5 Deployment -- 5 Results -- 5.1 Training and Test Dataset. ; 5.2 Experimental Settings -- 5.3 Model Building and Test -- 6 Lessons Learned -- 7 Conclusions -- References -- Predictive Analytics in the Production of Elevators -- 1 Challenges and Needs in Elevator Production Industry -- 2 Sensors and Data Acquisition Devices for Remote Condition Monitoring -- 2.1 Development of a Low-Cost Solution -- 2.2 Edge Processing -- 2.3 Use Case Results -- 2.4 Summary -- 3 Remote Factory Condition Monitoring and Predictive Analytics -- 3.1 Remote Connection to Production Line -- 3.2 Versatile Cloud-Based Platform for Remote Diagnostics -- 3.3 Predictive AI Condition-Based Maintenance and Service Scheduling -- 3.4 AR-Based Assistance for Operators During Maintenance -- 4 Integrated Solution and Technical Architecture -- 5 Impact -- 6 Conclusion -- References -- Predictive Maintenance in the Production of Steel Bars: A Data-Driven Approach -- 1 Introduction -- 2 Literature Review -- 3 Maintenance Needs and Challenges in the Steel Bar Production Industry -- 4 Steel Bar Production Case Study: Present and Future -- 4.1 Maintenance Needs -- 4.2 Equipment Description -- 4.3 Steel Bar Production Process -- 5 SERENA System in the Steel Bar Production Case -- 5.1 Architecture -- 5.2 Data Connections -- 5.3 Data Acquisition -- 5.4 Data Analytics -- 5.5 Results -- 5.6 System Deployment -- 6 Discussion and Conclusion -- References -- In-Situ Monitoring of Additive Manufacturing -- 1 Introduction -- 2 Additive Manufacturing: Process and Methods -- 3 In-Situ Monitoring: Current Solutions -- 4 Case-Study: Layer-Wise Defects Monitoring System for DMLS Based on Visible-Range Imaging -- 4.1 Data Acquisition and Processing System -- 4.2 Defects Detection Algorithms -- 4.3 Part Profile Monitoring Algorithm -- 4.4 Dataset Augmentation with GAN -- 5 Experimental Results -- 6 Conclusion and Future Works -- References -- Appendix Glossary. ; Index.
• Titolo autorizzato: Predictive maintenance in smart factories
• ISBN: 981-16-2940-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996466730103316
• Lo trovi qui: Univ. di Salerno
• Serie: Information Fusion and Data Science