EMaintenance : essential electronic tools for efficiency
Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibiliti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Galar, Diego [verfasserIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Place of publication not identified: Academic Press ; 2017 |
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| Schlagwörter: |
COMPUTERS ; Programming ; Open Source COMPUTERS ; Software Development & Engineering ; General COMPUTERS ; Software Development & Engineering ; Tools |
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| Formangabe: |
Electronic books |
| Umfang: |
Online Ressource |
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| Reproduktion: |
Online-Ausg. |
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| Links: | |
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| ISBN: |
978-0-12-811154-3 0-12-811154-2 |
| Katalog-ID: |
892971703 |
|---|
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| 245 | 1 | 0 | |a EMaintenance |b essential electronic tools for efficiency |c Diego Galar and Uday Kumar |
| 264 | 1 | |a Place of publication not identified |b Academic Press |c 2017 | |
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| 520 | |a Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing | ||
| 520 | |a 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks | ||
| 520 | |a 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor | ||
| 520 | |a 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance | ||
| 520 | |a Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range | ||
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9780128111543 : electronic bk. 978-0-12-811154-3 0128111542 : electronic bk. 0-12-811154-2 0128111534 9780128111536 (DE-627)892971703 (DE-576)517991446 (DE-599)GBV892971703 (OCoLC)991670744 (OCoLC)991670744 (ELSEVIER)ocn991670744 (EBP)014125609 DE-627 ger DE-627 rakwb eng QA76.76.S64 COM051390 bisacsh COM051230 bisacsh COM051440 bisacsh COM 051230 bisacsh COM 051440 bisacsh COM 051390 bisacsh COM 051390 bisacsh COM 051440 bisacsh COM 051230 bisacsh EMaintenance essential electronic tools for efficiency Diego Galar and Uday Kumar Place of publication not identified Academic Press 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range Online-Ausg. Software maintenance Computer systems Sensor networks Sensor networks Computer systems Software maintenance COMPUTERS ; Programming ; Open Source COMPUTERS ; Software Development & Engineering ; General COMPUTERS ; Software Development & Engineering ; Tools Computer systems Sensor networks Software maintenance Logiciels - Entretien et réparations (CaQQLa)201-0120442 Systèmes informatiques (CaQQLa)201-0017888 Réseaux de capteurs (CaQQLa)201-0388026 Electronic books Electronic books Galar, Diego verfasserin aut Kumar, Uday oth 0128111534 9780128111536 Erscheint auch als Druck-Ausgabe Galar, Diego EMaintenance [Place of publication not identified] : Academic Press, 2017 0128111534 9780128111536 http://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128111536.pdf V:DE-601 X:Bowker application/pdf 2018-01-26 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 005.16 045F 005.1/6 105 01 0841 4074511010 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499993083 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514689378 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 454027581X EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520357683 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046046775 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128111536 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128111536 185 01 3519 http://www.sciencedirect.com/science/book/9780128111536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128111536 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128111536 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128111536 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| spelling |
9780128111543 : electronic bk. 978-0-12-811154-3 0128111542 : electronic bk. 0-12-811154-2 0128111534 9780128111536 (DE-627)892971703 (DE-576)517991446 (DE-599)GBV892971703 (OCoLC)991670744 (OCoLC)991670744 (ELSEVIER)ocn991670744 (EBP)014125609 DE-627 ger DE-627 rakwb eng QA76.76.S64 COM051390 bisacsh COM051230 bisacsh COM051440 bisacsh COM 051230 bisacsh COM 051440 bisacsh COM 051390 bisacsh COM 051390 bisacsh COM 051440 bisacsh COM 051230 bisacsh EMaintenance essential electronic tools for efficiency Diego Galar and Uday Kumar Place of publication not identified Academic Press 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range Online-Ausg. Software maintenance Computer systems Sensor networks Sensor networks Computer systems Software maintenance COMPUTERS ; Programming ; Open Source COMPUTERS ; Software Development & Engineering ; General COMPUTERS ; Software Development & Engineering ; Tools Computer systems Sensor networks Software maintenance Logiciels - Entretien et réparations (CaQQLa)201-0120442 Systèmes informatiques (CaQQLa)201-0017888 Réseaux de capteurs (CaQQLa)201-0388026 Electronic books Electronic books Galar, Diego verfasserin aut Kumar, Uday oth 0128111534 9780128111536 Erscheint auch als Druck-Ausgabe Galar, Diego EMaintenance [Place of publication not identified] : Academic Press, 2017 0128111534 9780128111536 http://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128111536.pdf V:DE-601 X:Bowker application/pdf 2018-01-26 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 005.16 045F 005.1/6 105 01 0841 4074511010 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499993083 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514689378 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 454027581X EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520357683 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046046775 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128111536 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128111536 185 01 3519 http://www.sciencedirect.com/science/book/9780128111536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128111536 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128111536 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128111536 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfields_unstemmed |
9780128111543 : electronic bk. 978-0-12-811154-3 0128111542 : electronic bk. 0-12-811154-2 0128111534 9780128111536 (DE-627)892971703 (DE-576)517991446 (DE-599)GBV892971703 (OCoLC)991670744 (OCoLC)991670744 (ELSEVIER)ocn991670744 (EBP)014125609 DE-627 ger DE-627 rakwb eng QA76.76.S64 COM051390 bisacsh COM051230 bisacsh COM051440 bisacsh COM 051230 bisacsh COM 051440 bisacsh COM 051390 bisacsh COM 051390 bisacsh COM 051440 bisacsh COM 051230 bisacsh EMaintenance essential electronic tools for efficiency Diego Galar and Uday Kumar Place of publication not identified Academic Press 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range Online-Ausg. Software maintenance Computer systems Sensor networks Sensor networks Computer systems Software maintenance COMPUTERS ; Programming ; Open Source COMPUTERS ; Software Development & Engineering ; General COMPUTERS ; Software Development & Engineering ; Tools Computer systems Sensor networks Software maintenance Logiciels - Entretien et réparations (CaQQLa)201-0120442 Systèmes informatiques (CaQQLa)201-0017888 Réseaux de capteurs (CaQQLa)201-0388026 Electronic books Electronic books Galar, Diego verfasserin aut Kumar, Uday oth 0128111534 9780128111536 Erscheint auch als Druck-Ausgabe Galar, Diego EMaintenance [Place of publication not identified] : Academic Press, 2017 0128111534 9780128111536 http://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128111536.pdf V:DE-601 X:Bowker application/pdf 2018-01-26 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 005.16 045F 005.1/6 105 01 0841 4074511010 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499993083 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514689378 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 454027581X EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520357683 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046046775 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128111536 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128111536 185 01 3519 http://www.sciencedirect.com/science/book/9780128111536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128111536 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128111536 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128111536 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfieldsGer |
9780128111543 : electronic bk. 978-0-12-811154-3 0128111542 : electronic bk. 0-12-811154-2 0128111534 9780128111536 (DE-627)892971703 (DE-576)517991446 (DE-599)GBV892971703 (OCoLC)991670744 (OCoLC)991670744 (ELSEVIER)ocn991670744 (EBP)014125609 DE-627 ger DE-627 rakwb eng QA76.76.S64 COM051390 bisacsh COM051230 bisacsh COM051440 bisacsh COM 051230 bisacsh COM 051440 bisacsh COM 051390 bisacsh COM 051390 bisacsh COM 051440 bisacsh COM 051230 bisacsh EMaintenance essential electronic tools for efficiency Diego Galar and Uday Kumar Place of publication not identified Academic Press 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range Online-Ausg. Software maintenance Computer systems Sensor networks Sensor networks Computer systems Software maintenance COMPUTERS ; Programming ; Open Source COMPUTERS ; Software Development & Engineering ; General COMPUTERS ; Software Development & Engineering ; Tools Computer systems Sensor networks Software maintenance Logiciels - Entretien et réparations (CaQQLa)201-0120442 Systèmes informatiques (CaQQLa)201-0017888 Réseaux de capteurs (CaQQLa)201-0388026 Electronic books Electronic books Galar, Diego verfasserin aut Kumar, Uday oth 0128111534 9780128111536 Erscheint auch als Druck-Ausgabe Galar, Diego EMaintenance [Place of publication not identified] : Academic Press, 2017 0128111534 9780128111536 http://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag Volltext https://www.sciencedirect.com/science/book/9780128111536 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128111536.pdf V:DE-601 X:Bowker application/pdf 2018-01-26 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 005.16 045F 005.1/6 105 01 0841 4074511010 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499993083 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514689378 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 454027581X EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520357683 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046046775 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128111536 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128111536 185 01 3519 http://www.sciencedirect.com/science/book/9780128111536 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128111536 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128111536 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128111536 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfieldsSound |
9780128111543 : electronic bk. 978-0-12-811154-3 0128111542 : electronic bk. 0-12-811154-2 0128111534 9780128111536 (DE-627)892971703 (DE-576)517991446 (DE-599)GBV892971703 (OCoLC)991670744 (OCoLC)991670744 (ELSEVIER)ocn991670744 (EBP)014125609 DE-627 ger DE-627 rakwb eng QA76.76.S64 COM051390 bisacsh COM051230 bisacsh COM051440 bisacsh COM 051230 bisacsh COM 051440 bisacsh COM 051390 bisacsh COM 051390 bisacsh COM 051440 bisacsh COM 051230 bisacsh EMaintenance essential electronic tools for efficiency Diego Galar and Uday Kumar Place of publication not identified Academic Press 2017 Online Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range Online-Ausg. 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Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range |
| abstractGer |
Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range |
| abstract_unstemmed |
Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing 1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks 1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor 1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 1.1.2.2.2 Sensitivity; 1.1.2.2.3 Span or Dynamic Range |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a22002652 4500</leader><controlfield tag="001">892971703</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240503140404.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190225s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780128111543</subfield><subfield code="c">: electronic bk.</subfield><subfield code="9">978-0-12-811154-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0128111542</subfield><subfield code="c">: electronic bk.</subfield><subfield code="9">0-12-811154-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">0128111534</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9780128111536</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)892971703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-576)517991446</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV892971703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)991670744</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)991670744</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)ocn991670744</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)014125609</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.76.S64</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM051390</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM051230</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM051440</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM 051230</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM 051440</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM 051390</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM 051390</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM 051440</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM 051230</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">EMaintenance</subfield><subfield code="b">essential electronic tools for efficiency</subfield><subfield code="c">Diego Galar and Uday Kumar</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Place of publication not identified</subfield><subfield code="b">Academic Press</subfield><subfield code="c">2017</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Annotation, eMaintenance: Essential Electronic Tools for Efficiency enables the reader to improve efficiency of operations, maintenance staff, infrastructure managers and system integrators, by accessing a real time computerized system from data to decision. In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 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General</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Software Development & Engineering ; Tools</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensor networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software maintenance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Logiciels - Entretien et réparations</subfield><subfield code="0">(CaQQLa)201-0120442</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Systèmes informatiques</subfield><subfield code="0">(CaQQLa)201-0017888</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Réseaux de capteurs</subfield><subfield code="0">(CaQQLa)201-0388026</subfield></datafield><datafield tag="655" ind1=" " ind2="0"><subfield code="a">Electronic 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Sensors and Data Acquisition … Sensors in Maintenance and the Need to Integrate Information … Sensors Put Intelligence Into Maintenance … Basic Sensor Technology … Role of Sensors and Objectives of Sensing … Distributed Intelligent Sensors … Infrastructure for Intelligent Systems … Sensor Fusion … Principles of Sensor Fusion … Motivation for Sensor Fusion … Limitations of Sensor Fusion … Types of Sensor Fusion … Architectures for Sensor Fusion … Sensor Networks: A Distributed Approach in Large Assets … Sensor Network Research in the 21st Century … Technology Trends … Wireless Sensor Network … New Applications of Sensor Networks … Smart Sensors … Structure of Smart Sensor … Standards of Smart Sensor Network … Importance and Adoption of Smart Sensor … General Architecture of Smart Sensor … Description of Smart Sensor Architecture … Varieties of Smart Sensors … Smart Sensors for Condition Based Maintenance … Energy Harvesting for Sensors and Configuration Issues … Energy Harvesting Sources … Energy Harvesting for Microelectromechanical Systems … Harvesting Methods … Applications … References … Further Reading … Data Collection … Data Collection in Industry … Data Needs for Industry Management … Data Collection Strategy … Data Collection Methods … Data Cleaning … Data Cleaning Problems … Data Cleaning Approaches … Tool Support … Data Cleaning Overview … Data Cleaning From a Statistical Perspective … New Challenges … Data Sanitation … Data Sanitization Techniques … Data Sanitization Methods … Data Compression and Transmission … Data Compression … Data Compression Strategies … A Data Compression Model … Data Transmission … Data Transmission and Open Systems Interconnection Model … References … Preprocessing and Features … Time and Frequency Domains for Data Representation … Time Domain Versus Frequency Domain … Vibration Data Representation for Advanced Technology Facilities … Time Series Data Representation … Feature Selection … Filter Methods Used for Feature Selection … Wrapper Method Approach … A Statistical View of Feature Selection … A Machine Learning View of Feature Selection … Cross-Validation Versus Overfitting … Feature Selection Algorithms … Feature Extraction … Feature Extraction From the Time Domain … Other Types of Feature Extraction Methods … References … Data and Information Fusion From Disparate Asset Management Sources … Online and Off-Line Condition Monitoring Information … Condition Monitoring Data and Automatic Asset Data Collection … Fusion of Maintenance and Control Data … Data Fusion: A Need for Maintenance Processes … Cloud Computing … Computerized Maintenance Management Systems … Computerized Maintenance Management System Needs Assessment … Computerized Maintenance Management System Capabilities … Computerized Maintenance Management System Benefits … Computerized Maintenance Management System Resources … The Role of Computerized Maintenance Management System … Computerized Maintenance Management System Implementation … Maintenance Knowledge Management Fusing Computerized Maintenance Management … System and Condition Monitoring Supervisory Control and Data Acquisition and Automation Data From Programmable Logicp. … Controllers and Similar Devices Supervisory Control and Data Acquisition System … Basics of Supervisory Control and Data Acquisition … Architecture of Supervisory Control and Data Acquisition … Types of Supervisory Control and Data Acquisition Systems … Applications of Supervisory Control and Data Acquisition … Understanding Supervisory Control and Data Acquisition … Programmable Logic Controller Programming … Connections and Protocols … Enterprise Resource Planning and Other Cooperative Information Related to the Asset … Understanding Enterprise Resource Planning … Core Components of Enterprise Resource Planning … Why Use Enterprise Resource Planning? … Enterprise Resource Planning Implementation Process … Modeling the Requirements for Enterprise Resource Planning … Model-Based Customization … Modeling the Future: Enterprise Resource Planning Goes e-Business … Conclusion: Enterprise Resource Planning Versus Computerized Maintenance Management System … References … Diagnosis … Goals of Detection, identification, and Localization of Failures … Diagnostic Framework … Artificial Intelligence-Based Machine Condition Monitoring and Fault Diagnosis … Neural Network Alternatives … Supervising the Diagnostic Neural Network … Other Methods for Fault Diagnosis … Data-Driven Versus Physical Models … Introduction … Data-Driven Approaches … Physics-Based Approaches … Physical Model-Based Methods … Supervised, Semisupervised, and Unsupervised Learning: Issues and Challenges … Supervised and Unsupervised Learning … Semisupervised Learning … No Fault Found (NFF) and Issues of Complex Systems … Introduction to NFF … What Causes NFFs? … Classifying Depot Level Repair Causes … Standards for NFF … Organizational Procedures and Administration … Implications of NFF … References … Prognosis … Introduction … Maintenance and Prognosis … Types of Maintenance … Prognosis Techniques … Concept of Prognostics … Remaining Useful Life … Technical Approaches … Remaining Useful Life and Prognostics … Prognostic Techniques … Selection of Prognosis Techniques for Different Types of Assets … Rotating Machines … Infrastructures … Complex Systems … Context-Based Prognosis: The Influence of Information and Communication Technology … in Remaining Useful Life Estimation Context-Aware Condition Monitoring … Diagnosis With Anomaly Detection … Context-Driven e-Maintenance … Conclusions and Discussion … References … Further Reading … Maintenance Decision Support Systems … A New Era in Industry … Maintenance … What is Industry … Industry … Key Components … Principles of Industry … Virtualization and Emulation: The e-Factory for Fault Rate Reduction … Terminology … History … Virtualization for Manufacturing and Internet of Things … Embedded Virtualization … Emulation Frameworks … Multivariate Maintenance Decision Support: A Consequence of Internet of Things … Decision Support Systems … Representation of the Decision-Making Process … Condition-Based Maintenance Decision Support Systems … Internet of Things … The End of Traditional Maintenance Approaches: Real-Time Decisions Based on Industrial Big Data … Big Data: Analytics and Decision-Making … Real-Time Responses With Big Data … Real-Time Big Data Analytics Applications … Real-Time Big Data Analytics Challenges … Big Data Techniques and Technologies … EMaintenance and Maintenance … Impact of Technology on Operation and Maintenancep. … Key Performance Indicators Maintenance … Challenges of Maintenance … EMaintenance … Type of Maintenance Indicators: Leading Versus Lagging and Hard Versus Soft … Maintenance Performance Indicators in the Literature … Key Performance Indicators … References … Further Reading … Actuators and Self-Maintenance Approaches … Intelligent (Smart) Materials for Maintenance … Introduction … Concept of Intelligent Materials … Types of Smart Materials … Classification of Smart Materials … Applications of Smart Materials … Future Trends in Smart Materials … Improvement in Intelligent/Smart Materials … Smart Devices With Actuation Capabilities: Smart Bearings … Smart Devices … Smart Device Paradigm … Smart Device Architecture … Smart Device Specification … Smart Bearings: From Sensing to Actuation … Risk Assessment for Maintenance Actions … Diagnosis and Prognosis as Maintenance Decision Support System Enablers: Risk Assessment … Conclusions … Robotics in Maintenance Duties … Application Examples and Techniques … Emerging Trends … References … Further Reading … Index … |
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In recent years, the exciting possibilities of eMaintenance have become increasingly recognized as a source of productivity improvement in industry. The seamless linking of systems and equipment to control centres for real time reconfiguring is improving efficiency, reliability, and sustainability in a variety of settings.The book provides an introduction to collecting and processing data from machinery, explains the methods of overcoming the challenges of data collection and processing, and presents tools for data driven condition monitoring and decision making. This is a groundbreaking handbook for those interested in the possibilities of running a plant as a smart asset.Provides an introduction to collecting and processing data from machineryExplains how to use sensor-based tools to increase efficiency of diagnosis, prognosis, and decision-making in maintenanceDescribes methods for overcoming the challenges of data collection and processing</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.2.4.4 Categorization Based on Sensor Configuration1.2.5 Architectures for Sensor Fusion; 1.2.5.1 Joint Directors of Laboratories Fusion Architecture; 1.2.5.2 Waterfall Fusion Process Model; 1.2.5.3 Boyd Model; 1.2.5.4 LAAS Architecture; 1.2.5.5 Omnibus Model; 1.3 SENSOR NETWORKS: A DISTRIBUTED APPROACH IN LARGE ASSETS; 1.3.1 Sensor Network Research in the 21st Century; 1.3.2 Technology Trends; 1.3.3 Wireless Sensor Network; 1.3.3.1 Sensor Node Architecture; 1.3.3.2 Characteristics of Wireless Sensor Networks; 1.3.3.3 Fields of Application of Wireless Sensor Networks</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.3.3.3.1 Security and Surveillance1.3.3.3.2 Environmental Monitoring; 1.3.3.4 Health Applications; 1.3.3.5 Smart Buildings; 1.3.3.6 Energy Control Systems; 1.3.3.7 Area Monitoring; 1.3.3.8 Agriculture Applications; 1.3.3.8.1 Agriculture; 1.3.3.8.2 Greenhouse Monitoring; 1.3.3.9 Industrial Applications; 1.3.3.10 Structural Monitoring; 1.3.4 New Applications of Sensor Networks; 1.3.4.1 Infrastructure Security; 1.3.4.2 Environment and Habitat Monitoring; 1.3.4.3 Industrial Sensing; 1.3.4.4 Traffic Control; 1.4 SMART SENSORS; 1.4.1 Structure of Smart Sensor</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.4.2 Standards of Smart Sensor Network1.4.3 Importance and Adoption of Smart Sensor; 1.4.3.1 Cost Improvement; 1.4.3.2 Reduced Cost of Bulk Cables and Connectors; 1.4.3.3 Remote Diagnostics; 1.4.3.4 Enhancement of Application; 1.4.3.4.1 Self-Calibration; 1.4.3.4.2 Computation; 1.4.3.4.3 Communication; 1.4.3.4.4 Multisensing; 1.4.3.5 System Reliability; 1.4.3.6 Better Signal to Noise Ratio; 1.4.3.7 Sensor Improvement; 1.4.4 General Architecture of Smart Sensor; 1.4.5 Description of Smart Sensor Architecture; 1.4.6 Varieties of Smart Sensors; 1.4.7 Smart Sensors for Condition Based Maintenance</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Front Cover; eMaintenance; eMaintenance: Essential Electronic Tools for Efficiency; Copyright; Contents; 1 -- Sensors and Data Acquisition; 1.1 SENSORS IN MAINTENANCE AND THE NEED TO INTEGRATE INFORMATION; 1.1.1 Sensors Put Intelligence Into Maintenance; 1.1.1.1 Sensing Maintenance Needs; 1.1.1.2 Going Wireless; 1.1.1.3 Enhancing Equipment, Both New and Old; 1.1.1.4 Taking Intelligence to the Next Level; 1.1.2 Basic Sensor Technology; 1.1.2.1 Sensor Data Sheets; 1.1.2.2 Sensor Performance Characteristics; 1.1.2.2.1 Transfer Function; 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