Disruptive Trends in Automation Technology
The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fiel...
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| Format: | Online |
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| Sprog: | engelsk |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online adgang: | ONIX_20240704_9783725812110_115 |
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| _version_ | 1869518371760898048 |
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| collection | Directory of Open Access Books |
| description | The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses, and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control, and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance, and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data, and artificial intelligence are all driving the transformation of conventional simulators to digital twins. |
| format | Online |
| id | doab-20.500.12854ir-139319 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1393192024-07-04T09:41:53Z Disruptive Trends in Automation Technology Sierla, Seppo Hästbacka, David Zenger, Kai CNN architecture normalization techniques intelligent fault diagnosis vibration continuous software engineering DevOps electricity market Machine Learning MLOps time-series analysis performance control loop monitoring overall controller efficiency single-input single-output cyber security cyber exercise digital twin critical systems food supply chain robotics force control stability soft sensor wastewater treatment modelling resource efficiency exhaustive search trajectory tracking control powered parafoil system linear active disturbance rejection control twin delayed deep deterministic policy gradient automated troubleshooting real-time product activity detection problem root cause analysis machine learning explainable AI proactive SaaS support cross-pollination Juglans regia literature review self-compatibility walnut blight disease aerial pollination artificial pollination technologies pollination drone high-voltage testing surface-mount devices (SMDs) dielectric fluid hydrodynamics SOIC package misalignment thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses, and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control, and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance, and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data, and artificial intelligence are all driving the transformation of conventional simulators to digital twins. 2024-07-04T09:41:50Z 2024-07-04T09:41:50Z 2024 book ONIX_20240704_9783725812110_115 9783725812110 9783725812127 https://directory.doabooks.org/handle/20.500.12854/139319 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9316 https://mdpi.com/books/pdfview/book/9316 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1212-7 10.3390/books978-3-7258-1212-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725812110 9783725812127 206 open access |
| spellingShingle | CNN architecture normalization techniques intelligent fault diagnosis vibration continuous software engineering DevOps electricity market Machine Learning MLOps time-series analysis performance control loop monitoring overall controller efficiency single-input single-output cyber security cyber exercise digital twin critical systems food supply chain robotics force control stability soft sensor wastewater treatment modelling resource efficiency exhaustive search trajectory tracking control powered parafoil system linear active disturbance rejection control twin delayed deep deterministic policy gradient automated troubleshooting real-time product activity detection problem root cause analysis machine learning explainable AI proactive SaaS support cross-pollination Juglans regia literature review self-compatibility walnut blight disease aerial pollination artificial pollination technologies pollination drone high-voltage testing surface-mount devices (SMDs) dielectric fluid hydrodynamics SOIC package misalignment thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science Disruptive Trends in Automation Technology |
| title | Disruptive Trends in Automation Technology |
| title_full | Disruptive Trends in Automation Technology |
| title_fullStr | Disruptive Trends in Automation Technology |
| title_full_unstemmed | Disruptive Trends in Automation Technology |
| title_short | Disruptive Trends in Automation Technology |
| title_sort | disruptive trends in automation technology |
| topic | CNN architecture normalization techniques intelligent fault diagnosis vibration continuous software engineering DevOps electricity market Machine Learning MLOps time-series analysis performance control loop monitoring overall controller efficiency single-input single-output cyber security cyber exercise digital twin critical systems food supply chain robotics force control stability soft sensor wastewater treatment modelling resource efficiency exhaustive search trajectory tracking control powered parafoil system linear active disturbance rejection control twin delayed deep deterministic policy gradient automated troubleshooting real-time product activity detection problem root cause analysis machine learning explainable AI proactive SaaS support cross-pollination Juglans regia literature review self-compatibility walnut blight disease aerial pollination artificial pollination technologies pollination drone high-voltage testing surface-mount devices (SMDs) dielectric fluid hydrodynamics SOIC package misalignment thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | CNN architecture normalization techniques intelligent fault diagnosis vibration continuous software engineering DevOps electricity market Machine Learning MLOps time-series analysis performance control loop monitoring overall controller efficiency single-input single-output cyber security cyber exercise digital twin critical systems food supply chain robotics force control stability soft sensor wastewater treatment modelling resource efficiency exhaustive search trajectory tracking control powered parafoil system linear active disturbance rejection control twin delayed deep deterministic policy gradient automated troubleshooting real-time product activity detection problem root cause analysis machine learning explainable AI proactive SaaS support cross-pollination Juglans regia literature review self-compatibility walnut blight disease aerial pollination artificial pollination technologies pollination drone high-voltage testing surface-mount devices (SMDs) dielectric fluid hydrodynamics SOIC package misalignment thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240704_9783725812110_115 |