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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.14958565 |
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| _version_ | 1866901505550843904 |
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| author | Michael Onyekachukwu Nwabueze Abdulbasit Aliyu Kayode Joshua Adegbo Chukwujekwu Damian Ikemefuna |
| author_facet | Michael Onyekachukwu Nwabueze Abdulbasit Aliyu Kayode Joshua Adegbo Chukwujekwu Damian Ikemefuna |
| contents | <p>Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14958565 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency Michael Onyekachukwu Nwabueze Abdulbasit Aliyu Kayode Joshua Adegbo Chukwujekwu Damian Ikemefuna Industrial automation Deep learning Human intervention Throughput downtime Failure management <p>Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.</p> |
| title | Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency |
| topic | Industrial automation Deep learning Human intervention Throughput downtime Failure management |
| url | https://doi.org/10.5281/zenodo.14958565 |