1 Senior Consultant Michael Raymond Nigeria Limited, Nigeria.
2 Department of Environmental and Sustainability, University of Saskachewan, Canada.
3 Department of Physics, Astronomy and Geoscience, Towson University, Towson, MD.
4 Department of Cybersecurity American National University USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1919-1943
Received on 19 August 2024; revised on 17 September 2024; accepted on 19 September 2024
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.
Industrial automation; Deep learning; Human intervention; Throughput; downtime; Failure management
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Michael Onyekachukwu Nwabueze, Abdulbasit Aliyu, Kayode Joshua Adegbo and Chukwujekwu Damian Ikemefuna. Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency. World Journal of Advanced Research and Reviews, 2024, 23(3), 1919-1943. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2882