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The rapid advancement of Artificial Intelligence (AI) and Deep Learning (DL) technologies has transformed industrial automation by enabling intelligent, adaptive, and self-optimizing systems. This paper proposes a two-level hybrid architecture for smart industrial automation based on AI-driven deep learning models. The architecture integrates both machine-level and system-level intelligence to enhance operational efficiency, fault detection, predictive maintenance, and decision-making in real time. At the first level, deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are employed for data sensing, pattern recognition, and anomaly detection from industrial Internet of Things (IIoT) devices. The second level utilizes AI-based control mechanisms, including reinforcement learning and expert systems, to optimize process automation and resource management. The hybrid design ensures scalability, adaptability, and interoperability across various industrial domains. Experimental and analytical evaluations demonstrate improved accuracy, reduced downtime, and energy-efficient performance. This approach signifies a step toward achieving fully autonomous, intelligent, and sustainable industrial ecosystems.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: K. Sujatha, sujathakota29@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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