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Smart Industrial Systems Using Artificial Intelligence Technologies

Author(s) Prof. Dr. Jorge Casillas
Country Spain
Abstract The integration of artificial intelligence into industrial systems is transforming conventional manufacturing and production environments into interconnected, adaptive, data-driven, and intelligent ecosystems. Smart industrial systems combine artificial intelligence with industrial automation, robotics, Internet of Things technologies, machine learning, computer vision, predictive analytics, digital twins, edge computing, cloud platforms, intelligent control, and advanced decision-support mechanisms. These technologies enable industries to improve productivity, equipment reliability, product quality, energy efficiency, process flexibility, safety, and resource utilization. This study examines smart industrial systems using artificial intelligence technologies and proposes an integrated conceptual framework for AI-enabled industrial transformation. A conceptual descriptive research design based on secondary academic and technical literature was adopted to analyze the applications of artificial intelligence across manufacturing, process industries, industrial maintenance, quality management, robotics, supply-chain operations, energy management, production planning, and industrial safety. The analysis identifies machine learning, deep learning, computer vision, natural language processing, predictive analytics, intelligent optimization, reinforcement learning, digital twins, and AI-assisted robotics as major technologies supporting smart industrial environments.
The findings indicate that artificial intelligence can improve industrial decision-making by converting large volumes of operational data into predictions, recommendations, automated responses, and optimization strategies. Conventional industrial systems frequently depend on predefined rules, fixed schedules, periodic inspections, and manually interpreted information. Although these methods remain useful for stable operations, they may be insufficient for complex environments characterized by variable demand, nonlinear processes, equipment degradation, customized production, supply-chain uncertainty, and rapidly changing operational conditions. AI-enabled systems can address these challenges through continuous data analysis and adaptive decision-making. Industrial IoT provides the information infrastructure required by smart industrial systems. Sensors installed on machines, motors, pumps, compressors, production lines, warehouses, and environmental systems can generate continuous streams of operational information.
Keywords Smart Industrial Systems, Artificial Intelligence, Industrial Automation, Machine Learning, Industrial IoT, Smart Manufacturing, Predictive Maintenance, Computer Vision, Digital Twins, Intelligent Robotics, Industrial Analytics, AI-Enabled Manufacturing
Field Engineering
Published In Volume 3, Issue 6, November-December 2021
Published On 2021-11-08
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