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AI-Driven Predictive Maintenance and Energy Optimization in Intelligent Manufacturing
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Contemporary manufacturing is being redrawn by the transition of Industry 4.0 by involving the combination of artificial intelligence (AI), the Internet of Things (IoT), and cyber-physical systems. In the heart of this change, there is the necessity to decrease unscheduled shutdown, reduce operating expenditures associated with any energy use, and maximize the results without suppression the quality of output. In this paper, a practical method of executing predictive maintenance and energy optimization using AI is outlined in detail with an aim of adopting it into intelligent manufacturing system. We study state-of-the-art models of machine learning: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer-based models of machine learning, used to predict faults and schedule condition-based maintenance on real time sensor data. In addition, we explore the topic of reinforcement learning and optimization algorithms with industrial applications with the purpose of improving energy efficiency in the industrial process. The final system architecture brings together IoT-based information collection, edge computing, analytical information in clouds, and digital twins to develop a closed loop feedback process that intelligent decision-making process. There are empirical findings that indicate a decrease in energy by 25 35 percent and up to 60 percent increase in the response time of maintenance across different test cases. We test our solution on synthetic and industrial real-life datasets and evaluate the performance of our models against accuracy, F1-score, and energy savings. The paper also presents the practical issues in preprocessing of data, explanability of models and integration of the system. Finally, the proposed solution in this paper does not only provide a scalable AI-based framework of the predictive maintenance and energy optimization approach but also gives grounds to future work in the field of self-optimizing and autonomous smart factories. We believe the findings will assist manufacturers in their quest to enhance their operational efficiency in a bid to meet the global sustainability and digitalization objectives.
References
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How to cite this paper
@article{1709342,
author = {Adriano Dos Santos Junior},
title = {AI-Driven Predictive Maintenance and Energy Optimization in Intelligent Manufacturing},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {1126-1155},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709342.pdf},
abstract = {Contemporary manufacturing is being redrawn by the transition of Industry 4.0 by involving the combination of artificial intelligence (AI), the Internet of Things (IoT), and cyber-physical systems. In the heart of this change, there is the necessity to decrease unscheduled shutdown, reduce operating expenditures associated with any energy use, and maximize the results without suppression the quality of output. In this paper, a practical method of executing predictive maintenance and energy optimization using AI is outlined in detail with an aim of adopting it into intelligent manufacturing system. We study state-of-the-art models of machine learning: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer-based models of machine learning, used to predict faults and schedule condition-based maintenance on real time sensor data. In addition, we explore the topic of reinforcement learning and optimization algorithms with industrial applications with the purpose of improving energy efficiency in the industrial process. The final system architecture brings together IoT-based information collection, edge computing, analytical information in clouds, and digital twins to develop a closed loop feedback process that intelligent decision-making process. There are empirical findings that indicate a decrease in energy by 25 35 percent and up to 60 percent increase in the response time of maintenance across different test cases. We test our solution on synthetic and industrial real-life datasets and evaluate the performance of our models against accuracy, F1-score, and energy savings. The paper also presents the practical issues in preprocessing of data, explanability of models and integration of the system.
Finally, the proposed solution in this paper does not only provide a scalable AI-based framework of the predictive maintenance and energy optimization approach but also gives grounds to future work in the field of self-optimizing and autonomous smart factories. We believe the findings will assist manufacturers in their quest to enhance their operational efficiency in a bid to meet the global sustainability and digitalization objectives.
},
month = {June},
}