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Abstract
The integration of Artificial Intelligence (AI) algorithms in cloud infrastructure has significantly transformed industrial applications by enabling predictive maintenance and real-time data analysis. This research paper explores the deployment of AI-driven predictive maintenance systems in industrial settings, emphasizing the role of cloud infrastructure in data aggregation, processing, and analysis. By leveraging advanced machine learning models and anomaly detection algorithms, industries can achieve proactive maintenance strategies that minimize downtime and enhance operational efficiency. Furthermore, the incorporation of real-time data analysis frameworks provides decision-makers with actionable insights, fostering data-driven operational strategies. This study reviews existing literature to identify key research gaps and proposes a comprehensive architecture that integrates AI, cloud computing, and industrial IoT for effective predictive maintenance and real-time analytics.
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