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@article{176643,
        author = {Mahisha Mudaliar and Prakash Patel and Nishith Parmar},
        title = {AI DRIVEN PREDICTIVE MAINTAINANCE STRATERGIES FOR SMART MANUFACTURING SYSTEMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6904-6909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176643},
        abstract = {This research investigates the implementation of artificial intelligence (AI)-driven predictive maintenance systems in Industry 4.0 manufacturing environments. The study develops a comprehensive framework utilizing machine learning algorithms, including deep learning neural networks, random forests, and support vector machines, to analyze data from IoT sensors, maintenance histories, and operational parameters. Results demonstrate a 35% reduction in unplanned downtime and 25% decrease in maintenance costs compared to traditional preventive maintenance approaches. The framework addresses key challenges in data quality, system scalability, and real-time decision-making capabilities. Multiple case studies across manufacturing sectors validate the system's effectiveness in improving equipment reliability, extending asset lifecycles, and enhancing production quality. This research contributes to the advancement of smart manufacturing systems by establishing a robust methodology for AI-based predictive maintenance integration, ultimately fostering more resilient and efficient production environments.},
        keywords = {},
        month = {April},
        }