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@article{176549,
        author = {Harsh Diwathe and Prof. Neha Nandanwar and Deepanshu Bagde and Gopal Ojha and Prasheel  Lonare},
        title = {Cognitive Equipment Management: Anticipating Maintenance Through Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6037-6045},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176549},
        abstract = {In the era of Industry 4.0, the need for efficient, reliable, and cost-effective equipment management has become paramount. This review paper examines the transformative potential of Cognitive Equipment Management (CEM), a data-driven approach that leverages machine learning algorithms, Internet of Things (IoT) technologies, and big data analytics to transition from traditional reactive maintenance to predictive maintenance. By integrating historical operational data with real-time sensor inputs, CEM systems enable early detection of equipment anomalies and degradation patterns, thereby significantly reducing unplanned downtime and maintenance costs. The paper synthesizes recent advances in predictive maintenance research, including studies on Remaining Useful Life (RUL) prediction using neural networks and innovative feature selection techniques, and critically assesses the methodologies and tools that underpin these approaches. Additionally, the review addresses the challenges of integrating CEM with legacy systems and highlights the importance of human-machine collaboration in refining maintenance decisions. Through a comprehensive analysis of current literature and empirical case studies, this paper provides insights into the operational and economic benefits of proactive maintenance strategies while identifying key research gaps and future directions for sustainable industrial practice.},
        keywords = {Predictive Maintenance, Neural Networks, Cognitive Equipment Management, Industrial Efficiency, Machine Learning.},
        month = {April},
        }