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@article{164243,
        author = {Y. V. S. Swathi and Bommireddy Vyshnavi and Kanchumati Narendra and Molabanti Yamini and sirimamilla chandana},
        title = {Strategic Predictive Maintenance Using XG Boost},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {495-497},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164243},
        abstract = {Predictive maintenance is a data-driven approach that uses predictive modelling to assess the state of equipment and determine the optimal timing for maintenance activities. This technique is particularly advantageous for industries heavily reliant on equipment for their operations, such as manufacturing, transportation, energy, and healthcare. Predictive maintenance (PdM) uses data analysis to identify operational anomalies and potential equipment defects, enabling timely repairs before failures occur. It aims to minimize maintenance frequency, avoiding unplanned outages and unnecessary preventive maintenance costs. By implementing a predictive maintenance solution with Python and XGboost, we can proactively identify and address issues to prevent costly downtime and ensure the smooth operation of our milling machines.},
        keywords = {},
        month = {},
        }