Predictive Maintenance for IT Infrastructure: A Machine Learning Approach


Authors : Abdulaziz N Mansouri

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/5cx47are

Scribd : https://tinyurl.com/33dykyvh

DOI : https://doi.org/10.5281/zenodo.14437188

Abstract : As IT infrastructure grows in complexity, proactive maintenance strategies are becoming increasingly crucial. Traditional reactive maintenance approaches often fail to prevent failures and optimize resource utilization. This research proposes a machine learning-based approach to predictive maintenance to anticipate potential hardware failures in IT infrastructure components. The model can schedule preventive maintenance interventions by analyzing historical data and real-time sensor readings, minimizing downtime and reducing operational costs. The methodology involves data collection, preprocessing, feature engineering, feature selection, model development, and deployment. Various machine learning algorithms are explored, including time series forecasting, anomaly detection, and classification. The paper also discusses ethical considerations and future research directions, such as hybrid approaches, explainable AI, transfer learning, continuous learning, and edge computing

References :

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As IT infrastructure grows in complexity, proactive maintenance strategies are becoming increasingly crucial. Traditional reactive maintenance approaches often fail to prevent failures and optimize resource utilization. This research proposes a machine learning-based approach to predictive maintenance to anticipate potential hardware failures in IT infrastructure components. The model can schedule preventive maintenance interventions by analyzing historical data and real-time sensor readings, minimizing downtime and reducing operational costs. The methodology involves data collection, preprocessing, feature engineering, feature selection, model development, and deployment. Various machine learning algorithms are explored, including time series forecasting, anomaly detection, and classification. The paper also discusses ethical considerations and future research directions, such as hybrid approaches, explainable AI, transfer learning, continuous learning, and edge computing

Video Explanation for Published paper

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