Predictive Maintenance of Server using Machine Learning and Deep Learning

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Anjali Yeole, Dashrath Mane, Mahendra Gawali, Manas Lalwani, Mahindra Chetwani, Parth Suryavanshi, Harshita Anala

Abstract

Traditional IT maintenance often leads to wasted resources and downtime. This paper explores how predictive maintenance (PdM) with machine learning can revolutionize server management in Industry 4.0. By comparing various machine learning models for PdM, we analyze their effectiveness in predicting server failures. The paper emphasizes the critical role PdM plays in boosting server reliability and driving industry transformation, supported by statistics highlighting its growing importance. Furthermore, we bridge the theory-practice gap by proposing a web application specifically designed for server PdM, allowing for proactive maintenance and reduced downtime. Finally, we explore future research directions and emerging trends in server PdM, providing valuable insights for organizations seeking to optimize maintenance practices and achieve operational excellence in the digital age.

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