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In the course of manufacturing excellence, decision makers are consistently confronted with the task of making choices that will enhance and meet industrial plant’s requirements. To this end, it is essential to maintain machines and equipment in a timely manner, which can prove to be one of the primary challenges. Predictive maintenance (PdM) strategy can enable real-time maintenance, providing numerous benefits such as reduced downtime, lower costs, and improved production quality. This article tries to demonstrate efficient physical parameters used in PdM field. The paper presents a case study operated in industrial production process to compare between the most used algorithm in predicting equipment failures. Future research can improve prediction accuracy with other artificial intelligence tools.