Predictive Maintenance: Review Of Current Methods And Techniques

Résumé: Achieving operational excellence is crucial for maintaining competitiveness in today’s industrial landscape. Traditional maintenance strategies, both reactive and preventive, often fail to fully utilize the abundant data available. The emergence of Industry 4.0, emphasizing data acquisition and analytics, has introduced predictive maintenance, allowing for real-time insights into equipment health and proactive interventions. Business Intelligence (BI) systems play a central role in this shift by converting raw data into actionable insights, facilitating informed decision-making. This work examines the integration of Natural Language Processing (NLP), probabilistic models, and machine learning techniques in predictive maintenance, offering a comprehensive review of current methodologies. The research highlights how these advanced technologies can improve equipment reliability, reduce downtime, and optimize resource allocation, thereby enhancing production efficiency and profitability. The findings support a transition from traditional maintenance approaches to more proactive strategies, aligning with Industry 4.0 goals and fostering a data-driven, automated industrial environment

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