REAL-TIME DATA PROCESSING IN PREDICTIVE MAINTENANCE: ENHANCING INDUSTRIAL EFFICIENCY AND EQUIPMENT LONGEVITY

This article presents a comprehensive review of predictive maintenance strategiesenhanced by real-time data processing in industrial settings. As industries transitionfrom reactive and preventive maintenance approaches to more sophisticated predictivemethods, the integration of advanced data-driven techniques has become crucial. Weexplore the evolution of maintenance strategies, highlighting the limitations oftraditional methods and the transformative potential of predictive maintenance. Thearticle emphasizes the pivotal role of real-time data processing in enabling accuratefailure predictions and timely interventions. By examining various real-time datahandling techniques such as edge computing, stream processing, and in-memorycomputing, we illustrate how these technologies contribute to reducing unplanneddowntime, extending equipment lifespan, and optimizing maintenance costs. Casestudies from manufacturing, energy, and transportation sectors demonstrate thepractical implementation and benefits of these advanced maintenance systems.Furthermore, we discuss future trends, including the integration of artificial intelligenceand machine learning, while addressing challenges related to cybersecurity, scalability,and standardization. This article provides valuable insights for researchers andpractitioners seeking to leverage real-time data processing to enhance predictivemaintenance strategies and improve overall operational efficiency in industrialenvironments.