REAL-TIME DATA PROCESSING IN PREDICTIVE MAINTENANCE: ENHANCING INDUSTRIAL EFFICIENCY AND EQUIPMENT LONGEVITY
Keywords:
Predictive Maintenance, Real-Time Data Processing, Industrial Asset Management, Condition Monitoring, Sensor TechnologyAbstract
This article presents a comprehensive review of predictive maintenance strategies enhanced by real-time data processing in industrial settings. As industries transition from reactive and preventive maintenance approaches to more sophisticated predictive methods, the integration of advanced data-driven techniques has become crucial. We explore the evolution of maintenance strategies, highlighting the limitations of traditional methods and the transformative potential of predictive maintenance. The article emphasizes the pivotal role of real-time data processing in enabling accurate failure predictions and timely interventions. By examining various real-time data handling techniques such as edge computing, stream processing, and in-memory computing, we illustrate how these technologies contribute to reducing unplanned downtime, extending equipment lifespan, and optimizing maintenance costs. Case studies from manufacturing, energy, and transportation sectors demonstrate the practical implementation and benefits of these advanced maintenance systems. Furthermore, we discuss future trends, including the integration of artificial intelligence and machine learning, while addressing challenges related to cybersecurity, scalability, and standardization. This article provides valuable insights for researchers and practitioners seeking to leverage real-time data processing to enhance predictive maintenance strategies and improve overall operational efficiency in industrial environments.
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