Driving Efficiency: Harnessing Big Data and Data Mining for Next-Gen Predictive Maintenance in Automotive

Abstract

The implementation of efficient maintenance practices is critical for minimizing equipment malfunctions and disruptions in production processes, particularly within the automotive sector. Predictive maintenance emerges as a proactive strategy for identifying potential equipment failures before they occur, ensuring operational safety and cost-effectiveness. Leveraging big data and advanced analytics technologies enhances the predictive maintenance approach by enabling real-time data analysis and decision-making. We explore  the utilization of big data for predictive maintenance in the automotive industry, emphasizing the importance of historical maintenance data and the application of various analytical methods. The integration of predictive maintenance within a big data framework revolutionizes maintenance strategies, optimizing asset reliability, minimizing downtime, and driving significant cost savings. Through proactive maintenance interventions guided by predictive insights, automotive companies can enhance operational efficiency, improve customer satisfaction, and maintain a competitive edge in the industry. In  this paper we presented Predictive maintenance methods, Big data architecture, maintenance strategy and framework, Maintenance benefits and future directions.