Transforming Predictive Maintenance in Automotive Systems through Big Data and Data Mining Techniques

Abstract

Abstract. Effective maintenance practices are essential for reducing equipment failures and minimizing disruptions in production, especially in the automotive industry. Predictive maintenance has emerged as a proactive approach to detect potential equipment issues before they result in breakdowns, improving both safety and cost efficiency. By harnessing big data and advanced analytics, predictive maintenance benefits from real-time data processing and more informed decision-making. This paper examines the role of big data in enhancing predictive maintenance strategies within the automotive sector, highlighting the significance of historical data and the use of various analytical techniques. The integration of predictive maintenance with a big data framework transforms maintenance operations by boosting asset reliability, reducing downtime, and achieving substantial cost reductions. Predictive insights enable automotive companies to take preemptive maintenance actions, improving operational performance, customer satisfaction, and competitive positioning. This paper presents predictive maintenance methodologies, big data architecture, maintenance strategies and frameworks, as well as the benefits and future directions of predictive maintenance.