ENHANCING MAINTENANCE PROCESSES THROUGH PREDICTIVE ANALYTICS IN MECHANICAL SYSTEMS

Abstract

This paper examines the transformative role of predictive analytics in enhancing maintenance processes for mechanical systems. Traditional maintenance approaches, including reactive and preventive strategies, are first evaluated, highlighting their limitations in optimizing equipment performance and resource allocation. The study then explores the concept of predictive maintenance, emphasizing its potential to move maintenance practices through the application of advanced analytics and machine learning techniques. The research examines various predictive analytics methods, including regression analysis and machine learning algorithms such as decision trees, random forests, and support vector machines. These techniques are discussed in the context of their application to mechanical systems maintenance, with a focus on data collection methodologies, types of data utilized, and the architecture required for implementation. This comprehensive review underscores the significant potential of predictive analytics to optimize maintenance processes, reduce downtime, and improve overall equipment effectiveness in mechanical systems. The findings suggest that despite implementation challenges, the adoption of predictive maintenance strategies can lead to substantial improvements in operational efficiency and cost-effectiveness for organizations across various industries.