Predictive Maintenance Strategies in Agriculture Using Survival Analysis

Abstract

This study explores the application of survival analysis models—Kaplan-Meier, Cox Proportional Hazards, Aalen’s Additive, and Random Survival Forests—to predict failure times and identify critical factors influencing the reliability and efficiency of agricultural machinery. By integrating Directed Acyclic Graphs (DAGs), the research enhances the understanding of causal relationships among operational variables, thus providing a robust framework for predictive maintenance strategies. Utilizing simulated data to ensure confidentiality and integrity, this study demonstrates how modern data science techniques can significantly optimize maintenance schedules and reduce unplanned downtime. The findings underscore the potential of survival models to revolutionize agricultural machinery management by facilitating data-driven decision-making, improving operational efficiency, and reducing maintenance costs. The research not only contributes to agricultural engineering but also offers broader implications for predictive maintenance practices across various industries.