Paper Title
Predictive Maintenance of Armoured Vehicles using Machine Learning Approaches

Abstract
This paper proposes a predictive maintenance sys-tem for armoured vehicles using machine learning approaches, specifically ensemble models. The method seeks to increase the reliability and efficiency of armoured vehicles by predicting potential maintenance needs based on sensor data collected from the vehicles. The proposed ensemble model approach involves the use of various models such as Light Gradient Boosting, Random Forest, Decision Tree, Extra Tree Classifier and Gradient Boosting to accurately predict the maintenance requirements of the vehicles. In addition, K-fold cross validation was used to test the stability of the suggested ensemble model. The effectiveness of the system is evaluated using real-world data collected from armoured vehicles in military operations. The results indicate that the proposed system can effectively predict maintenance needs, thereby reducing vehicle downtime and improving operational efficiency. The study demonstrates the potential of machine learning-based predictive maintenance systems in improving the reliability and performance of armoured vehicles. Keywords - Ensemble Models, Machine Learning Models, Classification, Bootstrapping, Topsis Analysis, Cross-Validation.