Armoured vehicles are specialized and complex pieces of machinery designed to
operate in high-stress environments, often in combat or tactical situations.
This study proposes a predictive maintenance-based ensemble system that aids in
predicting potential maintenance needs based on sensor data collected from
these vehicles. The proposed model's architecture involves various models such
as Light Gradient Boosting, Random Forest, Decision Tree, Extra Tree Classifier
and Gradient Boosting to predict the maintenance requirements of the vehicles
accurately. In addition, K-fold cross validation, along with TOPSIS analysis,
is employed to evaluate the proposed ensemble model's stability. The results
indicate that the proposed system achieves an accuracy of 98.93%, precision of
99.80% and recall of 99.03%. The algorithm can effectively predict maintenance
needs, thereby reducing vehicle downtime and improving operational efficiency.
Through comparisons between various algorithms and the suggested ensemble, this
study highlights the potential of machine learning-based predictive maintenance
solutions.

提出了一种基于预测性维护的集成系统，通过从传感器数据收集的信息，准确预测装甲车辆的维护需求，评估了所提出的集成模型的稳定性和表现，结果表明该系统具有高精度和高召回率，可以有效预测维护需求，从而减少车辆停机时间，提高运营效率，强调了基于机器学习的预测性维护解决方案的潜力。