Predictive Maintenance of Armoured Vehicles using Machine Learning
  Approaches
Prajit Sengupta Anant Mehta Prashant Singh Rana
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.