Forecasting failure-prone air pressure systems (FFAPS) in vehicles using machine learning

Abstract

Vehicles become an inevitable factor in everyone’s life. Sometimes it becomes a threat to human lives and society. For any real-time-based applications, everyone should focus on predicting failure-prone components. A vehicle’s air pressure system (APS) is one of its most important parts. If any system failure happens against APS it leads to core-financial losses, which in turn sometimes leads to loss of human lives. Prediction of APS negligence in a real-time application requires a deep diagnosis and diligent solution. In this study, we developed a machine learning model to predict system failure against APS. A real-time dataset that includes the 170 features and the presence of high-class imbalance data and missing values has been taken and experimentally validated with existing linear and nonlinear classifiers. The performance metrics results show that the Random Forest classifier exceeds other algorithms for training and testing data with an accuracy and F1 score of 99.5 and 99.5 percent respectively.