A Comparative Study with J48 and Random Tree Classifier for Predicting the State of Hydraulic Braking System through Vibration Signals

Even though hydraulic brakes are valuable safety elements for riders, they are necessary for braking in a good condition. Vibration signatures may be used to assess the condition of the brake components. In this research, the monitor and make status tracking and the dynamic data acquisition method with a piezo-electric transducer is suggested as a promising approach to these challenges by machine-learning. The Ford EcoSport rig was used to get the vibration signal for good and bad braking conditions. The vibration signals had analytical mathematical predictive characteristics, and the decision tree model, J48 was used in the selection of the signals. In order to define a certain concern, a structural decision does not state the number of features required. Therefore, to find the right number of features a rigorous analysis is required. The failure study of the hydraulic braking system of Ford EcoSport has been determined using the decision tree classification J48 and the random wood classification. The findings were compared and showed that a random forest classifier with a computational time of 0.43s had a maximum classification accuracy of 98.5%.