Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

Abstract

Accurate fault diagnosis in air brake is crucial to reduce frequent brake inspection and maintenance in heavy commercial road vehicles. Existing model-based fault diagnostic schemes work well under limited vehicle operating conditions, which is insufficient for developing an on-board monitoring device. In this context, a learning-based fault identification scheme using the Random Forest technique, which accommodates the vehicle's wide operating conditions, is proposed. This scheme identifies the brake's fault levels with a better classification accuracy of 92% compared to techniques such as Naïve Bayes, k -Nearest Neighbors, Support Vector Machine, and Decision Tree. Further, a fault-tolerant controller is proposed to overcome the vehicle's directional instability arising due to the brake fault. Two sliding mode controllers, namely differential brake control and steering angle control, were developed to control the yaw angle. These have been implemented in a Hardware in Loop experimental platform with the vehicle dynamic simulation software TruckMaker®.

Keywords

• Classification algorithms
• fault diagnosis
• fault-tolerant controller
• heavy road vehicle
• machine learning
• random forests