Learning-based fault diagnosis of air brake system using wheel speed data

Abstract

Faults in the air brake system used in Heavy Commercial Road Vehicles (HCRVs) would adversely affect the vehicle’s dynamic performance, and hence their prompt detection is critical for vehicle safety. This paper first investigates the effect of air brake system faults through extensive hardware-in-loop experiments. These faults were observed to degrade the braking response, yaw stability, and vehicle braking distance. In many countries, an antilock brake system is mandatory in HCRVs, and wheel speed data are readily available. Inspired by this, the feasibility of using wheel speed data to detect faults is investigated in this study. As an initial step of predictive maintenance, a fault diagnostic scheme based on a supervised learning algorithm, Support Vector Machine (SVM) that uses only wheel speed data has been developed. The SVM algorithm’s efficacy was tested for 1937 test cases that encompassed a wide range of operating conditions. It was found that a Gaussian kernel SVM (G-SVM) provided a good classification accuracy of 96.54%, demonstrating its ability to predict a faulty condition accurately. The standard deviation of G-SVM’s prediction accuracy for five groups of data sets with 100 instances was found to be 1.57%, which shows that the model is more precise to predict the fault/no-fault condition of the air brake system.