Model-based Sensor Fault Diagnosis of Vehicle Suspensions with a Support Vector Machine

Abstract

In this paper, a means of generating residuals based on a quarter-car model and evaluating them using a support vector machine (SVM) is proposed. The proposed model-based residual generator shows very robust performance regardless of unknown road surface conditions. In addition, an SVM classifier without empirically set thresholds is used to evaluate the residuals. The proposed method is expected to reduce the effort required to design fault diagnosis algorithms. While an unknown input observer is used to generate the residual, the relative velocity of the vehicle suspension is obtained additionally. The proposed algorithm is verified using commercial vehicle simulator Carsim with Matlab & Simulink. As a result, the fault diagnosis algorithm proposed in this paper can detect sensor faults that cannot be detected by a limit checking method and can reduce the effort required when designing algorithms.