Fault identification of the vehicle suspension system based on binocular vision and kinematic decoupling

Abstract

Suspension faults have a detrimental impact on the safety and handling stability of a vehicle. Therefore, monitoring the condition of suspension systems is significant to ensuring the safe operation of modern vehicles. This paper proposes an online monitoring scheme that utilizes binocular vision and kinematic decoupling, to fulfill real-time monitoring requirements for suspensions. To implement the proposed method, a system consisting of a binocular camera and an inertial measurement unit (IMU) is established for acquiring vibration signals from the vehicle body. Additionally, the vibration signals are analyzed with stochastic subspace identification (SSI) method to determine the modal parameters of suspensions. By analyzing the changes in suspension modal parameters, the types and degrees of faults in the suspension system were identified and evaluated. The experimental results show that the proposed method can effectively extract the vertical vibration signals of a vehicle. Moreover, the fault identification method based on modal parameters can identify the changes in vehicle modal parameters with high reliability under different spring stiffness, damper damping and tire pressure conditions. The proposed method is proven to be effective in identifying suspension faults, paving a way for online condition monitoring and fault diagnosis of vehicle suspensions.