Vibration Analysis and Fault Diagnosis of Automotive Suspension Systems

Abstract

This paper presents an online condition monitoring strategy for vehicle suspension systems, focusing on the influence of tire pressure at each wheel. The Stochastic Subspace Identification (SSI) method is employed to extract key modal parameters of a full vehicle model, including natural frequencies, damping ratios, and mode shapes. A seven-degree-of-freedom (7-DOF) vehicle model is developed in MATLAB, where vertical acceleration signals measured at the four corners of the vehicle body serve as inputs to the SSI algorithm. Common suspension faults are simulated, particularly tyre under-inflation, by reducing nominal tire pressure by 10%, 20%, 30%, and 40% at individual wheels. Fault detection is achieved by monitoring variations in modal energy, especially those related to bounce and pitch motions. The results indicate that, tyre pressure changes significantly influence the distribution of modal energy within the system. Experimental vibration data under varying pressure conditions further validate the effectiveness and accuracy of the proposed method for early fault detection and suspension condition monitoring.