Stochastic Modeling and Time‐Frequency Analysis for Predictive Maintenance of Automotive Suspension Systems

ABSTRACT This article presents a real‐time predictive maintenance model of vehicle suspensions based on vibration signal analysis. The study is grounded in the observation that suspension wear and failure are primarily driven by cumulative stresses and external shocks encountered during vehicle operation. We use a wavelet‐based technique integrated with stochastic modeling and lifetime data analysis to predict the remaining useful life (RUL) of the suspension. The proposed framework provides a decision‐making tool for determining whether and when suspension systems should be subjected to inspection, replacement, or overhaul. An empirical application, using vibration data from a uniaxial accelerometer mounted on a vehicle suspension under varying road conditions, validates the theoretical model and estimation procedure.