Displacement reconstruction based on Kalman smoothing in multi-rate suspension health monitoring systems

Abstract Suspension displacements and wheel center accelerations are important signals for suspension health monitoring systems to improve vehicle reliability and safety. The current way to obtain these signals is to install sensors on vehicles to conduct direct measurements. Usually, displacements are sampled at a slower rate than accelerations due to technical or economic limitations in real scenarios. This paper introduces a method for displacement reconstruction with low-sampling-rate displacement and high-sampling-rate acceleration measurements by formulating the reconstruction problem as a state estimation problem. A state-space model is established by identifying two data-driven models: a time-series Auto-Regressive model and a Finite Impulse Response model. Then, Kalman smoothing is used to estimate the displacement. A series of experiments have been done to show that the estimates from Kalman smoother coincide with the measurements.