Model-Based Fault Detection for an Active Vehicle Suspension
Published April 19, 2004 by University of Salerno in Italy
An automobile fault detection and diagnosis method is described for an active vehicle suspension system. The method is based on mathematical models of the suspension system. It is shown how the unknown parameters of these mathematical models can be obtained experimentally by parameter estimation. Furthermore, the semi-physical approach of the local linear neuronal network LOLIMOT is applied for the generation of parity equations. Both, parameter estimation and parity equations are then used for model-based fault detection and identification. Various faults are simulated with test rig data and it is shown, how these faults are detected.