Vehicle Lateral Dynamics Fault Diagnosis Using an Autoassociative Neural Network and a Fuzzy System

摘要

The main goals of a fault diagnosis system in a vehicle are to prevent dangerous situations for occupants. This domain is a complex system that turns the monitoring task a very challenging one. On one hand, there is an inherent uncertainty caused by noisy sensor measurements and unmodeled dynamics and in the other hand the existence of false alarms that appears in a natural way due to the high correlation between several variables. The present work is a variant of a proposal made by the author. This paper presents a new approach based on history data process that can manage the variable correlation and can carry out a complete fault diagnosis system. In the first phase, the approach learns behavior from normal operation of the system using an autoassociative neural network. On a second phase a fuzzy system is carried out in order to diminish the presence of false alarms that could be originated by the noise presence and then a competitive neural network is used to give the final diagnosis. Results are shown for a ten variables vehicle monitoring.