A fault detection scheme for modeled and unmodeled faults in a simple hydraulic actuator system using an extended Kalman filter

Abstract

In this work an extended Kalman filter (EKF) is used to detect a variety of faults in a simple hydraulic actuator system. Much interest exists in detecting faults in their early stages in the hopes that machine downtime and repair costs can be kept to a minimum. This EKF model employs two different techniques for identifying the presence of system faults. In one case, parameters of interest are included in the statespace model as augmented states. Faults are then introduced into these new states, and the EKF successfully detects the faults by tracking the new post-fault parameter values. The second method is an indirect approach for identifying unmodeled faults. These faults become apparent through analysis of the difference between a state measurement and estimate, known as error residual data. It is shown that, for this simple hydraulic system, this extended Kalman filter detects system faults confidently and promptly.