A robust fault detection and identification strategy for aerospace systems

Abstract

Fault detection and identification strategies utilize knowledge of the systems and measurements to accurately and quickly predict faults. These strategies are important to mitigate full system failures, and are particularly important for the safe and reliable operation of aerospace systems. In this paper, a relatively new estimation method called the sliding innovation filter (SIF) is combined with the interacting multiple model (IMM) method. The corresponding method, referred to as the SIF-IMM, is applied on a magnetorheological actuator which was built for experimentation. These types of actuators are similar to hydraulic-based ones, which are commonly found in aerospace systems. The method is shown to accurately identify faults in the system. The results are compared and discussed with other popular nonlinear estimation strategies including the extended and unscented Kalman filters.