A New Fault Prognosis of MFS System Using Integrated Extended Kalman Filter and Bayesian Method

Abstract

This paper presents a new fault prognosis approach for a multifunctional spoiler (MFS) system which employs an extended Kalman filter (EKF) and Bayesian theorem method for prognosis. The MFS is an important part of an aircraft spoiler control system (SCS), and thus, prognosis and health management (PHM) of this system improves the safety of the aircraft. To monitor the system, residual estimation based on the EKF method is utilized to observe the progress of the failure in the system. Then, a new measure is introduced by using a transformation to estimate degradation path (DP) of the failure in the system. Furthermore, a new recursive Bayesian method is invoked to predict the RUL of the system using the estimated DP data. Finally, for performance assessment, relative accuracy (RA) is utilized to evaluate the accuracy of the proposed method.