A New Fault Prognosis of MFS System Using Integrated Extended Kalman Filter and Bayesian Method
Document Type
Article
Publication Date
3-10-2018
Publication Title
IEEE Transactions on Industrial Informatics
Keywords
Bayesian Method, extended Kalman filter, prognosis, remaining useful life
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.
DOI
10.1109/TII.2018.2815036
ISSN
15513203
Recommended Citation
Kordestani, Mojtaba; Samadi, M. Foad; Saif, Mehrdad; and Khorasani, Khashayar. (2018). A New Fault Prognosis of MFS System Using Integrated Extended Kalman Filter and Bayesian Method. IEEE Transactions on Industrial Informatics.
https://scholar.uwindsor.ca/electricalengpub/273