TY - GEN
T1 - Fault diagnosis of hybrid system with an efficient particle filtering estimation approach
AU - Zhao, Jianyu
AU - Zeng, Shengkui
AU - Guo, Jianbin
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/16
Y1 - 2014/12/16
N2 - Fault diagnosis is one of the central issues in hybrid system study, and the OTPF algorithm has been proposed to handle this problem. However, the performance of OTPF may become weaken in some cases. In this article, a new approach based on particle filtering is proposed to handle these situations. Comparable to OTPF, the method integrates information in modes with similar behavior to obtain better state estimation, and it considers history tracking of the system to make a more wise decision about mode detection at each time step. In addition, the ensemble Kalman filter is introduced to improve the quality of particles in the filtering process. Finally, a numerical simulation is conducted to demonstrate the efficiency of the new approach. The result indicates that the proposed approach can make more accurate estimation of hybrid system with lower computation burden than the OTPF algorithm.
AB - Fault diagnosis is one of the central issues in hybrid system study, and the OTPF algorithm has been proposed to handle this problem. However, the performance of OTPF may become weaken in some cases. In this article, a new approach based on particle filtering is proposed to handle these situations. Comparable to OTPF, the method integrates information in modes with similar behavior to obtain better state estimation, and it considers history tracking of the system to make a more wise decision about mode detection at each time step. In addition, the ensemble Kalman filter is introduced to improve the quality of particles in the filtering process. Finally, a numerical simulation is conducted to demonstrate the efficiency of the new approach. The result indicates that the proposed approach can make more accurate estimation of hybrid system with lower computation burden than the OTPF algorithm.
KW - Ensemble Kalman fitler
KW - OTPF
KW - fault diagnosis
KW - hybrid system
KW - particle filter
UR - https://www.scopus.com/pages/publications/84943171825
U2 - 10.1109/PHM.2014.6988150
DO - 10.1109/PHM.2014.6988150
M3 - 会议稿件
AN - SCOPUS:84943171825
T3 - Proceedings of 2014 Prognostics and System Health Management Conference, PHM 2014
SP - 140
EP - 144
BT - Proceedings of 2014 Prognostics and System Health Management Conference, PHM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 Prognostics and System Health Management Conference, PHM 2014
Y2 - 24 August 2014 through 27 August 2014
ER -