TY - JOUR
T1 - Ensemble of Models for Fatigue Crack Growth Prognostics
AU - Nguyen, Hoang Phuong
AU - Liu, Jie
AU - Zio, Enrico
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - Various models of fatigue crack growth in different scenarios have been proposed in the literature. Here, in this paper, we propose a general prognostic framework for tracking crack evolution in equipment undergoing fatigue and predicting the Remaining Useful Life (RUL). The main contribution of this work is to integrate Particle Filtering (PF) and a new ensemble model which combines diverse physical degradation models with respect to their accuracy performance in previous time steps, in order to maximize the overall prediction capability. To validate the effectiveness of the proposed framework, a case study concerning multiple fatigue crack growth degradations is extensively investigated.
AB - Various models of fatigue crack growth in different scenarios have been proposed in the literature. Here, in this paper, we propose a general prognostic framework for tracking crack evolution in equipment undergoing fatigue and predicting the Remaining Useful Life (RUL). The main contribution of this work is to integrate Particle Filtering (PF) and a new ensemble model which combines diverse physical degradation models with respect to their accuracy performance in previous time steps, in order to maximize the overall prediction capability. To validate the effectiveness of the proposed framework, a case study concerning multiple fatigue crack growth degradations is extensively investigated.
KW - Fatigue crack growth
KW - dynamic ensemble
KW - multiple stochastic degradation
KW - particle filter
KW - prognostics and health management
KW - remaining useful life
UR - https://www.scopus.com/pages/publications/85065184216
U2 - 10.1109/ACCESS.2019.2910611
DO - 10.1109/ACCESS.2019.2910611
M3 - 文章
AN - SCOPUS:85065184216
SN - 2169-3536
VL - 7
SP - 49527
EP - 49537
JO - IEEE Access
JF - IEEE Access
M1 - 8689036
ER -