TY - GEN
T1 - Particle filter-based approach to estimate remaining useful life for predictive maintenance
AU - Yang, Chunsheng
AU - Lou, Qingfeng
AU - Liu, Jie
AU - Gou, Hongyu
AU - Bai, Yun
PY - 2015
Y1 - 2015
N2 - Estimation of remaining useful life (RUL) plays a vital role in performing predictive maintenance for complex systems today. However, it still remains a challenge. To address this issue, we propose a Particle filter (PF)-based method to estimate remaining useful life for predictive maintenance by employing PF technique to update the nonlinear predictive models for forecasting system states. In particular, we applied PF techniques to estimate remaining useful life by integrating data-driven modeling techniques in order to effectively perform predictive maintenance. After introducing the PF-based algorithm, the paper presents the implementation along with the experimental results through a case study of Auxiliary Power Unit (APU) starter prognostics. The results demonstrated that the developed method is useful for estimating RUL for predictive maintenance.
AB - Estimation of remaining useful life (RUL) plays a vital role in performing predictive maintenance for complex systems today. However, it still remains a challenge. To address this issue, we propose a Particle filter (PF)-based method to estimate remaining useful life for predictive maintenance by employing PF technique to update the nonlinear predictive models for forecasting system states. In particular, we applied PF techniques to estimate remaining useful life by integrating data-driven modeling techniques in order to effectively perform predictive maintenance. After introducing the PF-based algorithm, the paper presents the implementation along with the experimental results through a case study of Auxiliary Power Unit (APU) starter prognostics. The results demonstrated that the developed method is useful for estimating RUL for predictive maintenance.
KW - complex systems
KW - particle filters
KW - predictive maintenance
KW - remaining useful life
UR - http://handle.uws.edu.au:8081/1959.7/uws:33453
UR - http://www.ieaaie2015.org/main/default.asp
U2 - 10.1007/978-3-319-19066-2_67
DO - 10.1007/978-3-319-19066-2_67
M3 - Conference Paper
SN - 9783319190655
SP - 692
EP - 701
BT - Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015: Proceedings
PB - Springer
T2 - International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems
Y2 - 10 June 2015
ER -