Remaining useful life prediction of nonlinear degradation process based on EKF

被引：1

作者：

Wang, Yubing ^{[1
]}

Xie, Guo ^{[1
]}

Yang, Jing ^{[1
]}

Liu, Yu ^{[1
]}

Hei, Xinhong ^{[1
]}

Gao, Huan ^{[1
]}

Wang, Dan ^{[1
]}

机构：

[1] Xian Univ Technol, Xian 710048, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

基金：

美国国家科学基金会; 国家重点研发计划;

关键词：

PHM; EKF; Remaining life prediction; PROGNOSTICS;

D O I：

10.1109/CCDC52312.2021.9602196

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Remaining useful life prediction has always been the core issue of prediction and health management (PHM), and has become a research hotspot in the field of health management. Aiming at the problem of engine life prediction, based on the data set provided by NASA to simulate the degradation process of aircraft turbofan engine, the Extended Kalman Filter (EKF) algorithm is applied to the engine life prediction process, combined with condition monitoring The remaining life of the engine is predicted by fitting the model. The experimental results show that the proposed prediction method can be effectively used in engine life prediction, and has good prediction effect.

引用

页码：2928 / 2933

页数：6

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