DocumentCode
3442554
Title
Notice of Retraction
Remaining useful life prediction of gearbox based on a nonlinear state space model
Author
Guoyu Lin ; Yunxian Jia ; Lei Sun ; Xin Liu ; Wenquan Zhang
Author_Institution
Ordnance Eng. Colleges, Shijiazhuang, China
fYear
2013
fDate
15-18 July 2013
Firstpage
1819
Lastpage
1822
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
To solve remaining useful life prediction problems of nonlinear and non-stationary process of components, a data-driven approach is presented. The approach constructs a state space model (SSM) to describe degradation evolution process; uses extend Kalman filter to estimate state distribution in SSM and take the Expectation-Maximization (EM) algorithm to update parameters. Based on the measured data, the time to reach the critical value is determined by estimating the distribution of the remaining useful life by using the estimated nonlinear model. Finally taking gearbox as an example, the results show the approach accurately estimating remaining useful life (RUL) of a gearbox.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
To solve remaining useful life prediction problems of nonlinear and non-stationary process of components, a data-driven approach is presented. The approach constructs a state space model (SSM) to describe degradation evolution process; uses extend Kalman filter to estimate state distribution in SSM and take the Expectation-Maximization (EM) algorithm to update parameters. Based on the measured data, the time to reach the critical value is determined by estimating the distribution of the remaining useful life by using the estimated nonlinear model. Finally taking gearbox as an example, the results show the approach accurately estimating remaining useful life (RUL) of a gearbox.
Keywords
Kalman filters; expectation-maximisation algorithm; gears; mechanical engineering computing; nonlinear filters; remaining life assessment; EM algorithm; RUL estimation; SSM; data-driven approach; degradation evolution process; estimated nonlinear model; expectation-maximization algorithm; extend Kalman filter; gearbox; nonlinear component process; nonlinear state space model; nonstationary component process; remaining useful life prediction; state distribution estimation; Estimation; Kalman filters; Mathematical model; Prediction algorithms; Predictive models; Time series analysis; Vectors; EM algorithm; extend kalman filtering; remaining useful life prediction; state space model;
fLanguage
English
Publisher
ieee
Conference_Titel
Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-1014-4
Type
conf
DOI
10.1109/QR2MSE.2013.6625930
Filename
6625930
Link To Document