Notice of RetractionRemaining useful life prediction of gearbox based on a nonlinear state space model

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.