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

Abstract

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.