Remaining Useful Life Estimation of Fan Slewing Bearings in Nonlinear Wiener Process with Random Covariate Effect

Since the degradation process of fan slewing bearings is easily influenced by the external environment, it is difficult to estimate its remaining useful life (RUL) accurately. A nonlinear Wiener degradation model considering the influence of random covariate is established for the prediction of the RUL of fan slewing bearings in this study. Firstly, considering the nonlinear and nonmonotonic properties of the operation process of fan slewing bearings, the degradation model of the nonlinear Wiener process of fan slewing bearings is established. Secondly, the combination of random covariate models and the nonlinear Wiener degradation process is researched. The stress effect which is used as a random covariate is introduced into the nonlinear Wiener degradation model in the form of the additive hazard model. Moreover, the closed expression for the RUL probability density function(PDF) is derived for the random variation of drift coefficients, the individual differences and the random variation of covariates. Thirdly, the maximum likelihood estimation algorithm is used to estimate the RUL parameters depending on the historical degradation data. Finally, the vibration data of fan slewing bearings monitored by sensors are used to verify the effectiveness of the proposed method. The results show that the proposed method can be used to improve the fitting degree of the model and the accuracy of RUL estimation.