Multi-Sensor Information Fusion and Multi-Model Fusion-Based Remaining Useful Life Prediction of Fan Slewing Bearings with the Nonlinear Wiener Process

Abstract

Many factors affect the accuracy of the estimation of the remaining useful life (RUL) of the fan slewing bearings, thereby limiting the sustainable development of the wind power industry. More specifically, the traditional vibration data, which are easily disturbed by noises, cannot comprehensively characterize the health status; thus, the physical model is difficult to establish, and when the data-driven model analyzes the status, it results in unclear physical mechanisms. A new nonlinear Wiener degradation model was established based on the fusion of the physical models and the data-driven models, which was employed to characterize the degradation process of the slewing bearings in this work, and for the local temperature distribution, which has a strong anti-interference ability, the multi-sensor temperature data fusion was selected to analyze the RUL prediction. First, the multi-sensor temperature data were fused by performing a principal component analysis (PCA) to obtain the comprehensive health index (CHI), which represents the fan slewing bearings. Second, the Arrhenius Equation, which characterizes the degradation using temperature, was introduced into the nonlinear Wiener model, and a new degradation model was established. Moreover, considering the random change of the drift coefficients and the individual differences, the closed expression of the probability density function (PDF) of RUL was derived. Third, maximum likelihood estimation (MLE) was used to estimate the parameters. In addition, Bayesian analysis was used to update parameters to achieve real-time estimation. The results demonstrated that the proposed method can be used to significantly improve the fitting degree of the model and the accuracy of RUL estimation.