RUL prediction for bivariate degradation process considering individual differences

Remaining useful life (RUL), is essential for prediction and health management. Because of unit-to-unit heterogeneity in degradation, it is intractable to perform precise degradation trajectories of an individual bivariate individual system. First, for predicting the RUL of an individual system, the parameter in the nonlinear transformation path of the degradation model is proposed as an indicator to characterize the heterogeneity of the degradation process, which is used for accurate degradation prediction. Furthermore, a prediction method based on this improved degradation model is presented for the RUL estimation of degrading systems. An improved parameter updating way based on the Bayesian approach is developed for parameter posterior estimation. The proposed prediction method is illustrated and validated through a publicly available GaAs lasers dataset and a practical degrading system case data. The results show that the new method is superior to the conventional method in prediction accuracy.