Remaining Useful Life Prediction for a Catenary, Utilizing Bayesian Optimization of Stacking

Abstract

This article addresses the problem that the remaining useful life (RUL) prediction accuracy for a high-speed rail catenary is not accurate enough, leading to costly and time-consuming periodic planned and reactive maintenance costs. A new method for predicting the RUL of a catenary is proposed based on the Bayesian optimization stacking ensemble learning method. Taking the uplink and downlink catenary data of a high-speed railway line as an example, the preprocessed historical maintenance and maintenance data are input into the integrated prediction model of Bayesian hyperparameter optimization for training, and the root mean square error (RMSE) of the final optimized RUL prediction result is 0.068, with an R-square (R2) of 0.957, and a mean absolute error (MAE) of 0.053. The calculation example results show that the improved stacking ensemble algorithm improves the RMSE by 28.42%, 30.61% and 32.67% when compared with the extreme gradient boosting (XGBoost), support vector machine (SVM) and random forests (RF) algorithms, respectively. The improved accuracy prediction lays the foundation for targeted equipment maintenance and system maintenance performed before the catenary system fails, thus potentially saving both planned and reactive maintenance costs and time.