Stacking-based method for predicting remaining useful life of engine room equipment

Abstract

ObjectivesWith reference to the definitions and requirements of intelligent engine rooms in the China Classification Society Rules for Intelligent Ships, this paper studies methods for predicting the remaining useful life (RUL) of bearings in order to explore prognostic and health management technologies.MethodsAddressing the poor prediction accuracy of conventional data-driven methods, this study uses the Stacking fusion strategy in integrated learning to construct an R-A-X (Ridge-ANN-XGBoost, with XGBoost and ANN as the base learner, and ridge regression as the meta learner) fusion model. It then designs a prediction performance comparison experiment using the life cycle data in the IEEE PHM 2012 Prognostic Challenge under the same working conditions, with MAE and R2 used as performance evaluation indicators to compare the R-A-X fusion model with the single algorithm and average.ResultsThe results show that the prediction performance of the R-A-X fusion model are better than those of the other methods involved in this article, with an improvement effect reaching up to 20%.ConclusionsThe proposed method can improve the accuracy of bearing RUL prediction and has certain reference value for the realization of the equipment health management of intelligent engine rooms.

Keywords

• intelligent engine room
• remaining useful life prediction
• bearing
• ensemble learning
• stacking