Remaining useful life prediction based on stacking ensemble learning
Remaining Useful Life(RUL)prediction plays a key role in the formulation of equipment maintenance strategies.In the face of variable environment and diverse operating conditions,the performance of a single life pre-diction model fluctuates greatly,and the generalization ability is weak.Aiming at this problem,a Stacking ensemble model integrating multiple dissimilar models was proposed to correct the prediction error of a single model.The state monitoring data was processed by sliding time window to obtain performance degradation data with time series information;with the goal of improving the accuracy and diversity of the model,the types of base learners were de-termined;the Gradient Boosting Decision Tree(GBDT)was used as a meta-learner to integrate the prediction re-sults of the base learner and output the final result.Based on the NASA C-MAPSS dataset,the proposed ensemble model was verified,and the results showed that the prediction accuracy of the Stacking ensemble model was signifi-cantly higher than that of base learners,and it also had obvious advantages compared with other traditional models.
Stacking ensemble modelremaining life predictionsliding time windowensemble learning
万方数据
维普
