Prediction of Railway Signal Equipment Failure Rate Based on Improved Stacking Model
To address the problems oflarge errors and low accuracy with single machine learning mod-els for predicting the failure rate of equipment,a prediction method based on improved Stacking fusion model is proposed.The basic Stacking fusion model is constructed by selecting XGBoost,LightGBM,Cat-Boost and the logistic regression model.On this basis,the Prophet time series prediction model is intro-duced,and the features extracted by the Prophet model are fused with the basic Stacking model level by level to construct the improved Stacking-Prophet prediction model.Finally,the validity of the prediction model is verified by taking the signal equipment data of a unit as an example.The experimental result shows that compared with the single prediction model,the Stacking-Prophet prediction model reduces the root mean square error(RMSE)by 94.14％ on average,and the prediction accuracy is greatly improved.It is of a certain reference value for equipment operation and maintenance.
machine learningfusion modeltime seriesrailroad signal equipmentfailure rate prediction
国家科技期刊平台
NSTL
万方数据
维普
