机械与电子2024，Vol.42Issue(1)：41-46,6.
基于改进Stacking模型的铁路信号设备故障率预测
Prediction of Railway Signal Equipment Failure Rate Based on Improved Stacking Model
摘要
Abstract
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.关键词
机器学习/融合模型/时间序列/铁路信号设备/故障率预测Key words
machine learning/fusion model/time series/railroad signal equipment/failure rate prediction分类
交通工程引用本文复制引用
袁武民,邢建平,杨栋..基于改进Stacking模型的铁路信号设备故障率预测[J].机械与电子,2024,42(1):41-46,6.基金项目
甘肃省中小企业创新基金项目(22CX3GA029) （22CX3GA029）
