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

针对单一机器学习模型在预测设备故障率的应用场景中存在误差大、精度低的问题,提出一种基于改进Stacking融合模型对铁路信号设备进行故障率预测的方法。采用XGBoost、LightGBM、CatBoost和逻辑回归方法构建基本Stacking模型,在此基础上引入Prophet时间序列预测模型,将Prophet模型提取的时序特征与基本Stacking模型逐级融合,构建改进后的Stacking-Prophet预测模型。最后以某单位信号设备数据为例,验证预测模型有效性。实验结果表明,相较单一预测模型,Stacking-Prophet预测模型均方根误差(RMSE)平均降低了94.14%,预测精度有较大的提升,对设备运维有一定的参考价值。

To address the problems oflarge errors and low accuracy with single machine learning models 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,CatBoost and the logistic regression model.On this basis,the Prophet time series prediction model is introduced,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.