Design and Verification of Random Forest Algorithm for Traction/Braking Fault Classification in Train Operation
Ensuring operational safety of trains is crucial for the sustainable development of rail transit systems,as faults in the traction and braking systems might lead to serious safety issues and operational delays.Precise fault detection can improve safety and diminish the likelihood of accidents.This study aims to improve the accuracy and efficacy of fault classification.Initially,this research conducted a data analysis based on the temporal features of train traction and braking fault datasets.A data resampling logic and feature extraction method were applied to address category imbalance in the raw data,enhancing data accuracy.Comparative experiments demonstrate that,with equivalent data volumes for machine learning and training,the classification performance of each model was improved by over 10%.Subsequently,machine learning models based on SVM and decision trees were established to identify and classify fault types,and the hyperparameter was optimized through grid search.Finally,various ensemble learning approaches based on basic classification algorithms were explored and the designed models were validated through simulation.Among them,the random forest algorithm emerged as the most effective with a test accuracy of 96.12%.Analysis of classification results and confusion matrices validates the effectiveness and reliability of this method,introducing an innovative solution for classifying fault types for train operation.
train traction and brakingfault diagnosissupport vector machinerandom forestensemble learning
NETL
NSTL
万方数据
