基于机器学习的铁路道岔故障识别

Railway Turnout Fault Recognition Based on Machine Learning

道岔的正常运转是保证列车正常运行的必备条件,传统的道岔故障检测方法主要来源于人的工作经验,根据电流的非正常变化来判别道岔是否发生故障,消耗较多的人力资源与物力资源。为了提升资源的有效利用率,本文运用概率主成分分析法提取数据的主要特征,分别采用支持向量机模型和k近邻模型作为道岔故障分类器,然后使用十折交叉验证法作为模型的评价标准,以达到智能识别铁路道岔故障的目的。

The normal operation of the switch is a necessary condition to ensure the normal operation of the train,traditional turnout fault detection methods are mainly derived from human work experience,it judges whether the turnout is malfunctioning according to the abnormal change of the current,which consumes more human resources and material resources.In order to improve the effective utilization of resources,this paper used the probabilistic principal component analysis method to extract the main characteristics of the data,respectively used the support vector machine model and the k-nearest neighbor model as the turnout fault classifier,and then used the ten-fold cross validation method as the evaluation standard of the model to achieve the purpose of intelligently identifying the railway turnout fault.