Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

Abstract

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.