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

Fan Gao,Wenqi Ge,Xinqin Li,Zhifei Wang,Fan Li,Jit S. Mandeep

doi:10.1155/2021/7146435

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.

Highlights

High-speed railway signal equipment is an important infrastructure to ensure the safety of high-speed railway operation [1]
In smart railway and railway under the construction of big data, it is urgent to study the machine learning algorithm based on text mining to realize the multilevel classification of high-speed railway signal fault equipment
According to the subordination relationship between different levels of signal fault, this paper introduces the idea of multitask cooperative voting; after k-fold cross validation, the k-mode produces multiple prediction results for the same data to vote, and different levels of voting strategies are adopted

Summary

Introduction

High-speed railway signal equipment is an important infrastructure to ensure the safety of high-speed railway operation [1]. The multilevel fault classification model of high-speed railway signal equipment is designed based on the research methods of feature extraction and single-layer classification model of railway safety text. Based on the fault single-layer text classification of high-speed railway signaling equipment based on stacking, by using the cyclic neural network BiGRU and BiLSTM as the first learning device of stacking and using the prediction results of the two neural networks as features to train the combined weighted secondary learning device, the prediction results of the primary learning device are integrated by the secondary learning device. Two neural networks output the prediction probability of each classification label in the Softmax layer, respectively, and pass the combined weight classifier.

The Principle of Multitask Cooperative Voting Based on the Decision Tree

Experimental Verification and Result Analysis

Method BiGRU BiLSTM

Conclusion