Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing

The Lanzhou-Xinjiang (Lan-Xin) high-speed railway is one of the principal sections of the railway network in western China, and signal equipment is of great importance in ensuring the safe and efficient operation of the high-speed railway. Over a long period, in the railway operation and maintenance...

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Main Authors: Lei Shi, Yulin Zhu, Youpeng Zhang, Zhongji Su
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/9126745
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author Lei Shi
Yulin Zhu
Youpeng Zhang
Zhongji Su
author_facet Lei Shi
Yulin Zhu
Youpeng Zhang
Zhongji Su
author_sort Lei Shi
collection DOAJ
description The Lanzhou-Xinjiang (Lan-Xin) high-speed railway is one of the principal sections of the railway network in western China, and signal equipment is of great importance in ensuring the safe and efficient operation of the high-speed railway. Over a long period, in the railway operation and maintenance process, the railway signaling and communications department has recorded a large amount of unstructured text information about equipment faults in the form of natural language. However, due to irregularities in the recording methods of these data, it is difficult to use directly. In this paper, a method based on natural language processing (NLP) was adopted to analyze and classify this information. First, the Latent Dirichlet Allocation (LDA) topic model was used to extract the semantic features of the text, which were then expressed in the corresponding topic feature space. Next, the Support Vector Machine (SVM) algorithm was used to construct a signal equipment fault diagnostic model that reduced the impact of sample data imbalance on the classification accuracy. This was compared and analyzed with the traditional Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. This study used signal equipment failure text data from the Lan-Xin high-speed railway to conduct experimental analysis and verify the effectiveness of the proposed method. Experiments showed that the accuracy of the SVM classification algorithm could reach 0.84 after being combined with the LDA topic model, which verifies that the natural language processing method can effectively realize the fault diagnosis of signal equipment and has certain guiding significance for the maintenance of field signal equipment.
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institution Kabale University
issn 1076-2787
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language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-82488110fcae45149ed1b7109e6199432025-02-03T01:25:01ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/91267459126745Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language ProcessingLei Shi0Yulin Zhu1Youpeng Zhang2Zhongji Su3School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaThe Lanzhou-Xinjiang (Lan-Xin) high-speed railway is one of the principal sections of the railway network in western China, and signal equipment is of great importance in ensuring the safe and efficient operation of the high-speed railway. Over a long period, in the railway operation and maintenance process, the railway signaling and communications department has recorded a large amount of unstructured text information about equipment faults in the form of natural language. However, due to irregularities in the recording methods of these data, it is difficult to use directly. In this paper, a method based on natural language processing (NLP) was adopted to analyze and classify this information. First, the Latent Dirichlet Allocation (LDA) topic model was used to extract the semantic features of the text, which were then expressed in the corresponding topic feature space. Next, the Support Vector Machine (SVM) algorithm was used to construct a signal equipment fault diagnostic model that reduced the impact of sample data imbalance on the classification accuracy. This was compared and analyzed with the traditional Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. This study used signal equipment failure text data from the Lan-Xin high-speed railway to conduct experimental analysis and verify the effectiveness of the proposed method. Experiments showed that the accuracy of the SVM classification algorithm could reach 0.84 after being combined with the LDA topic model, which verifies that the natural language processing method can effectively realize the fault diagnosis of signal equipment and has certain guiding significance for the maintenance of field signal equipment.http://dx.doi.org/10.1155/2021/9126745
spellingShingle Lei Shi
Yulin Zhu
Youpeng Zhang
Zhongji Su
Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing
Complexity
title Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing
title_full Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing
title_fullStr Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing
title_full_unstemmed Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing
title_short Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing
title_sort fault diagnosis of signal equipment on the lanzhou xinjiang high speed railway using machine learning for natural language processing
url http://dx.doi.org/10.1155/2021/9126745
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AT youpengzhang faultdiagnosisofsignalequipmentonthelanzhouxinjianghighspeedrailwayusingmachinelearningfornaturallanguageprocessing
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