Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining
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 si...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/7146435 |
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| _version_ | 1832547757895712768 |
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| author | Fan Gao Fan Li Zhifei Wang Wenqi Ge Xinqin Li |
| author_facet | Fan Gao Fan Li Zhifei Wang Wenqi Ge Xinqin Li |
| author_sort | Fan Gao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-12e9f815e34d4b6788726d22fe0e566b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-12e9f815e34d4b6788726d22fe0e566b2025-02-03T06:43:33ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/71464357146435Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text MiningFan Gao0Fan Li1Zhifei Wang2Wenqi Ge3Xinqin Li4Postgraduate Department, China Academy of Railway Science, Beijing 100081, ChinaChina Academy of Railway Sciences Corporation Limited, Beijing 100081, ChinaChina Academy of Railway Sciences Corporation Limited, Beijing 100081, ChinaSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaChina Academy of Railway Sciences Corporation Limited, Beijing 100081, ChinaIn 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.http://dx.doi.org/10.1155/2021/7146435 |
| spellingShingle | Fan Gao Fan Li Zhifei Wang Wenqi Ge Xinqin Li Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining Journal of Electrical and Computer Engineering |
| title | Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining |
| title_full | Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining |
| title_fullStr | Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining |
| title_full_unstemmed | Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining |
| title_short | Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining |
| title_sort | research on multilevel classification of high speed railway signal equipment fault based on text mining |
| url | http://dx.doi.org/10.1155/2021/7146435 |
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