Abstract
In response to the problem of imbalanced textual records of urban rail transit faults, this paper proposes a data-driven method for automatically classifying urban rail transit vehicle on-board signal system fault texts. By leveraging the vehicle on-board signal system fault logs of urban rail transit, the pkuseg domain segmenter was trained to identify out-of-vocabulary words and perform Chinese word segmentation. Term Frequency-Inverse Document Frequency (TF-IDF) was employed for feature extraction, transforming the failure texts into word vectors. Adaptive Synthetic Sampling (ADASYN) was utilized for data augmentation to balance the minority class samples. Finally, the Support Vector Machine (SVM) algorithm was applied for fault text classification. Through the analysis of vehicle on-board signal system fault logs from a certain urban rail line spanning from 2016 to 2021, experimental results demonstrate that the proposed model can enhance word segmentation and fault classification performance in the field of rail transit.
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References
Yi, Z.: Analysis of common faults and solutions for CBTC system on-board equipment [J] Res. Urban Rail Transit 24(9), 224–227 (2021). https://doi.org/10.16037/j.1007-869x.2021.09.049. (in Chinese)
You, Y., Yu, F., Xiaoping, W.: Overview of chinese text classification methods [J] J. Netw. Inf. Secur. 5(5), 1–8 (2019). https://doi.org/10.11959/j.issn.2096-109x.2019045. (in Chinese)
Xiaoli, L.: Research on Fault Classification of Railway Signal Equipment Based on Text Mining [D] Lanzhou Jiaotong University, 2020. (in Chinese)
Xiaoxi, H., Niuru, Tao, T.: Subway signal equipment fault text preprocessing based on term and semantic fusion [J]. J. Railway 43(2), 78–85 (2021). https://doi.org/10.3969/j.issn.1001-8360.2021.02.010. (in Chinese)
Mingjun, X., Jianfeng, H., Xiaoxi, H.: Fault diagnosis of urban rail ground signal based on fault log [J]. J. Beijing Jiaotong Univ. 44(5), 27–35 (2020). https://doi.org/10.11860/j.issn.1673-0291.20190138. (in Chinese)
Lianbao, Y., Xiang, S., Xinqin, L. et al.: Research on a text analysis based classification model for high-speed railway turnout faults [J]. China Railway (8), 13–18 (2020). https://doi.org/10.19549/j.issn.1001-683x.2020.08.013. (in Chinese)
Qinghua, Z., Xiaoli, L.: Research on short text classification method for railway signal equipment fault based on MCNN [J]. J. Railway Sci. Eng. 16(11), 2859–2865 (2019). https://doi.org/10.19713/j.cnki.43-1423/u.2019.11.027. (in Chinese)
Wei, J., Zou, K. Eda: Easy data augmentation techniques for boosting performance on text classification tasks [J]. arXiv preprint arXiv:1901.11196 (2019)
Xiaoqin, M., Xiaohe, G., Yufeng, X. et al.: Data enhancement technology for Named-entity recognition [J]. J. East China Normal Univ. (Nat. Sci. Edn.) (5), 14–23 (2021). https://doi.org/10.3969/j.issn.1000-5641.2021.05.002. (in Chinese)
Lianbao, Y., Ping, L., Rui, X. et al.: Intelligent classification of railway signal equipment faults based on unbalanced text data mining [J]. J. Railway 40(2), 59–66 (2018). https://doi.org/10.3969/j.issn.1001-8360.2018.02.009. (in Chinese)
Yang, Z., Tianhua, X.: Fault diagnosis of on-board equipment of high-speed railway signal system based on Text mining [J]. J. Railway (8), 53–59 (2015). https://doi.org/10.3969/j.issn.1001-8360.2015.08.009. (in Chinese)
Xinqin, L., Pengxiang, Z., Tianyun, S. et al.: A fault diagnosis method for high speed railway signal equipment based on deep learning integration [J]. J. Railway 42(12), 97–105 (2020). https://doi.org/10.3969/j.issn.1001-8360.2020.12-013. (in Chinese)
Shi, L., Zhu, Y., Zhang, Y., et al.: Fault diagnosis of signal equipment on the Lanzhou-Xinjiang high-speed railway using machine learning for natural language processing [J]. Complexity 2021(8), 1–13 (2021). https://doi.org/10.1155/2021/9126745
Yujin, L., Qian, W.: Failure analysis of communication connection interruption of on-board equipment of Guangzhou Metro Line 6 [J]. Modern Urban Rail Transit (5), 68–74 (2019). (in Chinese)
Luo, R., Xu, J., Zhang, Y., et al.: PKUSEG: a toolkit for multi-domain Chinese word segmentation (2019). https://doi.org/10.48550/arXiv.1906.11455
Hong, C., Jianzhi, Z., Chenglong, X. et al.: Research on improving the intrusion detection model of ADASYN-SDA [J]. Comput. Eng. Appl. 56(2), 97–105 (2020). https://doi.org/10.3778/j.issn.1002-8331.1811-0013. (in Chinese)
Cortes, C., Vapnik, V.: Support-vector networks [J]. Mach. Learn. 20(3), 273–297 (1995)
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Xi, Q., Dai, S. (2024). Data-Driven Fault Text Classification of Urban Rail Transit Vehicle On-Board Signal System. In: Gong, M., Jia, L., Qin, Y., Yang, J., Liu, Z., An, M. (eds) Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023. EITRT 2023. Lecture Notes in Electrical Engineering, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-99-9319-2_30
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DOI: https://doi.org/10.1007/978-981-99-9319-2_30
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