IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p1908-d232381.html
   My bibliography  Save this article

A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data

Author

Listed:
  • Liu Yang

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Keping Li

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Dan Zhao

    (Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education, School of Information, Renmin University of China, Beijing 100872, China)

  • Shuang Gu

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Dongyang Yan

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by using a root cause identification-new word sentence (RCI-NWS) keyword extraction method. The experimental results demonstrate that the extraction of railway fault text data can be performed using the keyword extraction method and the highest values are obtained using RCI-NWS.

Suggested Citation

  • Liu Yang & Keping Li & Dan Zhao & Shuang Gu & Dongyang Yan, 2019. "A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data," Energies, MDPI, vol. 12(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1908-:d:232381
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/1908/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/10/1908/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu Yang & Keping Li & Hangfei Huang, 2018. "A new network model for extracting text keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 339-361, July.
    2. Chao Wen & Zhongcan Li & Javad Lessan & Liping Fu & Ping Huang & Chaozhe Jiang, 2017. "Statistical investigation on train primary delay based on real records: evidence from Wuhan–Guangzhou HSR," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 5(3), pages 170-189, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gu, Shuang & Li, Keping & Feng, Tao & Yan, Dongyang & Liu, Yanyan, 2022. "The prediction of potential risk path in railway traffic events," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xicheng Yin & Hongwei Wang & Pei Yin & Hengmin Zhu & Zhenyu Zhang, 2020. "A co-occurrence based approach of automatic keyword expansion using mass diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1885-1905, September.
    2. Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    3. Tingting Zhang & Baozhen Lee & Qinghua Zhu & Xi Han & Ke Chen, 2023. "Document keyword extraction based on semantic hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2623-2647, May.
    4. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    5. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    6. Chengzhi Zhang & Lei Zhao & Mengyuan Zhao & Yingyi Zhang, 2022. "Enhancing keyphrase extraction from academic articles with their reference information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 703-731, February.
    7. Zhongcan Li & Ping Huang & Chao Wen & Yixiong Tang & Xi Jiang, 2020. "Predictive models for influence of primary delays using high‐speed train operation records," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1198-1212, December.
    8. YiJun Liu & Li Zhang & Xiaoli Lian, 2020. "A document-structure-based complex network model for extracting text keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1765-1791, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1908-:d:232381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.