IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v226y2022ics0951832022003246.html
   My bibliography  Save this article

A method of online anomaly perception and failure prediction for high-speed automatic train protection system

Author

Listed:
  • Kang, Renwei
  • Wang, Junfeng
  • Chen, Jianqiu
  • Zhou, Jingjing
  • Pang, Yanzhi
  • Guo, Longlong
  • Cheng, Jianfeng

Abstract

Automatic train protection (ATP) system is the key to ensure the safe operation of high-speed trains. However, the existing operation and maintenance mode for ATP systems cannot diagnose fault in time. In order to improve the protection capability of trains, this paper proposes an online anomaly perception and failure prediction method. First, with real-time operating data, an anomaly perception model based on long short-term memory network is established, where unstructured data are parsed into structured log keys and parameter vectors. It is trained with sequence matrices and its learning performance under different parameters is tested to find the optimal model. Experimental results show that the classification accuracy is 0.981, which is better than the existing methods. Then, with historical data, a failure prediction model based on time series is established, where one-dimensional time series of failure rate are reconstructed to high-dimensional space. The support vector regression method is used to fit the complex functional relationship between phase point and predicted point. And different algorithms are taken to find the optimal parameters. The results show that the model has the strongest generalization ability with the accuracy of 0.987. Finally, the intelligent operation and maintenance data service platform is designed.

Suggested Citation

  • Kang, Renwei & Wang, Junfeng & Chen, Jianqiu & Zhou, Jingjing & Pang, Yanzhi & Guo, Longlong & Cheng, Jianfeng, 2022. "A method of online anomaly perception and failure prediction for high-speed automatic train protection system," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003246
    DOI: 10.1016/j.ress.2022.108699
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022003246
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108699?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lee, Juseong & Mitici, Mihaela, 2020. "An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. BULUT, Merve & ÖZCAN, Evrencan, 2021. "A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Hu, Jiawen & Chen, Piao, 2020. "Predictive maintenance of systems subject to hard failure based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    4. Li, Ruopu & Arzaghi, Ehsan & Abbassi, Rouzbeh & Chen, Diyi & Li, Chunhao & Li, Huanhuan & Xu, Beibei, 2020. "Dynamic maintenance planning of a hydro-turbine in operational life cycle," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    6. Fauriat, William & Zio, Enrico, 2020. "Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    7. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    9. Zhang, Nailong & Si, Wujun, 2020. "Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    10. Wu, Tianyi & Yang, Li & Ma, Xiaobing & Zhang, Zihan & Zhao, Yu, 2020. "Dynamic maintenance strategy with iteratively updated group information," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    11. Wen, Pengfei & Zhao, Shuai & Chen, Shaowei & Li, Yong, 2021. "A generalized remaining useful life prediction method for complex systems based on composite health indicator," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    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. Li, Haoqian & Wang, Yong & Zeng, Jing & Li, Fansong & Yang, Zhenhuan & Mei, Guiming & Ye, Yunguang, 2024. "Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Nie, Songlin & Gao, Jianhang & Ma, Zhonghai & Yin, Fanglong & Ji, Hui, 2023. "An online data-driven approach for performance prediction of electro-hydrostatic actuator with thermal-hydraulic modeling," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

    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. Wang, Jingjing & Qiu, Qingan & Wang, Huanhuan & Lin, Cong, 2021. "Optimal condition-based preventive maintenance policy for balanced systems," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    2. Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Azizi, Fariba & Salari, Nooshin, 2023. "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2024. "Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    8. Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang, 2022. "The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    10. Meissner, Robert & Rahn, Antonia & Wicke, Kai, 2021. "Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    14. Cheng, Yao & Wei, Yian & Liao, Haitao, 2022. "Optimal sampling-based sequential inspection and maintenance plans for a heterogeneous product with competing failure modes," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    15. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Zhang, Wenyu & Zhang, Xiaohong & He, Shuguang & Zhao, Xing & He, Zhen, 2024. "Optimal condition-based maintenance policy for multi-component repairable systems with economic dependence in a finite-horizon," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    17. Xiang, Sheng & Qin, Yi & Liu, Fuqiang & Gryllias, Konstantinos, 2022. "Automatic multi-differential deep learning and its application to machine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    18. Oakley, Jordan L. & Wilson, Kevin J. & Philipson, Pete, 2022. "A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    19. Zhang, Nan & Deng, Yingjun & Liu, Bin & Zhang, Jun, 2023. "Condition-based maintenance for a multi-component system in a dynamic operating environment," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    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:eee:reensy:v:226:y:2022:i:c:s0951832022003246. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.