Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Research and Application of Fault Prediction Method for High-speed EMU Based on PHM Technology

PHM 기술을 이용한 고속 EMU의 고장 예측 방법 연구 및 적용

  • Wang, Haitao (Division of Information and Communication Convergence Engineering, Mokwon University) ;
  • Min, Byung-Won (Division of Information and Communication Convergence Engineering, Mokwon University)
  • 왕해도 (목원대학교 정보통신융합공학부) ;
  • 민병원 (목원대학교 정보통신융합공학부)
  • Received : 2022.09.12
  • Accepted : 2022.11.08
  • Published : 2022.12.31
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Abstract

In recent years, with the rapid development of large and medium-sized urban rail transit in China, the total operating mileage of high-speed railway and the total number of EMUs(Electric Multiple Units) are rising. The system complexity of high-speed EMU is constantly increasing, which puts forward higher requirements for the safety of equipment and the efficiency of maintenance.At present, the maintenance mode of high-speed EMU in China still adopts the post maintenance method based on planned maintenance and fault maintenance, which leads to insufficient or excessive maintenance, reduces the efficiency of equipment fault handling, and increases the maintenance cost. Based on the intelligent operation and maintenance technology of PHM(prognostics and health management). This thesis builds an integrated PHM platform of "vehicle system-communication system-ground system" by integrating multi-source heterogeneous data of different scenarios of high-speed EMU, and combines the equipment fault mechanism with artificial intelligence algorithms to build a fault prediction model for traction motors of high-speed EMU.Reliable fault prediction and accurate maintenance shall be carried out in advance to ensure safe and efficient operation of high-speed EMU.

최근 중국에서 중대형 도시철도의 급속한 발전으로 고속철도의 총 운행거리와 총 EMU(Electric Multiple Units) 수가 증가하고 있다. 고속 EMU의 시스템 복잡성은 지속적으로 증가하고 있으며, 이는 장비의 안전성과 유지보수의 효율성에 대한 더 높은 요구사항을 제시한다. 현재 중국의 고속 EMU의 유지보수 모드는 여전히 계획적인 유지보수 및 고장보수에 기반한 사후 유지보수 방식을 채택하고 있어 유지보수가 미흡하거나 과도하게 이루어지며, 장비 고장 처리의 효율성을 떨어뜨리고 유지보수 비용을 증가시킨다. PHM(진단 및 예측관리)의 지능형 운영 및 유지관리 기술을 기반으로 합니다. 본 논문은 고속 EMU의 서로 다른 시나리오의 다중 소스 이기종 데이터를 통합하여 "차량 시스템-통신 시스템-지상 시스템"의 통합 PHM 플랫폼을 구축하고, 장비 고장 메커니즘을 인공지능 알고리즘과 결합하여 고속 EMU의 트랙션 모터에 대한 고장 예측 모델을 구축한다. 고속 EMU의 안전하고 효율적인 작동을 보장하기 위해 고장 예측 및 정확한 유지보수를 사전에 수행해야 한다.

Keywords

References

  1. J. Y. Liang, "Research on intelligent diagnosis and fault prediction technology of high-speed trains", Journal of Beijing Jiaotong University, Vol.43, NO.1, pp.63-70, 2019.
  2. B. Liu, J. Shao, H. Lu, Y. Li & M Xie, "Thoughts on the research of fault prediction and health management (PHM) system architecture of EMU", China Railway, pp.1-9, 2022.
  3. Z. Y. Wu, "Application of big data technology in EMU fault warning", Control and information technology, pp.91-96, 2021.
  4. W. T. Hu, "Research on fault prediction and health management of traction motor of multiple units", Lanzhou Jiaotong University, 2021.
  5. D. G. Song and Q. M. Niu, "Research and application of big data PHM system for high-speed EMU", Railway computer application, Vol.27, NO.10, pp.44-48, 2018.
  6. N. Zhe, "Research on fault diagnosis and residual life prediction methods of rolling bearings based on big data technology", Beijing University of chemical technology, 2018.
  7. Z. Y. Wu, "Application of big data technology in EMU fault warning", Control and information technology, pp.91-96, 2021.
  8. K. Zhao, "Design and Implementation of PHM Big Data Architecture for Distributed High speed EMUs", Software, Vol.39, NO.12, pp.90-94, 2018.
  9. C. Z. Chi, S. Y. Feng & J. K. Zhang, "Analysis on development trend of aviation equipment PHM and maintenance support technology", Annual Meeting of China Aviation Industry Technical Equipment Engineering Association 2019, pp.167-171, 2019.
  10. T. Yang, G. K. Yang,, "Vehicle failure rate prediction based on BP neural network", Computer simulation, Vol.26, NO.1, pp.276-271, 2009. https://doi.org/10.3969/j.issn.1006-9348.2009.01.074
  11. Q. M. Niu, "Research on intelligent analysis model of health status of high-speed railway equipment based on PHM technology", Beijing Jiaotong University, 2019.
  12. Y. G. Chi, "Research and application of fault prediction and health management technology for multiple units", Locomotive electric drive, pp.118-124, 2018.
  13. X. Z. Li, "Fault diagnosis method of high-speed railway signal equipment based on deep learning integration", Journal of Railways, Vol.42, NO.12, pp.97-105, 2020.
  14. X. X. Xie and S. H. Dai. "Research on fault diagnosis of uninsulated track circuit based on deep learning", Journal of Railways, Vol.42, NO.6, pp.79-85, 2020.
  15. Y. Li, "Research on the Application of PHM Technology in the Operation and Maintenance of Beijing Zhangjiakou High speed Railway", Railway Transportation and Economy, Vol.44, NO.9, pp.103-111, 2022.
  16. C. X. Li, "Research on PHM fault model and management of EMU", China Academy of Railway Sciences, 2020.