The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Case study and implications for AI-powered predictive maintenance in the railroad industry

철도산업에서 AI기반 예측 유지보수를 위한 사례 연구 및 시사점

  • Eun-Kyung Park (Dept. of Railway Operations and Electrical Signaling, DongYang University)
  • 박은경 (동양대학교 철도운전 전기신호학과)
  • Received : 2024.06.08
  • Accepted : 2024.08.12
  • Published : 2024.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to analyze the concept and application of AI-based predictive maintenance in the railroad industry and draw implications from it. Focusing on the adoption of AI-based maintenance systems by the Korea Railroad Corporation and Seoul Metro, we examined how AI technology can improve the efficiency and safety of railroad operations. We also compared and analyzed the application of AI technology in the European railroad industry through the cases of Deutsche Bahn in Germany and SNCF in France. The study found that AI-powered predictive maintenance contributes to reducing the frequency of breakdowns, reducing maintenance costs, and increasing the reliability of railroad operations.

본 연구는 철도 산업에서 AI 기반 예측 유지보수의 개념과 적용 사례를 분석하고, 이를 통해 얻을 수 있는 시사점을 도출하는 것을 목적으로 한다. 한국철도공사와 서울교통공사의 AI 기반 유지보수 시스템 도입 사례를 중심으로, AI 기술이 철도 운영의 효율성과 안전성을 어떻게 향상시키는지 살펴보았다. 또한, 독일의 Deutsche Bahn과 프랑스 SNCF의 사례를 통해 유럽 철도 산업에서의 AI 기술 적용 현황을 비교 분석하였다. 연구 결과, AI 기반 예측 유지보수는 고장 발생 빈도를 줄이고, 유지보수 비용을 절감하며, 철도 운영의 신뢰성을 높이는 데 기여하는 것으로 나타났다.

Keywords

Acknowledgement

이 논문은 2022년도 동양대학교 학술연구비의 지원으로 수행되었음.

References

  1. S. Ahn, "Smart predictive maintenance system for urban railway vehicle using artificial intelligence solution." Doctoral Dissertation, Dongyang University Graduate Department of Electrical & Communication Engineering, 2019.
  2. KAIA, "Condition-based Smart Maintenance Core Technology Development Final Report," R&D Report, 2018.
  3. E. Park, "Research on Data-driven Railway Risk Assessment Criteria" J. of the Korea Institute of Electronic Communication Sciences, vol. 18, no. 4, 2023, pp. 555-562. https://doi.org/10.13067/JKIECS.2023.18.4.555
  4. KAIA, "Rolling stock major component defects Early detection modules and operating technology for vehicles and ground Development Final Report," R&D Report, 2020.
  5. KAIA, "Final Report on Smart Railway Safety System Technology Development Project" R&D Report, 2019.
  6. S. Go, "Effects of Smart Maintenance System for Railway Vehicles.," Doctoral Dissertation, Seoul National University of Science and Technology Graduate Department of Railway Management and Policy, 2022
  7. E. Park, "Information Security Strategy by Risk Factors based on Smart Railway Communications" J. of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 4, 2022, pp. 695-702. https://doi.org/10.13067/JKIECS.2022.17.4.695
  8. J. Jeong and J. Seo, "A Study of Establishment and Operation Management of a Data-Based Risk Analysis System - Focusing on the Case of Korail," J. of the Korean Society for Urban Railway, vol. 10, no. 1, 2022, pp. 1219-1230. https://doi.org/10.24284/JKOSUR.2022.03.10.1.1219
  9. S. Kim, K. Choi, "Study on Bird Nest Detection System Using Digital Signal Processing and Artificial Intelligence,," J. of the Korean Society for Railway, vol. 26, no. 12, 2023, pp. 866-875. https://doi.org/10.7782/JKSR.2023.26.12.866
  10. S. Park, H. Han, K. Lee, J. Sa and C. Kim, "A Study on AI Edge System for Detecting Abnormal Behavior in Trains," J. of the Korean Society for Railway, vol. 24, no. 12, 2021. pp. 1,062 - 1,074, 2023. https://doi.org/10.7782/JKSR.2021.24.12.1062
  11. H. Yoon, K. Swo, J. Song and S. Shin, "A study on the functional linkage of urban railway rolling stock's smart maintenance system with the existing management system" J. of Korean Society for Urban Railway, vol. 11, no. 4, 2023, pp. 1577-1586. https://doi.org/10.24284/JKOSUR.2023.12.11.4.1577
  12. UIC, "The journey toward AI-enabled railway companies" International Union of Railways & McKinsey & Company. R&D Report, 2024.
  13. D. Jang, J. Kim, "Two-way Interactive Algorithms Based on Speech and Motion Recognition with Generative AI Technology" J. of the Korea Institute of Electronic Communication Sciences, vol. 19, no. 2, 2024, pp. 397-402. https://doi.org/10.13067/JKIECS.2024.19.2.397
  14. Sensorfy, "Predictive Maintenance in Railway Tracks," 2024.
  15. McKinsey, "The Rail Sector's Changing Maintenance Game," 2024.