Guidance and evaluation system for smooth operation of heavy-haul trains

The operation rules and methods for heavy-haul trains were studied and summarized according to the characteristics of the Daqin Railway, such as a large traffic volume, a high density and high-speed and difficult-to-operate heavy-haul trains. Combined with traction calculation and operation experience, these can be quantificationally decomposed into an evaluation standard for the smooth modularized operation of heavy-haul trains that can be recognized by computers. A train operation guidance system was designed to collect locomotive drivers' operation data, display the actual operation and standard curves in real time and give voice prompts and violation-operation alarms for safety-critical operation. In addition, software for operation analysis and evaluation was developed according to the quantified smooth operation standard. The smooth operation of heavy-haul trains was evaluated and statistically analysed through a comparative analysis of the actual operation records. Moreover, a train impact force detection device capable of monitoring the three-dimensional impact force of heavy-haul trains in real time was developed. Meanwhile, the evaluation standard for smooth operation was verified and optimized by real-time monitoring of the impact force of heavy-haul trains. Finally, on the basis of the above studies, a complete closed-loop management scheme for the smooth operation of heavy-haul trains was constructed, and the objectives of optimizing train operation strategy, standardizing drivers' operations and ensuring the smooth operation of trains were realized through application.

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  • Supplemental Notes:
    • © 2020 Zhi Zhang et al. Published by Oxford University Press on behalf of Central South University Press.
  • Authors:
    • Zhang, Zhi
    • Wei, Xiang
    • Zhang, Tao
    • Wang, Songxu
  • Publication Date: 2020-9

Language

  • English

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  • Accession Number: 01837836
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 28 2022 9:42AM