Liu, J., Huang, Y., Zou, Q., Tian, M., Wang, S., Zhao, X. and Ren, S. (2019) Learning Visual Similarity for Inspecting Defective Railway Fasteners. IEEE Sensors Journal, 19, 6844-6857.
https://doi.org/10.1109/JSEN.2019.2911015

has been cited by the following article:

• TITLE: Establishment and Optimization of Status Assessment Variables for Heavy Haul Railway Line Service Performance

  AUTHORS: Changfan Zhang, Wendong Kong, Zhongmei Wang, Lin Jia, Shou Chen

  KEYWORDS: Set of Variables, Key Variables, Heavy Haul Railway Line, Association Rule, Principal Component Analysis

  JOURNAL NAME: Journal of Transportation Technologies, Vol.13 No.4, October 26, 2023

  ABSTRACT: In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.