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27 September 2024 Research on the identification method of heavy haul railway rail surface injury study based on the fusion of vibration and profile data
Xiangrui Meng, Yongkui Sun, Chenshuo Luo, Fengbin Yang, Yuntong An
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Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132751M (2024) https://doi.org/10.1117/12.3037489
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China
Abstract
Due to the heavy haul rail's large single train capacity and strong impact on the track, the rails of heavy haul rail are prone to injuries, and the accurate detection and identification of injuries is the basis of intelligent operation and maintenance of the rails of heavy haul rail. However, the existing rail surface injury recognition methods mainly based on machine vision have the disadvantages of strong noise and limited field of view. Therefore, this paper proposes a detection method based on the fusion of profile data and vibration signals. The profile data has high accuracy, but it has the disadvantages of low detection rate and low accuracy of a single profile. The vibration signal is easy to measure, but it is easy to be interfered, and the vibration signals of some injuries will reflect the same characteristics. The respective defects are overcome by fusion of the two. The experimental results show that the detection accuracy is effectively improved after the fusion of the two. The vibration signals are extracted and dimensionality reduced using stacked self-coding network, and then input into the classifier to obtain the classification results based on the vibration data. At the same time, the profile data and standard rails are matched, and then the wear sequence is generated by the DTW algorithm, and then the features are extracted by the PCA algorithm and classified to obtain the results based on the profile data. Finally, the two methods are fused at the decision level to obtain a new rail surface injury recognition and detection method based on the fusion of the two.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xiangrui Meng, Yongkui Sun, Chenshuo Luo, Fengbin Yang, and Yuntong An "Research on the identification method of heavy haul railway rail surface injury study based on the fusion of vibration and profile data", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132751M (27 September 2024); https://doi.org/10.1117/12.3037489
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KEYWORDS
Vibration
Injuries
Data modeling
Feature extraction
Matrices
Data fusion
Principal component analysis
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