November 7, 2025Transportation Safety and EnvironmentOpen Access

Heavy-haul rail surface defect identification based on multi-sensor data fusion

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Authors

XWXiyun Wang YSYongkui Sun YCYuan Cao

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Implication

This analysis employs dynamic time warping and support vector machine to enhance defect identification in rail surfaces, suggesting improved safety and efficiency.

Key Points

High accuracy in defect identification at 96.4%, improving rail safety and operational efficiency.
Dynamic time warping used for extracting geometric features from rail profile data.
Employing support vector machine for effective identification of rail surface defects.
The multi-sensor data fusion approach indicates significant advantages in rail maintenance.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/69253a39c0ce034ddc357d68 https://doi.org/10.1093/tse/tdaf063

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Implication

This analysis employs dynamic time warping and support vector machine to enhance defect identification in rail surfaces, suggesting improved safety and efficiency.

Heavy-haul rail surface defect identification based on multi-sensor data fusion

Discussion

Member takes

Implication

Key Points

Cite This Study

Heavy-haul rail surface defect identification based on multi-sensor data fusion

Discussion

Member takes

Implication

Key Points

Cite This Study

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