Damage detection for floating-slab track steel-spring based on residual convolutional network

ZHU Sheng-yang; ZHANG Qing-lai; YUAN Zhan-dong; ZHAI Wan-ming

doi:10.19818/j.cnki.1671-1637.2022.02.009

Volume 22 Issue 2

Apr. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(2): 123-135

ZHU Sheng-yang, ZHANG Qing-lai, YUAN Zhan-dong, ZHAI Wan-ming. Damage detection for floating-slab track steel-spring based on residual convolutional network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 123-135. doi: 10.19818/j.cnki.1671-1637.2022.02.009

Citation:

ZHU Sheng-yang, ZHANG Qing-lai, YUAN Zhan-dong, ZHAI Wan-ming. Damage detection for floating-slab track steel-spring based on residual convolutional network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 123-135. doi: 10.19818/j.cnki.1671-1637.2022.02.009

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Damage detection for floating-slab track steel-spring based on residual convolutional network

doi: 10.19818/j.cnki.1671-1637.2022.02.009

State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Natural Science Foundation of China 51978587

National Natural Science Foundation of China 11790283

Independent Project of State Key Laboratory of Traction Power 2019TPL-T16

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Author Bio:
ZHU Sheng-yang(1986-), male, associate professor, PhD, syzhu@swjtu.edu.cn
Received Date: 2021-10-21
Publish Date: 2022-04-25

Abstract

Abstract

As traditional fault diagnosis methods can hardly effectively detect the steel-spring damage of floating-slab track (FST), a damage detection method based on the one-dimensional residual convolutional network was proposed. A vehicle-FST coupled dynamics model was built, and the data sets for the floating-slab vibration response caused by the passing vehicles under various conditions were generated. The residual convolutional network was utilized for the feature extraction and data classification of the vibration response under different damage scenarios to achieve the accurate positioning of damaged steel springs. The detection performance of the residual convolutional network on different sensor deployment schemes were studied. The influence of the complex positional relationship between the damaged steel springs and the sensors on the detection performance was analyzed, and the economic and reliable sensor deployment schemes were optimized and determined. Analysis results reveal that when the sensors are closer to the middle of the floating-slab, better classification accuracy and robustness of the residual convolutional network can be achieved on the data under different damage scenarios. As the number of sensors increases, the detection performance of the method also improves, but the excessive concentration of the sensors in the middle of the floating-slab will not bring about significant improvement on the performance. The damage of steel-springs in the middle of the floating-slab is more difficult to identify than that at the end of the floating-slab. The damage detection method achieves a classification accuracy of 99.11% on the full-coverage deployment scheme, boasting good adaptability to complex and changeable detection scenarios. The classification accuracies of the optimized two-sensor deployment scheme and three-sensor deployment scheme reach 98.23% and 98.96%, respectively. The optimized sensor deployment schemes have good detection performance and keep the adaptability of the damage detection method to complex scenarios. 4 tabs, 16 figs, 30 refs.
- vehicle-track coupled dynamics,
- floating-slab track,
- damage detection,
- convolutional neural network,
- residual learning,
- sensor deployment

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References(30)

References

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Damage detection for floating-slab track steel-spring based on residual convolutional network

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Damage detection for floating-slab track steel-spring based on residual convolutional network

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