Evaluation of subway vibration reduction effect based on ultra-weak FBG array using deep forest

Jiahui Chen; Fang Liu; Biao Xu; Honghai Wang; Lina Yue

doi:10.1117/12.2662716

16 December 2022 Evaluation of subway vibration reduction effect based on ultra-weak FBG array using deep forest

Jiahui Chen, Fang Liu, Biao Xu, Honghai Wang, Lina Yue

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125002F (2022) https://doi.org/10.1117/12.2662716
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

Monitoring the train vibration is an important issue in subway safety management and maintenance. Aiming at the problems of traditional technology for detection and acquisition of subway vibration, such as unable to achieve dynamic detection or evaluation, and unable to give early warning to the changes of subway tunnel structure, this paper proposes a method to obtain and predict the vibration reduction effect based on grating array. The method uses short-time power spectral density (PSD) to extract train vibration signal and uses Z-vibration level (VLz) to obtain the vibration reduction effect of subway track. A model based on deep forest (DF) is improved to predict the variation trend of vibration reduction effect. The experimental results on the actual train data illustrate that the proposed method can accurately extract the train vibration signal, and the model can effectively forecast the vibration reduction effect, which has a lower error precision and shows improved performance over other prediction models.

Citation Download Citation

Jiahui Chen, Fang Liu, Biao Xu, Honghai Wang, and Lina Yue "Evaluation of subway vibration reduction effect based on ultra-weak FBG array using deep forest", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125002F (16 December 2022); https://doi.org/10.1117/12.2662716

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