Analysis of Metro Car Ride Comfort Based on FBG Measurement and Validation
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Keywords

Metro train
FBG measurement
Model validation
Ride comfort index
Error tracing

DOI

10.26689/jera.v10i5.15280

Submitted : 2026-05-30
Accepted : 2026-06-14
Published : 2026-06-29

Abstract

To accurately evaluate the ride comfort of metro cars, this paper collects and analyzes carbody vibration acceleration on an urban metro line using Fiber Bragg Grating (FBG) measurement technology. Through trend term removal and bandpass filtering preprocessing, effective vibration signals are extracted, and characteristic parameters such as the Sperling ride comfort index and dominant frequencies are calculated. Simultaneously, a SIMPACK vehicle dynamics model is established with American Level 5 track spectrum excitation. Simulation results are compared with measured data to validate model reliability, and the model is then used to provide simulation comparisons for ride comfort analysis. Results show that the vertical ride comfort indices at two measurement points are 1.939 and 1.956, and lateral indices are 2.015 and 2.045, all reaching the “Excellent” level. Relative errors of ride comfort indices between simulation and measurement range from 5.2% to 6.7%, indicating high model credibility. Frequency domain analysis shows that the vertical dominant frequency of the carbody is 1.50 Hz and the lateral dominant frequency is 1.75 Hz, with good consistency between measurement and simulation. This work provides an effective method for metro car ride comfort evaluation based on FBG measurements, and offers quantitative basis for parameter calibration of metro train dynamics models.

References

Zhang C, Kordestani H, Shadabfar M, 2023, A combined review of vibration control strategies for high-speed trains and railway infrastructures: Challenges and solutions. Journal of Low Frequency Noise, Vibration & Active Control, 42(1): 272–291.

Zhai W, 2015, Vehicle-Track Coupled Dynamics, 4th ed. Science Press, Beijing.

High-speed Railway Vibration and Noise Testing Technology, China Railway Publishing House, Beijing, 2018.

Xu L, Gao G, Peng C, et al., 2023, Train structural health monitoring method integrating fiber optic sensing and piezoelectric sensing. Journal of Railway Science and Engineering, 20(7): 2763–2772.

Yang T, Wang G, 2023, Research on load and dynamic stress monitoring system for key structures of high-speed maglev train based on fiber Bragg grating. Rolling Stock, 61(3): 93–97+131.

Xiao X, 2022, Vehicle system dynamics performance analysis based on SIMPACK. Machinery Manufacturing & Automation, 2022(5): 1–5.

Carpio J, et al., 2025, Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments, arXiv, 2025: 2503.21057.

Ding B, Zhao Q, Chen D, et al., 2024, Progress in fiber Bragg grating pressure sensing technology and applications. Acta Optica Sinica, 50(3): 23001801. (in Chinese)

Fan S, Liu X, Yang W, 2025, Comprehensive experimental teaching design of fiber grating sensing. Optical Technique, 51(2): 145–152.

Guo Y, Xiong L, Zhou X, et al., 2022, High-performance fiber Bragg grating inclination sensor for mechanical equipment. Journal of Mechanical Engineering, 58(8): 571–579.

Zhu J, Jiang Y, Xue W, 2024, Method for measuring acceleration using fiber Bragg grating. Optoelectronics·Laser, 35(4): 456–462.

State Administration for Market Regulation of the People’s Republic of China, Standardization Administration of the People’s Republic of China, 2019, GB/T 5599-2019 Railway vehicles – Specification for dynamic performance assessment and test identification, China Standard Press, Beijing.