Prediction of Ride Comfort of High-Speed Trains Based on Train Seat–Human Body Coupled Dynamics Model

Li, Heng; Zheng, Xu; Dai, Wenqiang; Qiu, Yi

doi:10.3390/app122412900

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The acoustic performance is reflected in the sound field, and it is also measured in the sound field [39]. In order to test the sound insulation performance of structural parts, relevant sound insulation data are collected to support the construction of prediction models and ultimately help enterprises and factories to plan early in the design, improve efficiency, and reduce costs.…”

Section: Sound Insulation Performance Testmentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

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Section: Sound Insulation Performance Testmentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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show abstract

“…Reference [3] uses the effective values of acceleration in the x, y, and z directions as evaluation indicators. It compares the comfort values in the front, middle, and rear sections of the train at different speeds.…”

Section: Introductionmentioning

confidence: 99%

Research on data-driven evaluation of passenger comfort

Liu,

2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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This paper focuses on the evaluation of passenger comfort in high-speed maglev trains using a data-driven approach. Specifically, the study targets the suspension control system as the object of research. The process begins with preprocessing the vibration acceleration data. Next, five-dimensional features are extracted, and the Jarque-Bera method is employed to test for normal distribution. If the feature values do not conform to a normal distribution, the Box-Cox transformation is applied. Finally, the features are standardized using the Z-score method, and the Euclidean distance is calculated based on a multi-feature fusion approach using hypersphere. The resulting Euclidean distance, referred to as the τ distance, serves as an evaluation metric for passenger comfort. Comparative experiments using real-world data demonstrate that this metric effectively assesses passenger comfort. The findings of this study have practical implications for evaluating the performance of suspension control systems and can be utilized to improve the design and operation of high-speed maglev trains, thereby enhancing the overall passenger experience.

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Analysis of the influence of track irregularity on high-speed train ride comfort

Xiao,

Xu,

Yang

et al. 2023

Vehicle System Dynamics

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Prediction of Ride Comfort of High-Speed Trains Based on Train Seat–Human Body Coupled Dynamics Model

Cited by 4 publications

References 17 publications

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Research on data-driven evaluation of passenger comfort

Analysis of the influence of track irregularity on high-speed train ride comfort

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