A Way for Vibration Analysis of High-Speed Train

Cai, Jiufeng; Liu, Jianxin; Guo, Feng; Xie, Ming Xiang

doi:10.1061/9780784482902.071

Cited by 1 publication

(1 citation statement)

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“…Waveform decomposition and multidirectional propagation (Scalea & McNamara, 2004) can be achieved using frequency and spectrum analysis (Wang et al., 2015) or eigenvalue solutions of vibration equations (Antali & Stepan, 2016). Hypotheses have been proposed (Iwnicki et al., 2019), sometimes with coupling between vertical and lateral interactions (Wu & Thompson, 2004), to analyze the mechanisms that form nonlinear phenomena (Cai et al., 2020), understand the vibrations of vehicles or track structures (Thompson, 2008), and guide infrastructure design (Zhao & Wang, 2017) or construction (Li et al., 2011).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity‐oriented ensemble learning for rail monitoring based on vehicle‐body vibration

Zhuang,

Liu,

Tang

2023

Computer aided Civil Eng

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In this study, railway vehicle‐body vibration was applied to rail detection for convenient sensor deployment and cost‐effectiveness. However, the waveform is difficult to analyze due to damping and interference. Data‐driven methods can help concatenate multidimensional signals and complex rail‐surface irregularities but are impressionably uncertain. This study proposes a method in which a deep learning framework is coupled with heterogeneous factors at every link in its ensemble strategy. The instantiation, module foundation, and scenario description establish a concrete system for dealing with the dilemma that an insufficient database hinders feature extraction, causing unproductive capacity. The performance is quantitatively discussed by combining different levels of on‐site rail‐surface conditions, reaching prediction errors of 4.7% and 6.5% and classifier accuracies of 98.4% and 93.7% for irregularities and defect severities, respectively. This work describes a way to extend self‐learner applicability in industry and will facilitate new support for railway track management.

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