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1-10 of 72 results for High-speed train bearings

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Select article: Vibration analysis of the axle bearings considering the combined errors for a high-speed train

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Research article

First published 2020-9

Vibration analysis of the axle bearings considering the combined errors for a high-speed train

Jing Liu, Xinbin Li, Wennian Yu

Proceedings of the Institution of Mechanical Engineers,Part K: Journal of Multi-body Dynamics

Select article: Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

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Research article

First published 2025-11

Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

Defu Han, Hongyuan Qi, ShuangXin Wang [...]

Structural Health Monitoring

Select article: A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

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Research article

First published 2023-11

A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

Hongyuan Qi, Defu Han, Dongming Hou [...]

Structural Health Monitoring

Select article: High-speed train wheel set bearing fault diagnosis and prognostics: evaluation of signal processing methods under multi-source interference

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Research article

First published 2023-7

High-speed train wheel set bearing fault diagnosis and prognostics: evaluation of signal processing methods under multi-source interference

Dongming Hou, Hongyuan Qi, Cuiping Wang [...]

Structural Health Monitoring

Select article: Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

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Research article

First published 2025-3

Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

Limu Qin, Gang Yang, Wen He

Structural Health Monitoring

Select article: Intelligent fault diagnosis of high-speed train axle box bearings under parameter fluctuation working conditions using continuous cohomology-based meta-transfer learning framework

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Research article

First published 2026-1

Intelligent fault diagnosis of high-speed train axle box bearings under parameter fluctuation working conditions using continuous cohomology-based meta-transfer learning framework

Yang Li, Xiangyin Meng, Feiyun Xu [...]

Structural Health Monitoring

Select article: Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

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Research article

First published 2022-7

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

Dongming Hou, Hongyuan Qi, Honglin Luo [...]

Structural Health Monitoring

Select article: Fault diagnosis method for high-speed train axlebox bearings based on the joint driving of bearing dynamics simulation and experimental data

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Research article

First published 2025

Fault diagnosis method for high-speed train axlebox bearings based on the joint driving of bearing dynamics simulation and experimental data

Yanlong Pan, Cai Yi, Xiaokang Liao [...]

Structural Health Monitoring

Select article: Effect of track irregularities of high-speed railways on the thermal characteristics of the traction motor bearing

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Research article

First published 2021-1

Effect of track irregularities of high-speed railways on the thermal characteristics of the traction motor bearing

Tingting Wang, Zhiwei Wang, Dongli Song [...]

Proceedings of the Institution of Mechanical Engineers,Part F: Journal of Rail and Rapid Transit

Select article: Semi-supervised multi-indicator noise filtered optimization for fault diagnosis of high-speed train axle box bearings under noisy labels

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Research article

First published 2026

Semi-supervised multi-indicator noise filtered optimization for fault diagnosis of high-speed train axle box bearings under noisy labels

Guo Zhihao, Zhang Kai, Liang Jiacheng [...]

Structural Health Monitoring

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