Railway applications. Braking systems of high speed trains

2015 ◽  
Keyword(s):  
High Speed ◽  
High Speed Trains

Battery Self-Traction System for High-Speed Trains

2020 ◽  
Vol 140 (5) ◽  
pp. 349-355
Author(s):  
Hirokazu Kato ◽  
Kenji Sato
Keyword(s):  
High Speed ◽  
High Speed Trains ◽  
Traction System

Development of Magnesium Alloys for High Speed Trains in China

2016 ◽  
pp. 7-8
Author(s):  
Eric Nyberg ◽  
Jian Peng ◽  
Neale R. Neelameggham
Keyword(s):  
Magnesium Alloys ◽  
High Speed ◽  
High Speed Trains

Iterative Learning Operation Control of High-Speed Trains With Adhesion Dynamics

2021 ◽  
pp. 1-11
Author(s):  
Deqing Huang ◽  
Wanqiu Yang ◽  
Tengfei Huang ◽  
Na Qin ◽  
Yong Chen ◽  
...  
Keyword(s):  
High Speed ◽  
Iterative Learning ◽  
High Speed Trains ◽  
Operation Control

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

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

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