scholarly journals A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 10278-10293 ◽  
Author(s):  
Dandan Peng ◽  
Zhiliang Liu ◽  
Huan Wang ◽  
Yong Qin ◽  
Limin Jia
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
One Dimensional ◽  
Residual Learning ◽  
High Speed Trains

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.

scholarly journals A Semi-Quantitative Information Based Fault Diagnosis Method for the Running Gears System of High-Speed Trains

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 38168-38178 ◽  
Author(s):  
Chao Cheng ◽  
Xinyu Qiao ◽  
Hao Luo ◽  
Wanxiu Teng ◽  
Mingliang Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Quantitative Information ◽  
High Speed Trains ◽  
Diagnosis Method

scholarly journals Fault Diagnosis for Brake System in High-Speed Trains Using the Phased Features and Multi-layer Perceptron

2019 ◽  
Vol 470 ◽  
pp. 012007 ◽  
Author(s):  
Changzheng Fang ◽  
Xiaoyong Zhang ◽  
Yijun Cheng ◽  
Shengnan Wang ◽  
Li Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Brake System ◽  
Multi Layer Perceptron ◽  
High Speed Trains

Novel Multistate Fault Diagnosis and Location Method for Key Components of High-Speed Trains

2021 ◽  
Vol 68 (4) ◽  
pp. 3537-3547
Author(s):  
Luonan Chang ◽  
Zhen Liu ◽  
Yuan Shen ◽  
Guangjun Zhang
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Location Method ◽  
High Speed Trains

scholarly journals A Tutorial on Hardware-Implemented Fault Injection and Online Fault Diagnosis for High-Speed Trains

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5957
Author(s):  
Xiaoyue Yang ◽  
Xinyu Qiao ◽  
Chao Cheng ◽  
Kai Zhong ◽  
Hongtian Chen
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Fault Injection ◽  
Traction Force ◽  
Electrical Drive ◽  
Performance Indexes ◽  
Integrated Platform ◽  
High Speed Trains ◽  
Safety And Reliability ◽  
Drive Systems

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, where FI is an important way to recur faults, and FD ensures the recurring faults can be successfully detected as soon as possible. In this paper, a tutorial on a hardware-implemented (HIL) platform that blends FI and FD techniques is given for electrical drive systems of high-speed trains. The main contributions of this work are fourfold: (1) An HIL platform is elaborated for realistic simulation of faults, which provides the test and verification environment for FD tasks. (2) Basics of both the static and dynamic FD methods are reviewed, whose purpose is to guide the engineers and researchers. (3) Multiple performance indexes are defined for comprehensively evaluating the FD approaches from the application viewpoints. (4) It is an integrated platform making the FI and FD work together. Finally, a summary of FD research based on the HIL platform is made.

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