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

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.

An Adaptive Multi-Sensor Fault Diagnosis Method for High-Speed Train Traction Converters

2020 ◽  
pp. 1-1
Author(s):  
Honghui Dong ◽  
Fuzhao Chen ◽  
zhipeng wang ◽  
Limin Jia ◽  
Yong Qin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Sensor Fault ◽  
Diagnosis Method

A Fault Diagnosis Method for the Roller-Marks in Offset Printing Machine Based on Texture Recognition

2012 ◽  
Vol 262 ◽  
pp. 361-366
Author(s):  
Zhuo Fei Xu ◽  
Hai Yan Zhang ◽  
Ling Hui Ren
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Solution Method ◽  
Offset Printing ◽  
Texture Recognition ◽  
Defect Image ◽  
Diagnosis Method ◽  
The Difference ◽  
Detecting Method ◽  
Printing Machine

Roller-mark is a common problem in offset printing and its solution method is important for printing. A new detecting method of texture analysis was given in this paper. In this study, printing image was acquired with high-speed CCD. Compared the difference between printing image and standard image, a defective image was obtained. Then the reason of roller-marks was given by the texture recognition of defect image. Finally, experiments were taken to prove the feasibility and effectiveness of this new method for the roller-marks diagnosis in the offset printing machine.

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

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
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