High Speed Train Bogie Fault Signal Analysis Based on Wavelet Entropy Feature

Mechanical behavior of high speed trains bogie seriously impact the reliability of the train system. Performance monitoring and fault diagnosis for the critical component on bogie are very important. Simulation data of high speed train bogie fault signal is selected in data experiment. Based on multiresolution analysis, wavelet entropy features are extracted to reflect the uncertainty level of the vibration signal on scales. In the high dimension space composed by several wavelet entropy features, the dates from four fault patterns are classified and the result is satisfactory. Result show that wavelet entropy feature is effective for fault signal analysis of high speed train bogie.

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Comparison of Vibration Signal Analysis Methods for Effective Gear Fault Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.709.456 ◽

2014 ◽

Vol 709 ◽

pp. 456-459

Author(s):

Woong Yong Lee ◽

Dong Hyong Lee ◽

Hae Young Ji

Keyword(s):

Signal Analysis ◽

High Speed ◽

Vibration Signal ◽

Spur Gear ◽

High Speed Train ◽

Crest Factor ◽

Gear Fault ◽

Specimen Test ◽

Vibration Signal Analysis ◽

Crack Condition

Reduction unit for high-speed train is an important component. However if faults of reduction unit occurred, the damages such as material and human damage have been caused. To prevent the damage, it is necessary to study reduction unit monitoring for high-speed train. We conducted spur gear specimen test which was crack, breakage and pitting tests and analyzed FFT, Sideband energy ratio (SER), RMS, crest factor, and kurtosis. There was not distinct difference between no-fault and pitting condition at RMS, crest factor and kurtosis. But SER increased depending on crack condition. In breakage test, all parameters had difference between no-fault and breakage condition.

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Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks

Journal of Vibroengineering ◽

10.21595/jve.2017.17238 ◽

2017 ◽

Vol 19 (4) ◽

pp. 2456-2474 ◽

Cited By ~ 13

Author(s):

Jian Ming Yan ◽

Yang Zhao ◽

Zheng Hong Guo

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Signal Analysis ◽

High Speed ◽

Deep Neural Networks ◽

Vibration Signal ◽

High Speed Train ◽

Vibration Signal Analysis

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

Structural Health Monitoring ◽

10.1177/14759217211036025 ◽

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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A Study on the Energy-Harvesting Device with a Magnetic Spring for Improved Durability in High-Speed Trains

Micromachines ◽

10.3390/mi12070830 ◽

2021 ◽

Vol 12 (7) ◽

pp. 830

Author(s):

Jaehoon Kim

Keyword(s):

Energy Harvesting ◽

High Speed ◽

Permanent Magnets ◽

Critical Issue ◽

Sensor Nodes ◽

High Speed Train ◽

Vibration Energy Harvesting ◽

Magnetic Spring ◽

High Speed Trains ◽

Energy Harvesting Devices

Durability is a critical issue concerning energy-harvesting devices. Despite the energy-harvesting device’s excellent performance, moving components, such as the metal spring, can be damaged during operation. To solve the durability problem of the metal spring in a vibration-energy-harvesting (VEH) device, this study applied a non-contact magnetic spring to a VEH device using the repulsive force of permanent magnets. A laboratory experiment was conducted to determine the potential energy-harvesting power using the magnetic spring VEH device. In addition, the characteristics of the generated power were studied using the magnetic spring VEH device in a high-speed train traveling at 300 km/h. Through the high-speed train experiment, the power generated by both the metal spring VEH device and magnetic spring VEH device was measured, and the performance characteristics required for a power source for wireless sensor nodes in high-speed trains are discussed.

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Harvesting Wind Energy by a Triboelectric Nanogenerator for an Intelligent High-Speed Train System

ACS Energy Letters ◽

10.1021/acsenergylett.1c00368 ◽

2021 ◽

pp. 1490-1499

Author(s):

Chuguo Zhang ◽

Yuebo Liu ◽

Baofeng Zhang ◽

Ou Yang ◽

Wei Yuan ◽

...

Keyword(s):

Wind Energy ◽

High Speed ◽

Triboelectric Nanogenerator ◽

High Speed Train ◽

Train System

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The Aerodynamic Characteristics of the Pantograph When the Train Passes Through the Tunnel

Volume 1B, Symposia: Fluid Measurement and Instrumentation; Fluid Dynamics of Wind Energy; Renewable and Sustainable Energy Conversion; Energy and Process Engineering; Microfluidics and Nanofluidics; Development and Applications in Computational Fluid Dynamics; DNS/LES and Hybrid RANS/LES Methods ◽

10.1115/fedsm2017-69171 ◽

2017 ◽

Author(s):

Dilong Guo ◽

Wen Liu ◽

Junhao Song ◽

Ye Zhang ◽

Guowei Yang

Keyword(s):

High Speed ◽

Aerodynamic Force ◽

Maximum Amplitude ◽

Aerodynamic Characteristics ◽

High Speed Train ◽

Expansion Waves ◽

Compression And Expansion ◽

High Speed Trains ◽

Current Characteristics ◽

Pass Through

The aerodynamic force acting on the pantograph by the airflow is obviously unsteady and has a certain vibration frequency and amplitude, while the high-speed train passes through the tunnel. In addition to the unsteady behavior in the open-air operation, the compressive and expansion waves in the tunnel will be generated due to the influence of the blocking ratio. The propagation of the compression and expansion waves in the tunnel will affect the pantograph pressure distribution and cause the pantograph stress state to change significantly, which affects the current characteristics of the pantograph. In this paper, the aerodynamic force of the pantograph is studied with the method of the IDDES combined with overset grid technique when high speed train passes through the tunnel. The results show that the aerodynamic force of the pantograph is subjected to violent oscillations when the pantograph passes through the tunnel, especially at the entrance of the tunnel, the exit of the tunnel and the expansion wave passing through the pantograph. The changes of the pantograph aerodynamic force can reach a maximum amplitude of 106%. When high-speed trains pass through tunnels at different speeds, the aerodynamic coefficients of the pantographs are roughly the same.

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