scholarly journals Health Assessment of High-Speed Train Running Gear System under Complex Working Conditions Based on Data-Driven Model

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Chao Cheng ◽  
Ming Liu ◽  
Bangcheng Zhang ◽  
Xiaojing Yin ◽  
Caixin Fu ◽  
...  
Keyword(s):  
Health Status ◽  
Working Conditions ◽  
High Speed ◽  
Random Noise ◽  
Gear System ◽  
Data Driven ◽  
Health Status Assessment ◽  
High Speed Train ◽  
High Speed Trains ◽  
Status Assessment

It is very important for the normal operation of high-speed trains to assess the health status of the running gear system. In actual working conditions, many unknown interferences and random noises occur during the monitoring process, which cause difficulties in providing an accurate health status assessment of the running gear system. In this paper, a new data-driven model based on a slow feature analysis-support tensor machine (SFA-STM) is proposed to solve the problem of unknown interference and random noise by removing the slow feature with the fastest instantaneous change. First, the relationship between various statuses of the running gear system is analyzed carefully. To remove the random noise and unknown interferences in the running gear systems under complex working conditions and to extract more accurate data features, the SFA method is used to extract the slowest feature to reflect the general trend of system changes in data monitoring of running gear systems of high-speed trains. Second, slowness data were constructed in a tensor form to achieve an accurate health status assessment using the STM. Finally, actual monitoring data from a running gear system from a high-speed train was used as an example to verify the effectiveness and accuracy of the model, and it was compared with traditional models. The maximum sum of squared resist (SSR) value was reduced by 16 points, indicating that the SFA-STM method has the higher assessment accuracy.

scholarly journals Health Status Prediction Based on Belief Rule Base for High-Speed Train Running Gear System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 4145-4159 ◽  
Author(s):  
Chao Cheng ◽  
Jiuhe Wang ◽  
Wanxiu Teng ◽  
Mingliang Gao ◽  
Bangcheng Zhang ◽  
...  
Keyword(s):  
Health Status ◽  
High Speed ◽  
Gear System ◽  
Rule Base ◽  
High Speed Train ◽  
Belief Rule Base

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

Micromachines ◽  
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.

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

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.

scholarly journals THE COMPREHENSIVE HEALTH STATUS ASSESSMENT OF URBAN ANDRURAL SCHOOLCHILDREN OF MONGOLIA

2017 ◽  
pp. 39-41
Author(s):  
I.G. Pogorelova ◽  
G. Amgalan
Keyword(s):  
Health Status ◽  
School Children ◽  
Rural School ◽  
Medical Examination ◽  
Educational Institutions ◽  
Health Status Assessment ◽  
Number Of Children ◽  
Study Results ◽  
Comprehensive Health ◽  
Status Assessment

In this article presentsthe key findings of health status assessments of urban and rural school children aged 7–16 years based on the materials of comprehensive medical examination and statistical reporting in dynamics 2010–2014. Based on the study results were determined the health status groups and leading causes of morbidity among surveyed school children studying in urban and rural educational institutions of Mongolia. Study results showed that the number of children classified in third group of health was increased with the age of students and incidence of diseases of respiratory, digestive, neurological systems, and diseases of ear nose thought and vision organs were more common among the urban and rural school children of Mongolia.

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