Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains

2020 ◽  
Author(s):  
Hongtian Chen ◽  
Bin Jiang ◽  
Ningyun Lu ◽  
Wen Chen
Keyword(s):  
High Speed ◽  
Data Driven ◽  
High Speed Trains ◽  
Detection And Diagnosis

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 Data-driven fault diagnosis for dynamic traction systems in high-speed trains

2020 ◽  
Vol 50 (4) ◽  
pp. 496-510
Author(s):  
Hongtian CHEN ◽  
Bin JIANG ◽  
Hui YI ◽  
Ningyun LU
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Data Driven ◽  
High Speed Trains

scholarly journals On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

2019 ◽  
Vol 9 (23) ◽  
pp. 5129 ◽  
Author(s):  
Moussa Hamadache ◽  
Saikat Dutta ◽  
Osama Olaby ◽  
Ramakrishnan Ambur ◽  
Edward Stewart ◽  
...  
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Complex Structure ◽  
Rapid Evolution ◽  
Suitable Condition ◽  
Fault Detection And Diagnosis ◽  
Rail Network ◽  
High Speed Trains ◽  
Detection And Diagnosis ◽  
Mechanical Devices

Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network.

Sign in / Sign up

Export Citation Format

Share Document