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

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.

Health Status Assessment for LCESs Based on Multi-discounted Belief Rule Base

2021 ◽  
pp. 1-1
Author(s):  
Chao Cheng ◽  
Jiuhe Wang ◽  
Hongtian Chen ◽  
Zhijie Zhou ◽  
Wanxiu Teng ◽  
...  
Keyword(s):  
Health Status ◽  
Rule Base ◽  
Health Status Assessment ◽  
Belief Rule Base ◽  
Status Assessment

scholarly journals Circuit Tolerance Design Using Belief Rule Base

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiao-Bin Xu ◽  
Zheng Liu ◽  
Yu-Wang Chen ◽  
Dong-Ling Xu ◽  
Cheng-Lin Wen
Keyword(s):  
High Speed ◽  
Nonlinear Modeling ◽  
Optimization Method ◽  
Quantitative Information ◽  
Rule Base ◽  
Nonlinear Relationship ◽  
Highly Nonlinear ◽  
Belief Rule Base ◽  
Feasibility Region ◽  
Brb System

A belief rule-based (BRB) system provides a generic nonlinear modeling and inference mechanism. It is capable of modeling complex causal relationships by utilizing both quantitative information and qualitative knowledge. In this paper, a BRB system is firstly developed to model the highly nonlinear relationship between circuit component parameters and the performance of the circuit by utilizing available knowledge from circuit simulations and circuit designers. By using rule inference in the BRB system and clustering analysis, the acceptability regions of the component parameters can be separated from the value domains of the component parameters. Using the established nonlinear relationship represented by the BRB system, an optimization method is then proposed to seek the optimal feasibility region in the acceptability regions so that the volume of the tolerance region of the component parameters can be maximized. The effectiveness of the proposed methodology is demonstrated through two typical numerical examples of the nonlinear performance functions with nonconvex and disconnected acceptability regions and high-dimensional input parameters and a real-world application in the parameter design of a track circuit for Chinese high-speed railway.

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