Research of Fault Diagnosis Method for New High-speed Railway Single Power Distribution Station Based on Bayesian Network

2021 ◽  
Author(s):  
Hao Wang ◽  
Xiaoqiong He
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Power Distribution ◽  
High Speed ◽  
High Speed Railway ◽  
Diagnosis Method ◽  
Distribution Station ◽  
Single Power

Research on the fault diagnosis method for high-speed loom using rough set and Bayesian network

2020 ◽  
Vol 39 (1) ◽  
pp. 1147-1161
Author(s):  
Yanjun Xiao ◽  
Heng Zhang ◽  
Wei Zhou ◽  
Feng Wan ◽  
Zhaozong Meng
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Rough Set ◽  
High Speed ◽  
Diagnosis Method

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.

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

scholarly journals Fault Diagnosis for Vehicle on-Board Equipments of High-Speed Railway Based on GA-ANFIS Model

2020 ◽  
Author(s):  
Linggang Kong ◽  
Shuo Li ◽  
Xinlong Chen ◽  
Hongyan Qin
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Principal Component ◽  
Weighting Factor ◽  
Anfis Model ◽  
High Speed Railway ◽  
Railway Line ◽  
Diagnosis Rate ◽  
Low Efficiency ◽  
Frequency Weighting

Vehicle on-board equipment is the most important train control equipment in high-speed railways. Due to the low efficiency and accuracy of manual detection, in this paper, we propose an intellectualized fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) network. Firstly, we collect the fault information sheets that are recorded by electrical personnel, using frequency weighting factor and principal component analysis (PCA) to realize the data extraction and dimension reduction; Then, in order to improve the fault diagnosis rate of the model, using genetic algorithm (GA) to optimize the parameters of the ANFIS network; Finally, using the fault data of a high-speed railway line in 2019 to test the model, the optimized ANFIS model can achieve 96% fault diagnosis rate for vehicle on-board equipments, which indicating the method is effective and accurate.

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