The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

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外文摘要：The pressure data of the train air braking system is of great significance to accurately evaluate its op-eration state.In order to overcome the influence of sensor fault on the pressure data of train air braking system,it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault,the system can accurately identify and locate the position of the faulty sensor,and estimate the fault data ac-cording to other normal data.A fault-tolerant mechanism based on multi-classification support vector machine(SVM)and adaptive network-based fuzzy inference system(ANFIS)is introduced.Multi-classification SVM is used to identify and locate the system fault state,and ANFIS is used to estimate the real data of the fault sensor.After estimation,the system will compare the real data of the fault sensor with the ANFIS estimated data.If it is similar,the system will recognize that there is a false alarm and record it.Then the paper tests the whole mechanism based on the real data.The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

外文关键词：

Multi-classification support vector machineAdaptive network-based fuzzy inference systemTrain air braking systemFault-tolerant votingMulti-sensors

作者：

Juhan WANG、Ying GAO、Yuan CAO、Tao TANG

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作者单位：

School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,China

Graduate Department,China Academy of Railway Science,Beijing 100044,China

National Engineering Research Center of Rail Transportation Operation and Control System,Beijing Jiaotong University,Beijing 100044,China

基金：

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

2021YFF0501102U19342195202201052202392

出版年：

2024

DOI：

10.23919/cje.2021.00.428

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(1)

参考文献量26