Research on Subhealth Diagnosis Method for Resistance of Urban Rail Transit Door System
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ORIGINAL RESEARCH PAPERS
Research on Subhealth Diagnosis Method for Resistance of Urban Rail Transit Door System Sulai Wei1 • Zhixing Xu2 • Jianfei Chen2
•
Xiang Shi2
Received: 26 December 2019 / Revised: 20 August 2020 / Accepted: 1 September 2020 Ó The Author(s) 2020
Abstract The rail vehicle door system is one of the key components of rail vehicles. Its failure rate accounts for more than 30% of vehicle failures. By analyzing early warnings provided by subhealth data from the door system, the efficiency and reliability of their health maintenance can be effectively improved and stable operation of the door system can also be guaranteed. In this paper, earlystage resistance changes in the subhealth state of rail vehicle door systems are considered as the research object. Firstly, the distribution rules for the motor parameters are studied, and the time-domain and normal operating envelope features of the operating motor are extracted. Secondly, subhealth conditions with different resistances are simulated using a test rig, and the experimental data are applied to summarize the rules. According to the subhealth types and the distribution of features, diagnostic rules for subhealth are formulated. To check the possibility of fault diagnosis, a verification using running rail vehicle door system data is carried out in MATLAB. The results reveal that the misdiagnosis rate of resistance subhealth is 0% while the rate of missed diagnoses is 2%. Meanwhile, the diagnostic process based on the established rules is relatively efficient. This method is suitable for application for resistance subhealth diagnosis of urban rail vehicle door systems.
& Jianfei Chen [email protected] 1
Nanjing Metro Construction Co., Ltd, Nanjing, China
2
Nanjing Kangni Mechanical and Electrical Co., Ltd, Nanjing, China
Communicated by Baoming Han.
Keywords Urban rail transit Door system Resistance Feature value Subhealth diagnosis
1 Introduction As one of the key components of rail vehicles, the door system has an important impact on the safe operation of trains. Due to the complexity of their structure and operating environment, many problems caused by passenger squeezing, train vibration, aging of electronic components, and wear of components arise during the operation of rail vehicles [1]. According to statistics, the failure rate of the door system accounts for more than 30% of all rail vehicle failures [2], thus posing a serious threat to train safety and requiring urgent solutions. Plenty of work has been carried out on the problem of frequent failures of rail vehicle door systems; For example, Long et al. [3] collected the angle, speed, and current signals from the door system’s motor to extract their time-domain features, providing a basis for door system fault diagnosis. Han et al. [4] designed an intelligent diagnosis system based on Big Data for a rail vehicle door system. Faults could be effectively classified and diagnosed by applying Big Data analysis and artificial intelligence diagnosis algor
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