利用模拟器的传感器信号,通过机器学习对电动多单元门系统进行故障诊断

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-08-21 DOI:10.1007/s42835-024-02003-6
Gil Hyun Kang, Kyung Sik Kim, Chin Young Chang, Chul Su Kim
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引用次数: 0

摘要

故障诊断和预测对于防止高峰时段因城市轨道交通车辆车门故障造成的交通拥堵非常重要。本文利用在车门模拟器上安装位移传感器收集到的数据集,研究如何通过机器学习提高故障分类性能。首先,通过 147,000 次无故障测试验证了传感器和所开发模拟器的耐用性。在机器学习方面,收集了 11,225 组门的正常和异常数据,并进行了监督学习。为了克服现有压力传感器或声学传感器故障诊断的困难,进行了预处理,将其转换为基于速度的数据。此外,通过测试双区分割方法,将特征提取与单区方法进行了比较。特征选择使用了为降低特征维度而开发的主成分分析算法。结果,使用单区方法的开闭数据分类性能优于双区加速和减速分割方法。在机器学习模型中,LGBM 模型的预测准确率最高,达到 99.55%,有望应用于实际车辆。
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Fault Diagnosis of the Electric Multiple Unit Door System by Machine Learning Using Sensor Signal of the Simulator

Fault diagnosis and prediction are important to prevent traffic congestion during rush hour due to door failures of urban railway vehicles. This paper is a study on improving failure classification performance through machine learning using the data set collected by installing a displacement sensor on a door simulator. First, the durability test of the sensor and the developed simulator was verified through 147,000 no-failure tests. For machine learning, 11,225 sets of normal and abnormal data of the door were collected and supervised learning was performed. In order to overcome the difficulty of fault diagnosis of the existing pressure sensor or acoustic sensor, pre-processing was performed that converted to speed-based data. In addition, feature extraction was compared with the single zone method by testing the 2-zone segmentation method. Feature selection was made using the principal component analysis algorithm developed for feature dimensionality reduction. As a result, the classification performance of the method using the single zone method with open and close data was better than the 2-zone segmentation method by acceleration and deceleration. Among the machine learning models, the LGBM model showed the highest prediction accuracy of 99.55%, which is expected to be applied to actual vehicles.

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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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