支持向量机
希尔伯特-黄变换
预处理器
熵(时间箭头)
主成分分析
人工智能
核主成分分析
计算机科学
振动
模式识别(心理学)
工程类
核(代数)
特征提取
核方法
计算机视觉
数学
物理
组合数学
滤波器(信号处理)
量子力学
作者
Yongkui Sun,Yuan Cao,Peng Li,Peng Li
出处
期刊:IEEE Intelligent Transportation Systems Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:15 (6): 96-108
被引量:1
标识
DOI:10.1109/mits.2023.3295376
摘要
Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics of vibration signals, this article develops a vibration signal-based diagnosis approach. First, variational mode decomposition (VMD) is utilized for nonstationary vibration signal preprocessing, which is verified as a more effective tool than empirical mode decomposition. Then, to comprehensively characterize nonlinear fault characteristics, five kinds of entropy are extracted. To eliminate the redundant information of high-dimensional features, kernel principal component analysis is utilized for multientropy feature fusion. Experiment comparisons demonstrate the superiority of the proposed VMD preprocessing and multientropy fusion method. The diagnosis accuracies of normal-to-reverse and reverse-to-normal switching directions reach 96.57% and 99.43%, respectively, which provides theoretical support for onsite operation and maintenance staff.
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