断层(地质)
点(几何)
功率(物理)
电压
小波
电流(流体)
电气设备
支持向量机
电力
状态监测
计算机科学
算法
工程类
电气工程
人工智能
数学
地质学
物理
地震学
量子力学
几何学
作者
Tomotsugu Asada,Clive Roberts,Takafumi Koseki
标识
DOI:10.1016/j.trc.2013.01.008
摘要
This paper develops a new approach for fault detection and diagnosis utilising parameters collected from low-cost and accessible sensors. An electrical railway point machine within a railway junction is used as a case study. The paper shows that electrical active power collected from electrical current and electrical voltage sensors can be used for condition monitoring systems. The methodology proposed in this paper utilises Wavelet Transforms and Support Vector Machines. It was found that together these methods can detect and diagnose misalignment faults of electrical railway point machine to a high degree of accuracy. Furthermore, it was proved that the approach can provide an indication of the severity of the faults. This work was carried out in collaboration between the University of Birmingham and Central Japan Railway Company.
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