Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan
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引用次数: 5

Abstract

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.
基于D-S证据理论的轻轨车辆悬架系统故障隔离及改进应用案例
提出了一种基于Dempster-Shafer (D-S)证据理论的轻轨车辆悬架系统故障隔离创新方法及其改进应用实例。考虑的LRV有三个机车车辆,每个机车车辆配备三个传感器用于监测悬挂系统。利用卡尔曼滤波产生残差进行故障诊断。为了实现故障隔离,预先建立了故障特征库。应用Eros和新发生故障的故障特征与特征库中的故障特征之间的范数距离来度量特征的相似度,这是对故障进行基本信念赋值的基础。在得到基本信念赋值后,利用D-S证据理论对其进行融合。基本信念赋值的融合显著提高了分离精度。通过两个实例验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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