摘要
转向架轴承作为地铁车辆的重要组成部分,其故障会对车辆运行安全构成严重威胁。现有的轴承故障诊断方法准确性较低,存在漏诊、误诊的可能。针对这一问题,分析地铁车辆转向架轴承的故障模型,对经典证据融合算法进行改进,提出一种新的均值加权融合法,该方法提取轴承振动信号的特征频率谱峰比值与频率均值,设置对应的概率分配函数来分配上述两种特征参数对不同元素的支持度,并进行证据融合。为了验证所提出方法的有效性,设计搭建了轴承实验平台,对已有的均值K系数法、吸收法及所提出的均值加权融合算法进行了对比实验。实验结果表明,本文提出的均值加权融合算法的漏诊率及误诊率明显小于已有的均值K系数法和吸收法,提高了地铁车辆轴承诊断的准确率,更加适用于地铁车辆转向架轴承的故障诊断。
The failure of bogie bearing, an important part of metro vehicles, will pose a serious threat to the safety of vehicle operation. Due to the low accuracy of the existing fault diagnosis methods, there may be misdiagnosis or missed diagnosis. To address this deficiency, this paper first analyzed the fault model of the metro vehicle bogie bearing. Based on the improvement of the classical evidence fusion algorithms, a new mean weighted fusion algorithm was proposed, which extracted the characteristic frequency spectrum peak ratio and the mean value of frequency of bearing vibration signal. The corresponding probability distribution functions were set to distribute the support degree of these two characteristic parameters to different elements and the evidence was fused. To verify the effectiveness of the proposed method, the bogie bearing experimental platform was designed and constructed. The mean K coefficient algorithm, the Absorption algorithm and the proposed mean weighted fusion algorithm were compared on that platform. The results show that missed diagnosis rate and misdiagnosis rate of the mean weighted fusion algorithm proposed in this paper are significantly lower than those of the other two existing improved algorithms, which improves the accuracy of the metro vehicle bearing diagnosis and is more applicable to the fault detection of metro vehicle bogie bearings.
引文
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