电子测量与仪器学报

彭乐乐,陈谢祺,郑树彬,林建辉,钟倩文.列车牵引电机轴承故障特征稀疏在线监测方法[J].电子测量与仪器学报,2023,37(11):109-118

列车牵引电机轴承故障特征稀疏在线监测方法

Sparse on-line monitoring method for rail vehicle tractionmotor bearing fault characteristics

DOI：

中文关键词: 轨道车辆多传感无线监测粒子群多点最优调整的最小熵解卷积压缩感知高阶频率加权能量算子

英文关键词:rail vehicle wireless sensing by multi-sensors and on-line monitoring PSO-MOMED compressed sensing HFWEO

基金项目:国家自然科学基金(51907117,51975347) 、上海市科技计划项目(22010501600)资助

作者	单位
彭乐乐	1. 上海工程技术大学城市轨道交通学院
陈谢祺	1. 上海工程技术大学城市轨道交通学院
郑树彬	1. 上海工程技术大学城市轨道交通学院
林建辉	2. 西南交通大学牵引动力国家重点实验室
钟倩文	1. 上海工程技术大学城市轨道交通学院

Author	Institution
Peng Lele	1. School of Urban Rail Transit, Shanghai University of Engineering and Technology
Chen Xieqi	1. School of Urban Rail Transit, Shanghai University of Engineering and Technology
Zheng Shubin	1. School of Urban Rail Transit, Shanghai University of Engineering and Technology
Lin Jianhui	2. State Key Laboratory of Traction Power, Southwest Jiaotong University
Zhong Qianwen	1. School of Urban Rail Transit, Shanghai University of Engineering and Technology

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中文摘要:

列车牵引电机轴承状态多传感数据无线感知及在线监测技术是确保轨道车辆可靠运行的关键技术之一,现有的方法存在数据量大传输困难及特征小数据监测可视化不明显的问题。为此,提出一种列车牵引电机轴承故障特征稀疏在线监测方法, 利用粒子群优化多点最优调整的最小熵解卷积的方法(PSO-MOMED)提取了背景噪声下电机轴承故障特征信号,采用离散余弦变换的压缩感知方法实现电机轴承特征的小数量多传感器采集,基于高阶频率加权能量算子(HFWEO)增强轴承故障特征稀疏可视化,并通过搭建试验台及某线路现场实测验证了所提方法的有效性。实验结果表明,信噪比为-10 dB 时,相比传统方法,粒子群优化多点最优调整的最小熵解卷积方法可以更加有效的提取故障特征信号;在压缩率 90%的情况下,从牵引电机轴承故障特征稀疏感知信号中能清晰表征轴承故障特征频率成分,有效解决了列车牵引电机轴承状态多传感数据无线感知及在线监测技术难题。

英文摘要:

The wireless sensing by multi-sensors and online monitoring technology for rail vehicle traction motor bearing is one of the key technologies to ensure the reliable operation. However, existing methods suffer from the problems of large data volume transmission difficulties and visualization of small characteristic data is not obvious. Therefore, sparse online monitoring method for rail vehicle traction motor bearing fault characteristics is proposed in this paper. The minimum entropy deconvolution method of particle swarm optimization multi-point optimal adjustment ( PSO-MOMED) is used to extract the fault characteristic signal of motor bearing under background noise. The method of discrete cosine transform (DCT) compression sensing is used to acquire the bearing characteristics with a small number of multi-sensors. Meanwhile, sparse visualization of bearing fault features is enhanced based on high order frequency weighted energy operator (HFWEO). Furthermore, the effectiveness of the proposed method is verified by setting up a test bench and measuring a line on site. The experimental results show that when the signal-to-noise ratio is -10 dB, PSO-MOMED method can extract fault characteristic signals more effectively than the traditional method. In the case of 90% compression, the frequency components of bearing fault features can be clearly characterized from the sparse perception signals of traction motor bearing fault features. It effectively

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