摘要
为了提升列车的智能化水平与现场检修效率,文中从系统角度出发,针对高速列车牵引传动系统网侧过流的精确故障定位问题,提出一种基于故障时序特征模式识别的实时诊断方法。该方法首先通过机理分析选择故障源集合关联的系统信号,其次,结合案例数据波形与专家经验,挖掘故障源与系统信号的关联规律,提取相关故障特征指标;然后,基于故障特征指标的时序变化特性,利用高斯混合模型与隐层马尔科夫链算法建立列车网侧过流的时序特征辨识的故障诊断模型。最后,应用列车实际运行数据对提出的故障诊断模型进行验证,实验结果表明,所提算法能实现有效的故障检测与隔离功能,具有良好的应用价值。
To improve the intelligence level of trains and the efficiency of on-site maintenance,in this paper,a real-time diagnosis method based on fault time-series feature pattern recognition was proposed from a system perspective to solve the problem of accurate fault location on the line-side of the high-speed train traction drive system.Firstly,the system signals associated with the fault were selected based on mechanism analysis.Secondly,the correlation relationships between the fault source and the system signal were revealed and fault feature indicators were extracted by combining actual case waveform data and expert experience.Then,based on the time-series change characteristics of the fault feature index,the Gaussian mixture models and hidden Markov models(GMM-HMM)algorithm was used to establish a fault diagnosis model for the identification of the time-series feature of the over-current of line-side in traction drive system.Finally,the actual train operation data was used to verify the fault diagnosis model.The test results show that the algorithm proposed can achieve effective fault detection and isolation,and has good application value.
作者
倪强
李学明
刘侃
黄庆
NI Qiang;LI Xueming;LIU Kan;HUANG Qing(School of Automation,Guangdong University of Technology,Guangzhou 510006,Guangdong Province,China;School of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,Hunan Province,China;CRRC Zhuzhou Electric Locomotive Research Institute,Zhuzhou 412001,Hunan Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2022年第11期3963-3974,共12页
Proceedings of the CSEE
基金
国家自然科学基金项目(51877075)。
关键词
故障时序特征
时序特征模式识别
高斯混合模型与隐层马尔科夫链
实时诊断
牵引传动系统
fault time-series feature
time series pattern recognition
Gaussian mixture models and hidden Markov models(GMM-HMM)
real-time diagnosis
traction drive system
