基于t-分布随机邻域嵌入融合最优多频带盒维数的牵引整流器故障诊断  

Fault Diagnosis of Traction Rectifier Based on t-Distributed Stochastic Neighbor Embedding Fusion Optimal Multi-Band Box Dimension

作　　者：毛向德 董海鹰[1,3] 梁金平 Mao Xiangde;Dong Haiying;Liang Jinping(School of Automation and Electrical Engineering Lanzhou Jiaotong University,Lanzhou 730070,China;Shaanxi Railway Institute,Weinan 714000,China;School of New Energy and Power Engineering Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070 [2]陕西铁路工程职业技术学院,渭南714000 [3]兰州交通大学新能源与动力工程学院,兰州730070

出　　处：《电工技术学报》2025年第2期531-543,共13页Transactions of China Electrotechnical Society

基　　金：甘肃省青年科技基金资助项目(24JRRA266)。

摘　　要：针对电力机车牵引变流器中故障率较高的牵引整流器,提出一种基于t-分布随机邻域嵌入(t-SNE)融合最优多频带盒维数特征的故障诊断方法。首先,在最优小波基函数的基础上,利用小波包分解牵引整流器在不同工况、不同运行模式下的故障信号,得到一系列的最优频带;其次,计算各频带系数的分形盒维数,作为高维故障特征;最后,采用t-SNE流形学习对高维特征进行融合,以减少特征之间的冲突和冗余,得到简单敏感的故障特征。结果表明:信噪比为30 dB时,15种故障模式的最终诊断结果为99.79%,对不同的信噪比仍有较高的准确率。与其他方法相比,所提方法对不同类型开关器件(绝缘栅双极晶体管和电力二极管)的开路故障具有较高的诊断率和较强的鲁棒性。In the traction converters of electric locomotives,the traction rectifier has a very high fault rate due to its complicated control and operating conditions.Fault diagnosis of rectifier can improve the operation and maintenance ability of electric locomotives and ensure the safety and stability of the train.The traditional rectifier fault diagnosis technology is limited in system modeling and signal analysis under variable load and strong noise environments.The data-driven rectifier fault diagnosis method is independent of the precise system model and prior knowledge of signal mode.However,redundant information and feature conflicts exist in the feature extraction process.The extracted fault feature dimension is large,which increases the difficulty of fault identification and diagnosis accuracy.This paper proposes a rectifier fault diagnosis method based on fractal box features fused by manifold learning.The fractal box dimension feature is extracted from the fault signal,the effective fault feature is obtained by manifold learning fusion,and the fault component of the traction rectifier is accurately diagnosed.Firstly,signals of the traction rectifier under different working conditions and fault modes are decomposed using nine Daubechies wavelet functions,and the information of 9 frequency bands for each fault mode is obtained.According to the larger energy entropy ratio of each frequency band coefficient,the optimal frequency band is selected.Secondly,the fractal box dimension of the optimal frequency band is calculated.The t-distributed random neighborhood embedding(t-SNE)algorithm fuses the fault features.Finally,a support vector machine is used for pattern recognition to realize fault diagnosis of the traction rectifier.The optimal frequency band decomposes signal decomposition with different wavelet functions.The fractal box dimension quantitatively describes nonlinear and non-stationary signals.The t-SNE algorithm lowers the conflict and redundancy among features,reducing the difficulty of fault identifica

关 键 词：牵引整流器 能熵比 分形盒维数 t-分布随机邻域嵌入 特征融合 

分 类 号：U264.4[机械工程—车辆工程]