基于深度高斯过程的永磁牵引电机匝间短路分级评估方法

戴计生; 胡德安; 徐海龙; 朱文龙; 廖启术

doi:10.13890/j.issn.1000-128X.2024.02.013

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基于深度高斯过程的永磁牵引电机匝间短路分级评估方法

健康与诊断 | 更新时间：2024-08-02

- 基于深度高斯过程的永磁牵引电机匝间短路分级评估方法
- Grading evaluation method for inter-turn short circuit of permanent magnet traction motor based on deep Gaussian processes
- 机车电传动 2024年第2期页码：108-117
- 作者机构：
  
  1.湖南大学机械与运载工程学院，湖南长沙 410082
  2.株洲中车时代电气股份有限公司，湖南株洲 412001
- 作者简介：
  
  戴计生，男，博士，正高级工程师，主要从事轨道交通与新能源状态监测、故障诊断与预测方面的研究；E-mail: daijs@csrzic.com
- 基金信息：
  
  国家重点研发计划项目(2022YFB4300600);中国国家铁路集团有限公司科技研究开发计划(K2022J032)
- DOI：10.13890/j.issn.1000-128X.2024.02.013
  中图分类号： TM351
- 纸质出版日期：2024-03-10，
  
  收稿日期：2023-09-01，
  
  修回日期：2024-02-05，
- 稿件说明：
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戴计生, 胡德安, 徐海龙, 等. 基于深度高斯过程的永磁牵引电机匝间短路分级评估方法[J]. 机车电传动, 2024(2): 108-117.

DAI Jisheng, HU Dean, XU Hailong, et al. Grading evaluation method for inter-turn short circuit of permanent magnet traction motor based on deep Gaussian processes[J]. Electric drive for locomotives,2024(2): 108-117.
戴计生, 胡德安, 徐海龙, 等. 基于深度高斯过程的永磁牵引电机匝间短路分级评估方法[J]. 机车电传动, 2024(2): 108-117. DOI：10.13890/j.issn.1000-128X.2024.02.013.

DAI Jisheng, HU Dean, XU Hailong, et al. Grading evaluation method for inter-turn short circuit of permanent magnet traction motor based on deep Gaussian processes[J]. Electric drive for locomotives,2024(2): 108-117. DOI：10.13890/j.issn.1000-128X.2024.02.013.

摘要

定子绕组匝间短路是影响永磁牵引电机安全稳定运行的主要故障之一，受运行工况、供电与电机本体不平衡的影响，现有方法难以实现永磁牵引电机匝间短路在线精准评估，这成为永磁电机推广应用迫切需要解决的关键技术难题。因此，文章提出一种基于多特征融合的深度高斯过程永磁牵引电机匝间短路分级评估方法：首先通过建立永磁牵引电机匝间短路故障模型，提取电流不平衡、电流三次谐波与

电流的二次谐波特征；然后采用一种双随机变分推断深度高斯过程（Doubly Stochastic Deep Gaussian Processes

DSDGP）方法对提取特征进行融合训练建模，实现永磁牵引电机匝间短路劣化状态在线分级评估；最后通过永磁电机匝间短路试验与现场案例进行算法验证。结果表明，文章所提方法在多特征融合条件下的评估准确率达到95%以上，相较于支持向量机（support vector machine

SVM）和反向传播神经网络（back-propagation neural

BPN）等分类方法，具有准确率高，适用于变工况、小样本的工程实际应用环境等优点，解决了永磁牵引电机匝间短路早期故障检测及故障严重程度评估的行业难题。

Abstract

The inter-turn short circuit of stator winding is one of the main faults affecting the safe and stable operation of permanent magnet traction motors. Affected by operating conditions

power supply and motor body imbalance

existing methods are difficult to achieve online accurate evaluation of inter-turn short circuit of permanent magnet traction motors. This has become a key technical problem that urgently needs to be solved for the widespread application of permanent magnet motors. Therefore

this paper proposed a grading evaluation method for inter-turn short circuit in permanent magnet traction motors based on multi-feature fusion of deep Gaussian processes. Firstly

by establishing a fault model for inter-turn short circuit in permanent magnet traction motors

features such as current unbalance

third harmonic currents and second harmonic feature of

currents were extracted. Then

a doubly stochastic deep Gaussian processes (DSDGP) method was adopted to fuse and train the extracted features

achieving online grading evaluation of inter-turn short circuit degradation in permanent magnet traction motors. Finally

the algorithm was validated through permanent magnet motor inter-turn short circuit tests and field cases. The results show that the proposed method achieves an evaluation accuracy of over 95% under the condition of multi-feature fusion. Compared with classification methods such as support vector machine (SVM) and back-oropagation neural network (BPN)

it exhibits high accuracy and suitability for engineering practical environments with variable operating conditions and small samples

addressing industry challenges in early fault detection and severity evaluation of inter-turn short circuits in permanent magnet traction motors.

关键词

永磁牵引电机多维特征融合匝间短路分级评估深度高斯过程

Keywords

permanent magnet traction motormulti-feature fusioninter-turn short circuitgraded evaluationdeep Gaussian processes

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