基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法

陈向民; 雷瀚霖; 张亢; 李泳辉; 李博; 姚鹏

doi:doi:10.19912/j.0254-0096.tynxb.2024-1759

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基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法

更新时间：2026-03-06

- 基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法
- 基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法
- 太阳能学报 2026年47卷第2期页码：470-477
- 作者机构：
- 作者简介：
- 基金信息：
  
  湖南省自然科学基金（2018JJ3541）;湖南省教育厅项目（20B019;21B0347）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1759
  中图分类号： TK83
- 网络首发：2026-03-05，
  
  纸质出版：2026-03-05
- 稿件说明：
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陈向民, 雷瀚霖, 张亢, 等. 基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法[J]. 太阳能学报, 2026,47(2):470-477.

陈向民, 雷瀚霖, 张亢, et al. 基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法[J]. 2026, 47(2): 470-477.
陈向民, 雷瀚霖, 张亢, 等. 基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法[J]. 太阳能学报, 2026,47(2):470-477. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1759.

陈向民, 雷瀚霖, 张亢, et al. 基于改进MobileViT的风力机增速齿轮箱智能故障诊断方法[J]. 2026, 47(2): 470-477. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1759.

摘要

针对传统方法在增速齿轮箱运行条件下无法充分提取全局信息且识别准确率偏低的问题

提出一种改进的MobileViT风力机增速齿轮箱智能故障诊断方法。该方法在数据预处理模块中利用格拉姆矩阵将一维数据转换成二维图像

保持了信号对时间的依赖性;其次

通过异构内核卷积（HetConv）提取故障浅层信息和提供空间位置偏置

并采用带有自注意力机制（SA）的视觉转换器（ViT）对故障信息进行全局特征提取;最后

使用全连接层对故障进行识别。采用轴承故障数据集和齿轮故障数据集对所提方法进行验证

结果表明：所提方法对齿轮箱状态的识别准确率均在99%以上

优于其他常用网络;且模型参数量更低

模型更小。

Abstract

The fault diagnosis of speed increasing gearbox is of great significance for ensuring the reliable operation of doubly fed wind turbines. However

traditional method cannot fully extract the global information and the recognition accuracy is low under the operating conditions of speed increasing gearbox. In order to tackle this issue an intelligent fault diagnosis technique for the wind turbine's speed-increasing gearbox is proposed in this paper

based on advanced mobile ViT. In this method

the Gram matrix is used to convert the one-dimensional data into a two-dimensional image in the data preprocessing module

which maintains the dependence of the signal on time. Then

heterogeneous kernel-based convolutions （HetConv） are employed to extract the fault local information and also provide spatial position bias

and then the Vision transformer （ViT） with Self-Attention is utilized to extract the global features of the fault information. Finally

fault identification is executed according to the output of the fully connected layer. The experimental results show that the average accuracy of the proposed method is high than 99%

which is better than other commonly used networks. Moreover

the number of model parameters is lower

and the model is smaller.

关键词

Keywords

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