信息融合的NRP-AlexNet-SENet风电齿轮箱故障诊断

doi:doi:10.19912/j.0254-0096.tynxb.2024-0848

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信息融合的NRP-AlexNet-SENet风电齿轮箱故障诊断

更新时间：2025-10-22

- 信息融合的NRP-AlexNet-SENet风电齿轮箱故障诊断
- Vol. 46, Issue 9, Pages: 143-151(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0848
  CLC： TH165
- Published Online：14 October 2025，
  
  Published：2025
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[J]. 2025, 46(9): 143-151.
DOI：

[J]. 2025, 46(9): 143-151. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0848.

摘要

针对风电机组齿轮箱故障振动信号的非线性、特征信息混叠和诊断精度低等问题

结合时域特征分析与多传感器信息融合技术

提出一种无阈值递归图（NRP）与深度学习相结合的早期故障识别方法。首先

将时域指标作为特征参数并采用特征级与数据级融合技术构建数据信息;其次

采用NRP将一维数据信息转换为二维彩色可视特征图;然后

构建一种AlexNet-SENet网络结构

利用嵌入SENet注意力机制的改进AlexNet使其自适应选择并重点搜索结构与节点的关键特征信息。最后

以华中科技大学行星齿轮箱动力学试验平台采集的振动数据集为实例

结果表明该方法的诊断准确率为99%

能更有效提取故障特征信息

具有更高的分类诊断精度。

Abstract

Aiming at the issues of nonlinearity

aliasing of characteristic information

and low diagnostic accuracy of vibration signals in the wind turbine gearbox

by integrating time-domain characteristic analysis and multi-sensor information fusion technology

an early fault identification approach based on non-threshold recurrence plot （NRP） and deep learning was proposed. First

the time-domain indices is used as the feature parameter

and the feature-level and data-level fusion technology is used to construct the dataset. Secondly

NRP converts one-dimensional data into two-dimensional colored visual feature maps. Then

an AlexNet-SENet network structure is built

and the improved AlexNet embedded with SENet's attention mechanism makes it adaptive to focus on the key structural and nodal feature. Finally

taking the vibration data set collected by the planetary gearbox dynamics test platform of Huazhong University of Science and Technology as an example

the results show that the diagnosis accuracy of this method is 99%

which can extract fault characteristics more effectively and has higher classification and diagnosis accuracy.

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references

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