基于电流信号的变速工况行星齿轮箱故障诊断*

doi:10.16731/j.cnki.1671-3133.2025.01.014

现代制造工程 ›› 2025, Vol. 532 ›› Issue (1): 111-120.doi: 10.16731/j.cnki.1671-3133.2025.01.014

• 设备设计/诊断维修/再制造 • 上一篇下一篇

基于电流信号的变速工况行星齿轮箱故障诊断^*

乔宁宁¹, 李峰²

1 山西工程科技职业大学设备工程学院,晋中 030619;
2 太原理工大学航空航天学院,太原 030024

收稿日期:2024-07-24 出版日期:2025-01-18 发布日期:2025-02-10
通讯作者: 乔宁宁,硕士,讲师,主要研究方向为机械安装工程。
作者简介:李峰,博士,讲师,主要研究方向为机械装备故障诊断与寿命预测。E-mail:58537495@qq.com;lifeng@tyut.edu.cn
基金资助:
* 山西省基础研究计划青年科学研究项目(20210302124204)

Fault diagnosis method for variable speed planetary gearbox based on current signal

QIAO Ningning¹, LI Feng²

1 School of Equipment Engineering,Shanxi Vocational University of Engineering Science and Technology,Jinzhong 030619,China;
2 College of Aeronautics and Astronautics,Taiyuan University of Technology,Taiyuan 030024,China

Received:2024-07-24 Online:2025-01-18 Published:2025-02-10

摘要/Abstract

摘要： 基于电流信号的故障诊断方法在信号采集和抗外界干扰方面存在一定的优势。由于工频信号的存在,导致齿轮故障特征提取困难;因此,去除电流信号中的工频成分是一项重要的工作,尤其是当齿轮箱处于变速工况时。目前,变速工况时基于电流信号的行星齿轮箱故障诊断仍没有理想的方法。提出了一种基于无功功率、格拉姆角场和平均教师半监督模型的行星齿轮故障诊断方法。首先通过计算系统无功功率去除时变电流信号中的工频成分,然后利用格拉姆角场将一维的时序数据转化为特征信息更完善的二维数据,最后基于改进的半监督学习模型实现了行星齿轮的故障识别。试验结果表明,在使用20 %的有标记数据的情况下故障诊断准确率可以达到92.31 %。

关键词: 故障诊断, 电流信号, 行星齿轮箱, 变速工况, 半监督学习

Abstract: The fault diagnosis method based on current signals has advantages in signal acquisition and anti-interference.It is difficult to extract gear fault features,due to the power frequency component. Therefore,removing the power frequency component from the current signal is a significant task,especially when the gearbox is in variable speed conditions. At present,there is still no ideal method for diagnosing planetary gearbox faults under variable speed conditions. A planetary gear fault diagnosis method based on reactive power,Gramion Angular Field,and average teacher semi-supervised model was proposed. Firstly,the power frequency component was removed from the time-varying current signal by calculating the reactive power. Then,Gramion Angular Field was used to transform 1D temporal data into 2D data with more complete feature information. Finally,the fault recognition of planetary gears was achieved based on an improved semi-supervised learning model. The experimental results showed that the recognition accuracy could reach 92.31 % when using 20 % labeled data.

Key words: fault diagnosis, current signal, planetary gearbox, variable speed operation, semi-supervised learning

中图分类号:

TH132

乔宁宁, 李峰. 基于电流信号的变速工况行星齿轮箱故障诊断^*[J]. 现代制造工程, 2025, 532(1): 111-120.

QIAO Ningning, LI Feng. Fault diagnosis method for variable speed planetary gearbox based on current signal[J]. Modern Manufacturing Engineering, 2025, 532(1): 111-120.

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基于电流信号的变速工况行星齿轮箱故障诊断^*

Fault diagnosis method for variable speed planetary gearbox based on current signal

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