基于DWAE和GRUNN组合模型的变工况齿轮箱故障诊断分析

刘传慧; 陈晓静; 侯晓晓; 朱强; 董勇

doi:10.16578/j.issn.1004.2539.2022.02.025

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基于DWAE和GRUNN组合模型的变工况齿轮箱故障诊断分析

开发应用 | 更新时间：2022-09-25

- 基于DWAE和GRUNN组合模型的变工况齿轮箱故障诊断分析
- Fault Diagnosis Analysis of Variable Working Condition Gearbox based on DWAE and GRUNN Combination Model
- 机械传动 2022年46卷第2期页码：155-159
- 作者机构：
  
  1.黄河交通学院汽车工程学院，河南焦作 454950
  2.河南理工大学机械工程学院，河南焦作 454003
  3.河南省东泰齿轮有限公司，河南新乡 453400
- 作者简介：
  
  刘传慧（1967— ），女，河南滑县人，硕士，高级工程师；主要从事于机械设计与机械制造。
- 基金信息：
  
  河南省教育厅科技研究课题(ZD2018020)
- DOI：10.16578/j.issn.1004.2539.2022.02.025
  中图分类号：
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刘传慧,陈晓静,侯晓晓等.基于DWAE和GRUNN组合模型的变工况齿轮箱故障诊断分析[J].机械传动,2022,46(02):155-159.

Liu Chuanhui,Chen Xiaojing,Hou Xiaoxiao,et al.Fault Diagnosis Analysis of Variable Working Condition Gearbox based on DWAE and GRUNN Combination Model[J].Journal of Mechanical Transmission,2022,46(02):155-159.
刘传慧,陈晓静,侯晓晓等.基于DWAE和GRUNN组合模型的变工况齿轮箱故障诊断分析[J].机械传动,2022,46(02):155-159. DOI： 10.16578/j.issn.1004.2539.2022.02.025.

Liu Chuanhui,Chen Xiaojing,Hou Xiaoxiao,et al.Fault Diagnosis Analysis of Variable Working Condition Gearbox based on DWAE and GRUNN Combination Model[J].Journal of Mechanical Transmission,2022,46(02):155-159. DOI： 10.16578/j.issn.1004.2539.2022.02.025.

摘要

为了更好地识别噪声与时变转速条件对变工况齿轮箱的故障，开发了一种通过深度小波自动编码器（DWAE）与门控循环单元神经网络（GRUNN）相结合的变工况齿轮箱故障识别方法，其能够从含噪样本自主提取得到鲁棒故障特征；通过Adam与Dropout方法进行训练，通过Softmax分类器对待诊样本的变工况齿轮箱运行状态进行了准确识别。研究结果表明，采用该模型识别齿轮故障时，能够达到有效分离齿轮的6种故障状态，从而满足齿轮状态聚类的优化功能；该模型能够提取出DWAE的鲁棒特征参数，也可以发挥GRUNN以实现消除梯度的效果。当训练样本数增加，待诊样本的准确率也发生了明显提升。样本数超过200后，测试待诊样本可获得稳定准确率，通过DWAE-GRUNN方法识别得到的准确率最高。针对变转速工况，该模型可以保持很好的准确率。

Abstract

In order to better identify the faults of gearbox under varying working conditions caused by noise and time-varying speed conditions， a fault identification method of gearbox under varying conditions is developed by combining DWAE and GRUNN， which could extract robust fault characteristics from noisy samples. The Adam and Dropout methods are used for training， and the softmax classifier is used to accurately identify the operating state of the gearbox under different working conditions. The results show that the six fault states of gears can be separated effectively when the model is used to identify gear faults， so as to meet the optimization function of gear state clustering. This model can extract the robust characteristic parameters of DWAE， and it can also use GRUNN to eliminate the gradient. When the number of training samples increased， the accuracy of waiting samples also improved significantly. When the number of samples is more than 200， stable accuracy could be obtained by testing the waiting samples， and the accuracy is the highest by dWAe-Grunn method. The model can maintain good accuracy under variable speed working conditions.

关键词

变工况齿轮箱故障识别深度小波自动编码器门控循环单元神经网络准确率

Keywords

Variable working condition gearboxFault identificationDeep wavelet autoencoder （DWAE）Gated cyclic unit neural network （GRUNN）Accuracy

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