Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 368-376.doi: 10.13229/j.cnki.jdxbgxb20200930

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Gearbox fault diagnosis baed on convolutional gated recurrent network

Long ZHANG1(),Tian-peng XU1,Chao-bing WANG1,2,Jian-yu YI1,Can-zhuang ZHEN1   

  1. 1.School of Mechatronics and Vehicle Engineering，East China Jiaotong University，Nanchang 330013，China
    2.CRRC Qishuyan Co. ，Ltd. ，Changzhou 213011，China
  • Received:2020-12-03 Online:2022-02-01 Published:2022-02-17

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Abstract:

In order to make full use of the time series correlation characteristics of signals and enhance the model's ability to mine data information comprehensively， thereby further improving the diagnostic accuracy of CNN.This paper proposes a new gearbox fault diagnosis model， which combines CNN with Gated Recurrent Unit （GRU）， which is good at dealing with data timing correlation characteristics. CNN extracts the spatial features of data by end-to-end， and uses the extracted features as the input of GRU to further extract the spatiotemporal features. Finally， the spatiotemporal features extracted by GRU are used as the input of softmax for fault identification. The experimental results show of two groups of gearbox experimental data show that the average fault diagnosis accuracy can reach 99.86% and 99.85% respectively and compared with other models， the results show that the model is superior and effective.

Key words: Mechanical design manufacture and automation major, convolutional neural network, gated recurrent unit, spatiotemporal characteristics, fault diagnosis

CLC Number: 

  • TH132.4

Fig.1

Internal structure diagram of GRU"

Fig.2

A new model of fault diagnosis based on method proposed in this paper"

Table 1

Fault types and pretreatment of gearbox 1"

故障类型样本总数训练样本验证样本测试样本故障类别
输入级齿轮断齿37522575751
输入级齿轮齿面胶合剥落37522575752
中间级齿轮齿面胶合剥落37522575753
输出级齿轮齿面胶合剥落与断齿37522575754
输出级齿轮缺齿37522575755
输入级齿轮齿根裂纹37522575756
中间级齿轮齿根裂纹37522575757
输出级齿轮齿根裂纹37522575758
输入级齿轮断齿与中间级齿轮齿面胶合剥落37522575759
健康375225757510

Table 2

Fault types and pretreatment of gearbox 2"

故障 类型故障程度样本 总数训练 样本验证 样本测试 样本故障 类别
正常无故障37522575751
剥落轻度37522575752
中度37522575753
重度37522575754
磨损轻度37522575755
重度37522575756

Table 3

Network structure parameters of new model"

网络层卷积/池化/节点步长输出大小激活函数填充方式
卷积层120×18None×256×64RuleValid
池化层14×14None×63×64\\
卷积层25×12None×30×128RuleValid
池化层22×12None×15×128\\
隐含层64\None×64\\
SoftMax层10\None×10SoftMax\

Fig.3

Loss and Accuracy changes of training and validation data"

Table 4

Results of 10 runs of new model"

类别错误分类数准确率/%平均准确率/%
1299.799.86
20100.0
3199.9
4199.9
5399.3
6199.9
70100.0
80100.0
9199.9
100100.0

Fig.4

Test data confusion matrix"

Fig.5

Loss and accuracy changes of training and validation data"

Fig.6

Validation data confusion matrix"

Fig.7

Visualization of hidden layer 2 features"

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