吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 368-376.doi: 10.13229/j.cnki.jdxbgxb20200930

• 车辆工程·机械工程 • 上一篇    

基于卷积门控循环网络的齿轮箱故障诊断

张龙1(),徐天鹏1,王朝兵1,2,易剑昱1,甄灿壮1   

  1. 1.华东交通大学 机电与车辆工程学院,南昌 330013
    2.中车戚墅堰机车有限公司,常州 213011
  • 收稿日期:2020-12-03 出版日期:2022-02-01 发布日期:2022-02-17
  • 作者简介:张龙(1980-),男,副教授,博士.研究方向:故障诊断,信号处理,模式识别.E-mail:longzh@126.com
  • 基金资助:
    国家自然科学基金项目(51665013);江西省研究生创新项目(YC2019-S243);江西省教育厅科学技术研究项目(191327)

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

摘要:

为充分利用信号的时序相关性特征,增强模型对数据信息的全面挖掘能力,以进一步提高卷积神经网络(CNN)诊断精度,本文将CNN与善于处理数据时序相关性特征的门控循环单元(GRU)相结合,提出了一种新的齿轮箱故障诊断模型。CNN通过端对端的方式提取数据空间特征,并将提取的特征作为GRU的输入进一步提取时空特征,最后将GRU提取的时空特征作为SoftMax的输入进行故障识别。两组齿轮箱实验数据分析结果显示:平均故障诊断精度分别可达99.86%和99.85%,与其它现有模型的结果对比体现了本文模型的有效性和优越性。

关键词: 机械设计制造及其自动化, 卷积神经网络, 门控循环单元, 时空特征, 故障诊断

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

中图分类号: 

  • TH132.4

图1

GRU内部结构示意图"

图2

本文方法故障诊断新模型"

表1

齿轮箱1故障类型及预处理"

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

表2

齿轮箱2故障类型及预处理"

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

表3

新模型网络结构参数"

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\

图3

训练与验证样本损失与准确率的变化"

表4

新模型10次运行结果"

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

图4

测试样本混淆矩阵"

图5

训练样本与验证样本损失与准确率随Epochs的变化曲线"

图6

测试样本混淆矩阵"

图7

隐含层特征可视化"

1 Liu Y B, Qian Q, Liu F, et al. Wayside bearing fault diagnosis based on envelope analysis paved with time-domain interpolation resampling and weighted-correlation-coefficient-guided stochastic resonance[J]. Shock and Vibration, 2017(1): 1-17.
2 McKee K K, Forbes G L, Mazhar I, et al. A vibration cavitation sensitivity parameter based on spectral and statistical methods[J]. Expert Systems with Applications, 2015, 42(1): 67-78.
3 胡茑庆, 陈徽鹏, 程哲, 等. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报, 2019, 55(7): 9-18.
Hu Niao-qing, Chen Hui-peng, Cheng Zhe, et al. Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18.
4 黄海松, 魏建安, 任竹鹏, 等. 基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(10): 65-74, 132.
Huang Hai-song, Wei Jian-an, Ren Zhu-peng, et al. Rolling bearing fault diagnosis based on imbanced sample characteristics oversampling algorithm and SVM[J]. Journal of Mechanical Engineering, 2020. 39(10): 65-74, 132.
5 谭刚, 李军. 机械设备故障诊断系统中决策树算法的应用研究[J]. 自动化与仪器仪表, 2016(12): 90-91.
Tan Gang, Li Jun. Application of decision tree algorithm in mechanical equipment fault diagnosis system[J].Automation & Instrumentation, 2016(12): 90-91.
6 何雷, 刘溯奇, 蒋婷, 等. 基于改进LMD与BP神经网络的变速箱故障诊断[J]. 机械传动, 2020, 44(1): 171-176.
He Lei, Liu Su-qi, Jiang Ting, et al. Gearbox fault diagnosis based on improved LMD and BP neural network[J]. Journal of Mechanical Transmission, 2020, 44(1): 171-176.
7 Boudiaf A, Moussaoui A, Dahane A, et al. A comparative study of various methods of bearing faults diagnosis using the case western reserve university data[J]. Journal of Failure Analysis and Prevention, 2016, 16(2): 271-284.
8 Li P, Chen Z K, Yang L T, et al. An incremental deep convolutional computation model for feature learning on industrial big data[J]. IEEE Transactions on Industrial Informatics, 2019, 15(3): 1341-1349.
9 Mao S B, Rajan D, Chia L T, et al. Deep residual learning for image recognition[J]. IEEE Computer Society, 2016: 770-778.
10 Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.
11 Tran Q T, Nguyen S D, Seo Tae-ll.Algorithm for estimating online bearing fault upon the ability to extract meaningful information from big data of intelligent structures[J]. IEEE Transactions on Industrial Electronics, 2019, 66(5): 3804-3813.
12 Sun W F, Yao B,Zeng N Y, et al. An intelligent gear fault diagnosis methodology using a complex wavelet enhanced convolutional neural network[J]. Materials, 2017, 10(7): 790-807.
13 He J, Yang S, Gan C. Unsupervised fault diagnosis of a gear transmission chain using a deep belief network[J]. Sensors, 2017, 10(7): 1564-1583.
14 Eren L. Bearing fault detection by one-dimensional convolutional neural networks[J]. Mathematical Problems in Engineering, 2017(7): 8617315.
15 Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
16 李琳辉, 伦智梅, 连静, 等. 基于卷积神经网络的道路车辆检测方法[J]. 吉林大学学报: 工学版, 2017, 47(2): 384-391.
Li Lin-hui, Zhi-mei Lun, Lian Jing, et al. Convolution neural network-based vehicle detection method[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(2): 384-391.
17 钟辉, 李红, 李振建, 等. 基于卷积神经网络的图像拼接篡改检测算法[J]. 吉林大学学报: 工学版, 2020, 50(4): 1428-1434.
Zhong Hui, Li Hong, Li Zhen-jian, et al. Image manipulation detection based on convolutional neural networks[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(4): 1428-1434.
18 何俊. 齿轮箱振动特性分析与智能故障诊断方法研究[D]. 杭州: 浙江大学机械工程学院, 2018.
He Jun. Vibration charcteristic analysis and intelligent fault diagnosis of gearboxes[D]. Hangzhou: School of Mechanical Engineering , Zhejiang University, 2018.
19 Vibroacoustic gear signatures with time-frequency spectrograms[EB/OL]. [202-12-03].
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