A CNN-ABiGRU method for Gearbox Fault Diagnosis

被引：0

作者：

Zheng X. ^{[1
]}

Ye Z. ^{[1
]}

Wu J. ^{[1
]}

机构：

[1] School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400000 China

来源：

International Journal of Circuits, Systems and Signal Processing | 2022年 / 16卷

关键词：

Attention Mechanism; BiGRU; CNN; Fault Diagnosis; Gearbox;

D O I：

10.46300/9106.2022.16.54

中图分类号：

TH13 [机械零件及传动装置];

学科分类号：

080203 ;

摘要：

As a key part of modern industrial machinery, there has been a lot of fault diagnosis methods for gearbox. However, traditional fault diagnosis methods suffer from dependence on prior knowledge. This paper proposed an end-to-end method based on convolutional neural network (CNN), Bidirectional gated recurrent unit (BiGRU), and Attention Mechanism. Among them, the application of BiGRU not only made perfect use of the time sequence of signal, but also saved computing resources more than the same type of networks because of the low amount of calculation. In order to verify the effectiveness and generalization performance of the proposed method, experiments are carried out on two datasets, and the accuracy is calculated by the ten-fold crossvalidation. Compared with the existing fault diagnosis methods, the experimental results show that the proposed model has higher accuracy. © 2022, North Atlantic University Union NAUN. All rights reserved.

引用

页码：440 / 446

页数：6

共 28 条

[1]

Gao H. Z., Liang L., Chen X. G., Xu G. H., Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization, Chinese Journal of Mechanical Engineering, 28, pp. 96-105, (2015)

[2]

Shen Z. J., Chen X. F., Zhang X. L., He Z. J., A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM, Measurement, 45, pp. 30-40, (2012)

[3]

Zhu D. Q., Yu S. L., Survey of knowledge based fault diagnosis methods, J. of Anhui University of Technology, 19, pp. 197-205, (2002)

[4]

Li H., Xiao D. Y., Survey of fault diagnosis methods based on data driven, Control and Decision, 26, pp. 1-9, (2011)

[5]

Du W. L., Tao J. F., Li Y. M., Liu C. L., Wavelet leaders multifractal features based fault diagnosis of rotating mechanism, Mechanical Systems and Signal Processing, 43, pp. 57-75, (2014)

[6]

Jin X. H., Zhao M. B., Chow T. W. S., Pecht M., Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis, IEEE Transactions on Industrial Electronics, 61, pp. 2441-2451, (2014)

[7]

Zheng X. Y., Wu J. L., Ye Z. Y., An End-To-End CNN-BiLSTM Attention Model for Gearbox Fault Diagnosis, IEEE International Conference on Progress in Informatics and Computing, pp. 386-390, (2020)

[8]

Pei X. L., Zheng X. Y., Wu J. L., Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder, Measurement, 179, (2021)

[9]

Shao S. Y., McAleer S., Yan R. Q., Baldi P., Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning, IEEE Transactions on Industrial Informatics, 15, pp. 2446-2455, (2019)

[10]

Wang H., Xu J. W., Sun C., Yan R. Q., Chen X. F., Intelligent Fault Diagnosis for Planetary Gearbox Using Time-Frequency Representation and Deep Reinforcement Learning, IEEE/ASME Transactions on Mechatronics, (2021)

← 1 2 3 →