Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
Source abstract
Abstract
Intelligent fault diagnosis is a hot research topic in machinery and equipment health monitoring. However, most intelligent fault diagnosis models have good performance in single fault mode, but poor performance in multiple fault modes. In real industrial scenarios, the interference of noise also makes it difficult for intelligent diagnostic models to extract fault features. To solve these problems, an adaptive multi-channel residual shrinkage network (AMC-RSN) is proposed in this paper. First, a channel attention mechanism module is constructed in the residual block and a soft thresholding function is introduced for noise reduction. Then, an adaptive multi-channel network is constructed to fuse the feature information of each channel in order to extract as many features as possible. Finally, the Meta-ACON activation function is used before the fully connected layer to decide whether to activate the neurons by the model outputs. The method was implemented in gearbox fault diagnosis, and the experimental results show that AMC-RSN has better diagnostic results than other networks under various faults and strong noises.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Guangxi University, Nanning 530004, China
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Guangxi Province
References (32)
Show source-provided references
1. Drive-train gearbox fault diagnosis: Vibration-and current-based approaches10.1109/MIAS.2017.2740470 ↗
2. Deep Learning-Based Partial Domain Adaptation Method on Intelligent Machinery Fault Diagnostics10.1109/TIE.2020.2984968 ↗
3. Multiscale deep intra-class transfer learning for bearing fault diagnosis10.1016/j.ress.2020.107050 ↗
4. Wind Turbine Blade Bearing Fault Diagnosis Under Fluctuating Speed Operations via Bayesian Augmented Lagrangian Analysis10.1109/TII.2020.3012408 ↗
5. Frequency domain averaging based experimental evaluation of gear fault without tachometer for fluctuating speed conditions10.1016/j.ymssp.2016.08.015 ↗
6. Deep Residual Shrinkage Networks for Fault Diagnosis10.1109/TII.2019.2943898 ↗
7. Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis10.1016/j.ymssp.2013.02.001 ↗
8. Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization10.1016/j.neucom.2015.04.069 ↗
9. Lu, J., Qian, W., Li, S., and Cui, R. (2021). Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery. Appl. Sci., 11.10.3390/app11030919 ↗
10. Artificial intelligence for fault diagnosis of rotating machinery: A review10.1016/j.ymssp.2018.02.016 ↗
11. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
12. Hierarchical diagnosis of bearing faults using branch convolutional neural network con-sidering noise interference and variable working conditions10.1016/j.knosys.2021.107386 ↗
13. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load10.1016/j.ymssp.2017.06.022 ↗
14. The Variational Kernel-Based 1-D Convolutional Neural Network for Machinery Fault Diagnosis10.1109/TIM.2021.3105252 ↗
15. Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability10.1109/TIE.2019.2931255 ↗
16. Convolutional Neural Network-Based Bayesian Gaussian Mixture for Intelligent Fault Diagnosis of Rotating Machinery
17. Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis10.1109/TIE.2019.2902817 ↗
18. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
19. Identity mappings in deep residual networks
20. Dual-Path Mixed-Domain Residual Threshold Networks for Bearing Fault Diagnosis10.1109/TIE.2022.3144572 ↗
21. Li, C., Yu, L., Zhang, A., He, Q., Yang, W., and Duan, Z. (2020, January 23). A Novel Bearing Fault Diagnosis of Raw Signals Based on 1D Residual Convolution Neural Network. Proceedings of the 2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), Shenzhen, China.10.1109/HPBDIS49115.2020.9130567 ↗
22. Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis10.1109/TIE.2018.2866050 ↗
23. Multi-Scale Cluster-Graph Convolution Network With Multi-Channel Residual Network for Intelligent Fault Diagnosis
24. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox10.1109/TIE.2018.2844805 ↗
25. Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis
26. Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions10.1109/TII.2019.2941868 ↗
27. A novel convolutional neural network with multiscale cascade midpoint residual for fault diag-nosis of rolling bearings10.1016/j.neucom.2022.07.022 ↗
28. Multi-fault diagnosis of rotating machinery via iterative multivariate variational mode decomposition10.1088/1361-6501/ac8274 ↗
29. Yuan, R., Lv, Y., and Song, G. (2018). Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition. Sensors, 18.10.3390/s18041210 ↗
30. Rolling element bearing multi-fault diagnosis using morphological joint time–frequency adaptive kernel–based semi-smart framework10.1177/10775463211022878 ↗
31. Ma, N., Zhang, X., Liu, M., and Sun, J. (2021, January 20–25). Activate or Not: Learning Customized Activation. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.00794 ↗
32. Iterative thresholding algorithms10.1016/j.acha.2007.10.005 ↗