The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Deep Residual Shrinkage Networks for Fault Diagnosis
Abstract
This article develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to eliminate unimportant features. Moreover, considering that it is generally challenging to set proper values for the thresholds, the developed deep residual shrinkage networks integrate a few specialized neural networks as trainable modules to automatically determine the thresholds, so that professional expertise on signal processing is not required. The efficacy of the developed methods is validated through experiments with various types of noise.
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“A multiscale residual deep neural network (MSRDN) (inspired by [94]) was developed in [93] to classify passive underwater acoustic signals by modelling the original signal waveform directly.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [94]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
(2008) · Journal of Machine Learning Research · Cited by 33,051
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
De-noising by soft-thresholding
Donoho, D. L. (1995) · IEEE Transactions on Information Theory · doi:10.1109/18.382009 · Cited by 9,655
Artificial intelligence for fault diagnosis of rotating machinery: A review
Liu, R., Yang, B., Zio, E., & Chen, X. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.02.016 · Cited by 2,188
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
İnce, T., Serkan Kiranyaz, B., Eren, L., Aşkar, M., & Gabbouj, M. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2582729 · Cited by 1,419
(2014) · arXiv (Cornell University) · Cited by 942
Research works printed but not linked: 1
- goodfellow, 2016, Deep Learning
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Sorted by citations
Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3005965 · Cited by 471
Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis
Xiao, Y., Shao, H., Wang, J., Yan, S., & Liu, B. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110936 · Cited by 278
Ding, Y., Zhuang, J., Ding, P., & Jia, M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108126 · Cited by 256
Ding, Y., Jia, M., Zhuang, J., Cao, Y., Zhao, X., & Lee, C.-G. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108890 · Cited by 246
Yu, J., & Zhou, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2966326 · Cited by 236
Metadata
- Authors
- Zhao, M., Zhong, S., Fu, X., Tang, B., & Pecht, M.
- Affiliations
- Harbin Institute of Technology (CN)
- Chongqing University (CN)
- University of Maryland, College Park (US)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-07
- Volume
- 16
- Issue
- 7
- Pages
- 4681-4690
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2977117446
- Cited by
- 1,447
- References
- 27
- Citations by year
- 2026: 200 · 2025: 309 · 2024: 314 · 2023: 268 · 2022: 227 · 2021: 107 · 2020: 21
- FWCI
- 49.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1533202)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Keywords
- deep residual shrinkage network, fault diagnosis, vibration signals, soft thresholding, representation learning, noise reduction, threshold selection, industrial machinery condition monitoring
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