Cited by 1,447Machine Fault Diagnosis TechniquesFault Detection and Control SystemsElectrical Fault Detection and Protection

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Deep Residual Shrinkage Networks for Fault Diagnosis

Zhao, Minghang, Zhong, Shisheng, Fu, Xuyun, Tang, Baoping, Pecht, Michael·2020·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2019.2943898

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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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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)
ORCID: Zhao, Minghang
Zhong, Shisheng
Fu, Xuyun
Tang, Baoping
Pecht, Michael
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-07
Volume: 16
Issue: 7
Pages: 4681-4690
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2019.2943898
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Deep Residual Shrinkage Networks for Fault Diagnosis

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