Deep Residual Shrinkage Networks for Fault Diagnosis

Deep Residual Learning for Image Recognition

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Series arc fault detection based on continuous wavelet transform and DRSN-CW with limited source data

Hu, C., Qu, N., & Zhang, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-17235-7 · Cited by 26

Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion

Wang, T., Xu, X. G., Hong-xia, P., Chang, X., Yuan, T., Zhang, X., & Xu, H. (2022) · Applied Sciences · doi:10.3390/app12157640 · Cited by 26

A Lightweight Privacy-Preserving System for the Security of Remote Sensing Images on IoT

Zhang, D., Ren, L., Shafiq, M., & Gu, Z. (2022) · Remote Sensing · doi:10.3390/rs14246371 · Cited by 26

An Interpretable Aerodynamic Identification Model for Hypersonic Wind Tunnels

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CSI Feedback With Model-Driven Deep Learning of Massive MIMO Systems

Jian-hua, G., Wang, L., Li, F., & Xue, J. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3138927 · Cited by 26

Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT

Peng, J., Lu, J., Zhuo, J., & Li, P. (2023) · Sensors · doi:10.3390/s24010150 · Cited by 28

Chemical fault diagnosis network based on single domain generalization

Guo, Y., & Zhang, J. (2024) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2024.05.106 · Cited by 25

Multibranch Horizontal Augmentation Network for Continuous Remaining Useful Life Prediction

Zhou, J., Luo, J., Pu, H., & Qin, Y. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3519347 · Cited by 25

ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network

Feng, X., Guo, Z., & Kwong, S. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1508747 · Cited by 25

Remaining useful life prediction methods of equipment components based on deep learning for sustainable manufacturing: a literature review

Pan, Y., Kang, S., Kong, L., Wu, J., Yang, Y., & Zuo, H. (2025) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060424000271 · Cited by 27

A multi-scale wavelet and spectral kurtosis-guided framework for noise-resistant rolling bearing fault diagnosis

Jia, Y., Yao, D., Yang, J., Jia, L., Zhou, T., & Long, Q. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113662 · Cited by 25

An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding

Zhao, Y., Jia, H., Luo, H., Zhao, F., Qin, Y., & Wang, Y. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22842 · Cited by 25

Mechanical compound fault diagnosis via suppressing intra-class dispersions: A deep progressive shrinkage perspective

Zhong, B., Zhao, M., Zhong, S., Lin, L., & Wang, L. (2022) · Measurement · doi:10.1016/j.measurement.2022.111433 · Cited by 25

Steering Actuator Fault Diagnosis for Autonomous Vehicle With an Adaptive Denoising Residual Network

Xiong, H., Wang, Z., Wu, G., Pan, Y., Yang, Z., & Long, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3199252 · Cited by 27

A novel transfer learning fault diagnosis method for rolling bearing based on feature correlation matching

Wang, B., Wang, B., & Ning, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac8d20 · Cited by 25

SdoNet: Speed Odometry Network and Noise Adapter for Vehicle Integrated Navigation

Wang, X., Zhuang, Y., Cao, X., Li, Q., Wang, Z., Cao, Y., & Chen, R. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3294947 · Cited by 25

Short-term probabilistic load forecasting method based on uncertainty estimation and deep learning model considering meteorological factors

Li, B., Mo, Y., Gao, F., & Bai, X. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109804 · Cited by 25

A novel diagnostic framework based on vibration image encoding and multi-scale neural network

Guan, Y., Meng, Z., Li, J., Cao, W., Sun, D., Liu, J., & Fan, F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124054 · Cited by 25

Industrial process fault diagnosis using dilated convolutional stacking bidirectional gated recurrent unit with high and low-order feature fusion

Zhu, Y., Zhang, R., & Gao, F. (2024) · Chemical Engineering Science · doi:10.1016/j.ces.2024.121164 · Cited by 24

DFSDNet: A dual-branch multi-scale feature fusion network for surface defect detection of copper strips and plates

Wan, F., Zhang, G., & Li, Z. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104265 · Cited by 26

The MLSE-SCAM architecture combines with the improved DRSN-TIC model for Raman spectroscopy small-sample data learning

Cheng, Z., Chen, F., Zuo, E., Gong, Z., Xiao, M., Li, C., Liu, Y., Liu, Y., Chen, C., Lv, X., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127462 · Cited by 24

Multiple path alignment generative adversarial network for rotating Machinery fault diagnosis with limited data

Wang, Y., Lu, Z., Zhang, Z., Liu, C., & Yu, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103550 · Cited by 24

Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks

Zhang, Z., Li, H., Chen, L., & Han, P. (2021) · Shock and Vibration · doi:10.1155/2021/9942249 · Cited by 24

A health indicator construction method based on self-attention convolutional autoencoder for rotating machine performance assessment

Ma, W., Guo, L., Gao, H., Yu, Y., & Qian, M. (2022) · Measurement · doi:10.1016/j.measurement.2022.112108 · Cited by 24

Intelligent Sensing Analysis Using Mel-Time-Frequency-Imaging and Deep Learning for Distributed Fiber-Optic Vibration Detection

Sun, Z., Liu, K., Xu, T., Xu, Y., Fang, W., Xue, K., Huang, Y., Li, S., & Liu, T. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3210603 · Cited by 24

Uncertainty-Aware Ensemble Combination Method for Quality Monitoring Fault Diagnosis in Safety-Related Products

Kafunah, J., Ali, M. I., & Breslin, J. G. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3280566 · Cited by 25

Radar Operation Mode Recognition via Multifeature Residual-and-Shrinkage ConvNet

Zhang, Y., Huo, W., Zhang, C., Huang, Y., Pei, J., Zhang, Y., & Yang, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3286913 · Cited by 24

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Tong, B., Fu, J., Deng, Y., Huang, Y., Chan, P. W., & He, Y. (2023) · Remote Sensing · doi:10.3390/rs15174188 · Cited by 24

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He, M., Tang, S., & Xiao, Y. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011535 · Cited by 24

Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)

Zhang, L., Bian, Y., Jiang, P., Huang, Y., & Liu, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3347510 · Cited by 24

Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event DetectionPreprint

Ye, Z., Wang, X., Liu, H., Qian, Y., Tao, R., Yan, L., & Ouchi, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02011 · Cited by 24

Deep Morphological Shrinkage Convolutional Autoencoder-Based Feature Learning of Vibration Signals for Gearbox Fault Diagnosis

Ye, Z., Shang, Y., Yang, P., Zhou, R., & Yu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3366570 · Cited by 23

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Lu, H., Zhou, K., & He, L. (2024) · Electronics · doi:10.3390/electronics13122403 · Cited by 23

Research on Compound Fault Diagnosis of Bearings Using an Improved DRSN-GRU Dual-Channel Model

Yin, S., & Chen, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3462540 · Cited by 23

Knowledge-informed FIR-based cross-category filtering framework for interpretable machinery fault diagnosis under small samples

Liu, R., Ding, X., Liu, S., Zheng, H., Xu, Y., & Shao, Y. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110610 · Cited by 23

Contrastive prototype guided federated learning for rotating machinery fault diagnosis under spatio-temporal domain shift

Luo, F., Shi, J., Qu, H., Safran, M., Tan, J., & Wan, J. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112707 · Cited by 25

Convolutional Neural Networks for Automated Rolling Bearing Diagnostics in Induction Motors Based on Electromagnetic Signals

Minervini, M., Mognaschi, M. E., Barba, P. D., & Frosini, L. (2021) · Applied Sciences · doi:10.3390/app11177878 · Cited by 23

A detection approach for bundled log ends using K-median clustering and improved YOLOv4-Tiny network

Lin, Y., Cai, R., Lin, P., & Cheng, S. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106700 · Cited by 23

Feeding Material Identification for a Crusher Based on Deep Learning for Status Monitoring and Fault Diagnosis

Pan, Y., Bi, Y., Zhang, C., Yu, C., Li, Z., & Chen, X. (2022) · Minerals · doi:10.3390/min12030380 · Cited by 23

Self-Supervised Learning and Multisource Heterogeneous Information Fusion Based Quality Anomaly Detection for Heavy-Plate Shape

Li, D., Lu, J., Zhang, T., & Ding, J. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3265649 · Cited by 23

Impact of Deep Learning Optimizers and Hyperparameter Tuning on the Performance of Bearing Fault Diagnosis

Lee, S., & Kim, T. (2023) · IEEE Access · doi:10.1109/access.2023.3281910 · Cited by 25

BiTCN_DRSN: An effective software vulnerability detection model based on an improved temporal convolutional network

Chen, J., Lin, W. Y., Cai, S., Yin, Y., Chen, H., & Towey, D. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111772 · Cited by 23

A meta-learning method for few-shot bearing fault diagnosis under variable working conditions

Zeng, L., Jian, J., Chang, X., & Wang, S. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad28e7 · Cited by 23

Zero-faulty sample machinery fault detection via relation network with out-of-distribution data augmentation

Chen, Z., Huang, H., Wu, J., & Wang, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109753 · Cited by 22

A partial discharge pattern recognition method based on multi-scale adaptive denoising network and Stacking Ensemble Learning

Zheng, S., Liu, J., & Zeng, J. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111392 · Cited by 22

A diffusion model-based deep learning approach for denoising acoustic emission signals in concrete

Fu, W., Zhou, R., Gao, Y., Guo, Z., & Yu, Q. (2025) · Measurement · doi:10.1016/j.measurement.2025.117143 · Cited by 24

Recent progress in artificial intelligence enabled NMR spectroscopy: Methodologies, implementations, quality assessments, and prospects

Zhan, H., Huang, Y., & Chen, Z. (2026) · Applied Physics Reviews · doi:10.1063/5.0277355 · Cited by 22

A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates

Yang, B. Y., Zhang, Y., Wang, S., Xu, W., Xiao, M., He, Y., & Mo, F. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105436 · Cited by 22

Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network

Shi, M., Huang, Z., Xiao, G., Xu, B., Ren, Q., & Zhao, H. (2023) · Sensors · doi:10.3390/s23021008 · Cited by 22

Multimodal Mutual Neural Network for Health Assessment of Power Transformer

Xing, Z., & He, Y. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3237225 · Cited by 23

An Efficient Sequential Embedding ConvNet for Rotating Machinery Intelligent Fault Diagnosis

Tang, J. X., Wú, Q., Li, X., Wei, C., Ding, X., Huang, W., & Shao, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3267376 · Cited by 22

RF-Mic

Chen, Y., Yu, J., Kong, L., Kong, H., Zhu, Y., & Chen, Y. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3596259 · Cited by 22

Dim and Small Space-Target Detection and Centroid Positioning Based on Motion Feature Learning

Su, S., Niu, W., Li, Y., Ren, C., Peng, X., Zheng, W., & Yang, Z. (2023) · Remote Sensing · doi:10.3390/rs15092455 · Cited by 22

Bearing Fault Diagnosis Based on ICEEMDAN Deep Learning Network

Liang, B., & Feng, W. (2023) · Processes · doi:10.3390/pr11082440 · Cited by 22

Interpret what a Convolutional Neural Network learns for fault detection and diagnosis in process systems

Ye, L., Wu, H., Chen, Y., & Fei, Z. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103086 · Cited by 22

Fourier Feature Refiner Network With Soft Thresholding for Machinery Fault Diagnosis Under Highly Noisy Conditions

Wang, H., Luo, W., Liu, Z., Zhang, J., & Zuo, M. J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3363216 · Cited by 22

Intelligent Framework for Bearing Fault Diagnosis in High-Noise Environments: A Location-Focused Soft Threshold Denoising Approach

Yao, D., Zhou, T., & Yang, J. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3362349 · Cited by 22

DCSIAN: A novel deep cross-scale interactive attention network for fault diagnosis of aviation hydraulic pumps and generalizable applications

Fu, S., Zou, L., Wang, Y., Lin, L., Lu, Y., Zhao, M., Guo, F., & Zhong, S. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110246 · Cited by 22

MC-WDWCNN: an interpretable multi-channel wide-kernel wavelet convolutional neural network for strong noise-robust fault diagnosis

Zhou, J., Zhang, X., Jiang, H., Shao, Z., Ma, B., & Zhou, R. F. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad56b8 · Cited by 22

An Adaptive Deep Learning Framework for Fast Recognition of Integrated Circuit Markings

Chen, Z., Zhang, C., Zuo, L., Xiahou, T., & Liu, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3093388 · Cited by 22

Deep Residual Learning With Attention Mechanism for OFDM Channel Estimation

Gao, W., Zhang, W., Liu, L., & Yang, M. (2022) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2022.3232378 · Cited by 21

Multichannel Vibration Signal Fusion Based on Rolling Bearings and MRST-Transformer Fault Diagnosis Model

Wu, X., Peng, H., Cui, X., Guo, T., & Zhang, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3380002 · Cited by 21

A semi-supervised wise-attention weighted prototype network for rolling bearing fault diagnosis under noisy and limited labeled data conditions

Chen, Y., Yue, J., Liu, Z., & Chen, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130563 · Cited by 21

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Yang, J., Gao, T., Jiang, S., Li, S., & Tang, Q. (2020) · Shock and Vibration · doi:10.1155/2020/8880960 · Cited by 22

A novel convolutional neural network with interference suppression for the fault diagnosis of mechanical rotating components

Yang, J., Yin, S., Sun, C., & Gao, T. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07022-w · Cited by 21

Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network

Sun, F. J., Xu, H., Zhao, Y., & Zhang, Y.-D. (2022) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.a2100598 · Cited by 21

Motion intention prediction of upper limb in stroke survivors using sEMG signal and attention mechanism

Li, J., Liang, T., Zeng, Z., Xu, P., Chen, Y., Guo, Z., Liang, Z., & Xie, L. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103981 · Cited by 21

Novel aeroengine fault diagnosis method based on feature amplification

Lin, L., He, W., Fu, S., Tong, C., & Zu, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106093 · Cited by 21

Prior knowledge-based residuals shrinkage prototype networks for cross-domain fault diagnosis

Hu, J., Li, W., Zheng, X., Tian, Z., & Zhang, Y. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ace46c · Cited by 21

Novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in adaptive threshold network

Ye, C., Wang, J., Peng, C. Q., Ju, Z., Geng, X., Zhang, L., Sui, Q., Jiang, M., & Zhang, F. (2023) · Measurement · doi:10.1016/j.measurement.2023.113677 · Cited by 21

SFC-Sup: Robust Two-Stage Underwater Acoustic Target Recognition Method Based on Supervised Contrastive Learning

Zhu, P., Zhang, Y., Huang, Y., Lin, B., Zhu, M., Zhao, K., & Zhou, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3329653 · Cited by 21

A Data-Driven Method for Remaining Useful Life Prediction of Rolling Bearings Under Different Working Conditions

Zhong, X., Song, X., Liu, G., Zhao, W., & Fan, W. (2023) · IEEE Transactions on Reliability · doi:10.1109/tr.2023.3328693 · Cited by 21

Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques

Tao, T., Han, K., Yao, X., Chen, X., Wu, Z., Yao, C., Tian, X., Zhou, Z., & Ren, K. (2024) · Remote Sensing · doi:10.3390/rs16061046 · Cited by 21

Wearable Sensor-Based Residual Multifeature Fusion Shrinkage Networks for Human Activity Recognition

Zeng, F., Guo, M., Long, T., Guo, F., & Liu, X. (2024) · Sensors · doi:10.3390/s24030758 · Cited by 24

Natural Gas Pipeline Leak Detection Based on Dual Feature Drift in Acoustic Signals

Yao, L., Zhang, Y., Wang, L., Li, R., & He, T. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3495787 · Cited by 20

A Self-Supervised Multiview Contrastive Learning Network for the Fault Diagnosis of Rotating Machinery Under Limited Annotation Information

Xu, Y., Lu, X., Gao, T., & Meng, R. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3527612 · Cited by 20

Bearing fault diagnosis method using CNN with denoising structure under strong noise background

Wang, J., Li, H., Liu, X., Sun, B., & Liu, Y. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adaa0a · Cited by 20

MDTCNet: A Novel Multiscale Denoising Transformer Convolutional Network for Fault Diagnosis of Partial Discharge

Zheng, S., Liu, J., & Zeng, J. (2025) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2025.3533472 · Cited by 20

Metric-Based Meta-Learning Relation Network for Cross-Domain Few-Shot Bearing Fault Diagnosis

Gao, W., Xu, Z., & Akoudad, Y. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3547258 · Cited by 25

Synthetic Aperture Radar SAR Image Target Recognition Algorithm Based on Attention Mechanism

Shi, B., Zhang, Q., Dayan, W., & Li, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3118034 · Cited by 20

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Cui, F., Tu, Y., & Gao, W. (2022) · Energies · doi:10.3390/en15113961 · Cited by 21

Fault Diagnosis of Diesel Engine Valve Clearance Based on Wavelet Packet Decomposition and Neural Networks

Kuai, Z., & Huang, G. (2023) · Electronics · doi:10.3390/electronics12020353 · Cited by 20

Explainable fault prediction using learning fuzzy cognitive maps

Mansouri, T., & Vadera, S. (2023) · Expert Systems · doi:10.1111/exsy.13316 · Cited by 21

An Intelligent Edge Diagnosis System Based on Multiplication–Convolution Sparse Network

Wu, Q., Ding, X., Zhang, Q., Liu, R., Siegfried Zhiqiang, W. U., & He, Q. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3304301 · Cited by 20

Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments

Yan, X., Lü, Y., Liu, Y., & Jia, M. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acf8e6 · Cited by 20

Generative Zero-Shot Compound Fault Diagnosis Based on Semantic Alignment

Xu, J., Kong, H., Li, K., & Ding, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3345912 · Cited by 20

Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation

Li, Q., Lu, T., Lai, C., Li, J., Pan, L., Ma, C., Zhu, Y., & Xie, J. (2023) · Energy · doi:10.1016/j.energy.2023.130208 · Cited by 20

Multi-scale deep residual shrinkage networks with a hybrid attention mechanism for rolling bearing fault diagnosis

Zhang, X., Wang, Y., Wei, S., Zhou, Y., & Jia, L. (2024) · Journal of Instrumentation · doi:10.1088/1748-0221/19/05/p05015 · Cited by 20

Continuous Remaining Useful Life Prediction by Self-Guided Attention Convolutional Neural Network and Memory Consciousness Adjustment

Zhou, J., Qi, J., Chen, D., & Qin, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421673 · Cited by 20

Contrastive learning-enabled digital twin framework for fault diagnosis of rolling bearing

Zhang, Y., Zhou, X. H., Gao, C. T., Lin, J., Ren, Z., & Feng, K. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad8f52 · Cited by 20

A fault diagnosis framework using unlabeled data based on automatic clustering with meta-learning

Zhao, Z., Jiao, Y., Xu, Y., Chen, Z., & Zio, E. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109584 · Cited by 20

Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks

Cao, J., Zhang, J., Jiao, X., Yu, P., & Zhang, B. (2024) · Sensors · doi:10.3390/s24144633 · Cited by 19

A multi-source heterogeneous information fusion network for comprehensive assessment of spot welding quality

Dai, W., Sun, X., Zhang, W., Wang, D., Tang, D., & Peng, Y. (2025) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2025.01.033 · Cited by 19

Online Rotor Systems Condition Monitoring Using Nonlinear Output Frequency Response Functions Under Harmonic Excitations

Zhu, Y., Zhao, Y., Lang, Z., Liu, Z., & Liu, Y. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3141866 · Cited by 19

Global Context-Based Threshold Strategy for Drone Identification Under the Low SNR Condition

Chen, Y., Zhu, L., Yao, C., Gui, G., & Lu, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3205065 · Cited by 19

A self-adaptive DRSN-GPReLU for bearing fault diagnosis under variable working conditions

Zhang, Z., Zhang, C., Zhang, X., Chen, L., Shi, H., & Li, H. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac86e3 · Cited by 19

A robust myocardial infarction localization system based on multi-branch residual shrinkage network and active learning with clustering

He, Z., Yuan, S., Zhao, J., Yuan, Z., & Chen, Y. A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104238 · Cited by 19

An Interpretable Multiplication-Convolution Sparse Network for Equipment Intelligent Diagnosis in Antialiasing and Regularization Constraint

Wu, Q., Ding, X., Zhao, L., Liu, R., He, Q., & Shao, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3269122 · Cited by 19

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Liu, Z., Zhang, C., Dong, E., Wang, R., Li, S., & Han, Y. (2023) · Processes · doi:10.3390/pr11071972 · Cited by 19

Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices

Zhao, Z., Chen, F., Gui, Z., Liu, D., & Yang, J. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.119310 · Cited by 19

Deep Residual Shrinkage Networks with Self-Adaptive Slope Thresholding for Fault Diagnosis

Zhang, Z., Li, H., & Chen, L. (2021) · International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO) · doi:10.1109/cmmno53328.2021.9467549 · Cited by 10

A novel residual global context shrinkage network based fault diagnosis method for rotating machinery under noisy conditions

Tong, J., Tang, S., Zheng, J., Zhao, H., & Wu, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad3b29 · Cited by 2

Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks

Zhang, Z., Li, H., Chen, L., & Han, P. (2021) · Shock and Vibration · doi:10.1155/2021/9942249 · Cited by 24

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Yang, J., Gao, T., Jiang, S., Li, S., & Tang, Q. (2020) · Shock and Vibration · doi:10.1155/2020/8880960 · Cited by 21

Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

Zhao, M., Zhong, S., Fu, X., Tang, B., Dong, S., & PECHT, M. G. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2972458 · Cited by 208