Deep Residual Shrinkage Networks for Fault Diagnosis

Deep Residual Learning for Image Recognition

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A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults

Lin, P., Guo, F., Lu, X., Zheng, Q., Cheng, S., Lin, Y., Chen, Z., Wu, L., & Qian, Z. (2023) · Solar Energy · doi:10.1016/j.solener.2023.112155 · Cited by 19

A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint

Zhou, J., & Qin, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3462723 · Cited by 19

Progressive Representation Learning for Real-Time UAV Tracking

Fu, C., Lei, X., Zuo, H., Yao, L., Zheng, G., & Pan, J. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10803050 · Cited by 19

Data Augmentation Fault Diagnosis of Rolling Machinery Using Condition Denoising Diffusion Probabilistic Model and Improved CNN

Zhao, Y., Sheng, T., & Li, D. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3545721 · Cited by 20

Detail-semantic guide network based on spatial attention for surface defect detection with fewer samples

Meng, Y., Xu, H., Ma, Z., Zhou, J., & Hui, D. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03671-5 · Cited by 19

PSNN-TADA: Prototype and Stochastic Neural Network-Based Twice Adversarial Domain Adaptation for Fault Diagnosis Under Varying Working Conditions

Yang, X., Yuan, X., Ye, T., Zhu, W., Zhou, F., & Jin, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3338678 · Cited by 18

Hybrid Data-Driven and Multisequence Feature Fusion Fault Diagnosis Method for Electro-Hydrostatic Actuators of Transport Airplane

Xing, X., Luo, Y., Han, B. B., Qin, L., & Xiao, B. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3523587 · Cited by 21

From data scarcity to diagnostic precision: A novel data augmentation and fault diagnosis framework for district heating substations

van Dreven, J., Cheddad, A., Alawadi, S., Ghazi, A. N., Koussa, J. A., & Vanhoudt, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110662 · Cited by 20

Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation

Zhang, Q., Wu, Z., An, B., Sun, R., & Cui, Y. (2025) · Sensors · doi:10.3390/s25092775 · Cited by 18

Fault diagnosis of rolling bearing based on singular spectrum analysis and wide convolution kernel neural network

Zhu, R., Wang, M., Xu, S., Li, K., Han, Q., Tong, X., & He, K. (2022) · Journal of low frequency noise, vibration and active control · doi:10.1177/14613484221104639 · Cited by 18

EEG-Based Emotion Identification Using 1-D Deep Residual Shrinkage Network With Microstate Features

Wang, J., Song, Y., Mao, Z., Liu, J., & Gao, Q. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3239507 · Cited by 18

Fault Diagnosis With Bidirectional Guided Convolutional Neural Networks Under Noisy Labels

Zhang, K., Li, Z., Zheng, Q. S., Ding, G., Tang, B., & Zhao, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3289948 · Cited by 18

Transmission dynamics informed neural network with application to COVID-19 infections

He, M., Tang, B., Xiao, Y., & Tang, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107431 · Cited by 18

DRSN-GAF: Deep Residual Shrinkage Network (DRSN) for Lithology Classification Through Well Logging Data Transformed by Gram Angle Field

Sun, Y., Pang, S., Zhang, J., & Zhang, Y.-A. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3346382 · Cited by 18

Source-free cross-domain fault diagnosis of rotating machinery using the Siamese framework

Ma, C., Tu, X., Zhou, G., Huang, Y., & Ding, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112179 · Cited by 18

Making transformer hear better: Adaptive feature enhancement based multi-level supervised acoustic signal fault diagnosis

Wang, S., Xu, Q., Zhu, S., & Wang, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125736 · Cited by 20

Alzheimer’s disease image classification based on enhanced residual attention network

Li, X., Gong, B., Chen, X., Li, H., & Yuan, G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317376 · Cited by 18

MF-MSRNet: a fault diagnosis method for train bogie bearing based on multi-source data fusion and multi-scale residual network

Sun, H., He, D., Lao, Z., Jin, Z., Wei, Z., & Wu, J. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2542388 · Cited by 18

Pose-guided part matching network via shrinking and reweighting for occluded person re-identification

Wang, H., Chen, X., & Liu, C. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104186 · Cited by 18

Efficient Residual Shrinkage CNN Denoiser Design for Intelligent Signal Processing: Modulation Recognition, Detection, and Decoding

Zhang, L., Yang, X., Liu, H., Zhang, H., & Cheng, J. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126074 · Cited by 18

A Lightweight Multi-Source Fast Android Malware Detection Model

Peng, T., Hu, B., Liu, J., Huang, J., Zhang, Z., He, R., & Hu, X. (2022) · Applied Sciences · doi:10.3390/app12115394 · Cited by 20

SA1D-CNN: A Separable and Attention Based Lightweight Sensor Fault Diagnosis Method for Solar Insecticidal Lamp Internet of Things

Yang, X. K., Shu, L., Li, K., Huo, Z., & Zhang, Y. (2022) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2022.3172899 · Cited by 20

Adversarial-Causal Representation Learning Networks for Machine fault diagnosis under unseen conditions based on vibration and acoustic signals

Wu, F., Xiang, Z., Xiao, D., Hao, Y., Qin, Y., Pu, H., & Luo, J. T. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109550 · Cited by 17

Collaborative and trustworthy fault diagnosis for mechanical systems based on probabilistic neural network with decision-level information fusion

Xu, Z., Zhao, K., Zhang, W., Miao, W., Sun, K., Wang, J., & Bashir, M. B. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100854 · Cited by 19

Mechanism-driven data model for tool condition monitoring

Wang, S., Li, S., Lin, X., Shi, Y., & Zhu, K. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113042 · Cited by 17

A fault diagnosis method for complex chemical process based on multi-model fusion

He, Y., Yang, Z., Dong, W., Chengdong, G., Li, C., & Yian, G. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2022.06.029 · Cited by 18

Fault diagnosis of rolling bearings with noise signal based on modified kernel principal component analysis and DC‐ResNet

Zhao, Y., Zhou, M., Xu, X., & Zhang, N. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12173 · Cited by 17

A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions

Weng, C., Lu, B., Gu, Q., & Zhao, X. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-08405-x · Cited by 17

BO-MADRSN: Bayesian optimized multi-attention residual shrinkage networks for industrial soft sensor modeling

Xu, J., Gao, S., Dang, X., Zhao, W., Zhang, Q., & Qiu, S. (2023) · Measurement · doi:10.1016/j.measurement.2023.113477 · Cited by 18

Smart Contract Vulnerability Detection Based on Multi-Scale Encoders

Guo, J., Lu, L., & Li, J. (2024) · Electronics · doi:10.3390/electronics13030489 · Cited by 17

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

Li, L., Li, Y. G., & Zhang, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad282e · Cited by 17

Fault Diagnosis Method of Wind Turbine Rolling Bearing Based on Improved Deep Residual Shrinkage Network

Wenbin, B., Deng, A., Dongchuan, L., Min, Z., Liu, Y., & Li, J. (2023) · Journal of Mechanical Engineering · doi:10.3901/jme.2023.12.202 · Cited by 17

Prior-knowledge-guided mode filtering network for interpretable equipment intelligent diagnosis under varying speed conditions

Liu, R., Ding, X., & Shao, Y. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102493 · Cited by 17

Meta-Learning With Intraclass and Interclass Optimization for Few-Shot Fault Diagnosis

Li, K., Ye, H., Gao, X., & Zhang, L. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3458091 · Cited by 17

Compact Modelling of Electrical, Optical and Thermal Properties of Multi-Colour Power LEDs Operating on a Common PCB

Górecki, K., & Ptak, P. P. (2021) · Energies · doi:10.3390/en14051286 · Cited by 17

Interrupted sampling repeater jamming suppression based on multiple extended complex‐valued convolutional auto‐encoders

Meng, Y., Yu, L., & Wei, Y. (2024) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12568 · Cited by 16

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution

Liu, W., Zhang, Z., Ye, Z., & He, Q. (2024) · Scientific Reports · doi:10.1038/s41598-024-75522-x · Cited by 16

Physical-Knowledge-Guided and Interpretable Deep Neural Networks for Gear Fault Severity Level Diagnosis

Li, J., Wang, X., Chen, H., & Yang, Z.-X. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3547004 · Cited by 16

A deep-learning model with improved capsule networks and LSTM filters for bearing fault diagnosis

Zhang, X., Kong, J., Zhao, Y., Qian, W., & Xu, X. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02340-x · Cited by 16

A novel distinction method of metal debris material based on inductive sensor with multi-sensing units

Shi, H., Xie, Y., Wang, C., Liu, Y., Zhang, H., Li, W., Zhang, S., He, R., Sun, Y., Li, G., et al. (2022) · Tribology International · doi:10.1016/j.triboint.2022.107948 · Cited by 16

Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis

Jiang, Q., Bao, B., Hou, X., Huang, A., Jiang, J., & Mao, Z. (2023) · Applied Sciences · doi:10.3390/app13020718 · Cited by 16

A multi-stream multi-scale lightweight SwinMLP network with an adaptive channel-spatial soft threshold for online fault diagnosis of power transformers

Liu, X., & He, Y. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acc2db · Cited by 16

Mutual dimensionless improved bearing fault diagnosis based on Bp-increment broad learning system in computer vision

Li, C., Hu, Q., Zhao, S., Wu, J., & Xiong, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107508 · Cited by 16

A novel two-stream multi-head self-attention convolutional neural network for bearing fault diagnosis

Ren, H., Liu, S., Wei, F., Qiu, B., & Zhao, D. (2023) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062231216877 · Cited by 17

Manifold assistant multi-modal multi-objective differential evolution algorithm and its application in actual rolling bearing fault diagnosis

Yang, X., Yuan, X., Dong, L., Mei, X., & Chen, K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108040 · Cited by 16

Improving Short-Term Photovoltaic Power Generation Forecasting with a Bidirectional Temporal Convolutional Network Enhanced by Temporal Bottlenecks and Attention Mechanisms

Gan, J., Lin, X., Chen, T., Fan, C., Wei, P., Li, Z., Huo, Y., Zhang, F., Liu, J., & He, T. (2025) · Electronics · doi:10.3390/electronics14020214 · Cited by 16

Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep Learning

Hou, J., Zhang, B., Zhong, Y., & He, W. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16020062 · Cited by 17

Multi-target prediction and dynamic interpretation method for displacement of arch dam with cracks based on A-DSRSN and SHAP

Xu, B., Zhang, H., Gu, C., Chen, Z., & Gu, H. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103467 · Cited by 16

Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network

Zhang, S., Pan, J., Han, Z., & Guo, L. (2021) · Sensors · doi:10.3390/s21237973 · Cited by 16

Adaptive nearest neighbor reconstruction with deep contractive sparse filtering for fault diagnosis of roller bearings

Qian, W., Li, S., & Lu, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104749 · Cited by 16

A fault diagnosis method for rolling bearings based on RDDAN under multivariable working conditions

Shi, H., Gan, C., Zhang, X., Meng, W., & Huang, C. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac9854 · Cited by 16

Predicting epileptic seizures based on EEG signals using spatial depth features of a 3D-2D hybrid CNN

Qi, N., Piao, Y., Yu, P., & Tan, B. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02792-4 · Cited by 16

An Improved Conditional Generative Adversarial Method for Wind Turbine Gearbox Fault Diagnosis With Imbalance Data

Xiangjun, Z., Lingqin, X., Chen, F., & Yang, M. Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3466895 · Cited by 15

You can be more trustworthy: A feature fusion reinforcement network for credible anti-noise fault diagnosis

Wei, Y., Peng, H., Rong, M., Gu, X., & Chen, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103056 · Cited by 15

Multiscale feature enhanced gating network for atrial fibrillation detection

Wu, X., Yan, M., Wang, R., & Xie, L. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108606 · Cited by 15

A Novel Radio Frequency Fingerprint Identification Scheme for Few-Shot Open-Set Recognition

Xie, W., Wang, H., Shen, Z., Li, X., Liu, Z., & Jiang, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3559183 · Cited by 15

Time-frequency perception guided multi-level contrastive learning for rotating machinery fault diagnosis

Shu, Z., Peng, D., Wang, H., Mao, C., & Yu, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128664 · Cited by 15

FedRad: A Dynamic Low-Cost Federated Learning for Fault Diagnosis of Rotating Machinery

Xiao, D., Diao, J., Ding, J., Jiang, L., & Zhao, C. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3593608 · Cited by 15

Supervised Contrastive Learning Enhanced Deep Residual Shrinkage Network for Dual Uncertainty-Aware Bearing RUL Prediction

Ye, Y., Wang, J., Yang, J., Yao, D., & Lu, Y.-C. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3587999 · Cited by 15

A Lightweight Deep Learning Model for Automatic Modulation Classification Using Dual-Path Deep Residual Shrinkage Network

Suman, P., & Qu, Y. (2025) · AI · doi:10.3390/ai6080195 · Cited by 15

1D-DRSETL: a novel unsupervised transfer learning method for cross-condition fault diagnosis of rolling bearing

Tong, J., Liu, C., Zheng, J., Pan, H., Wang, X., & Bao, J. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac6f46 · Cited by 16

Few-shot rolling bearing fault classification method based on improved relation network

Kang, S., Liang, X., Wang, Y., Wang, Q., Qiao, C., & Микулович, В. И. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac8ca6 · Cited by 15

A novel convolutional network with a self-adaptation high-pass filter for fault diagnosis of wind turbine gearboxes

Yang, F., Huang, D., Li, D., Zhao, Y., Lin, S., & Muyeen, S. M. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac991f · Cited by 15

Fault diagnosis of EHA with few-shot data augmentation technique

Chen, H., Xu, M., Mao, W., Zhao, S., Yang, G., & Yan, B. (2023) · Smart Materials and Structures · doi:10.1088/1361-665x/acc0ed · Cited by 15

Rock thin section image classification based on depth residuals shrinkage network and attention mechanism

Zhang, X., Wang, Z., Liu, D., Sun, Q. H., & Wang, J. (2023) · Earth Science Informatics · doi:10.1007/s12145-023-00981-1 · Cited by 15

Deep learning seismic damage assessment with embedded signal denoising considering three-dimensional time–frequency feature correlation

Su, Z., Yu, J., Xiao, X., Wang, J., & Wang, X. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116148 · Cited by 15

Shrinkage and Redundant Feature Elimination Network-Based Robust Image Zero-Watermarking

He, L., He, Z., Luo, T., & Song, Y. (2023) · Symmetry · doi:10.3390/sym15050964 · Cited by 15

Series Arc Fault Detection Based on Multimodal Feature Fusion

Qu, N., Wei, W., & Hu, C. (2023) · Sensors · doi:10.3390/s23177646 · Cited by 15

RFIS-HI: a new health indicator for quantitative condition monitoring of the bearing under variable speed conditions

Ma, W., Yu, Y., Guo, L., Qian, M., & Gao, H. (2023) · Structural Health Monitoring · doi:10.1177/14759217231203244 · Cited by 15

Fast ship radiated noise recognition using three-dimensional mel-spectrograms with an additive attention based transformer

Wang, Y., Zhang, H., & Huang, W. K. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1280708 · Cited by 16

STA-Former: enhancing medical image segmentation with Shrinkage Triplet Attention in a hybrid CNN-Transformer model

Liu, Y., Han, L., Yao, B., & Li, Q. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02893-5 · Cited by 15

An Interpretable CNN With Wavelet Group Policy Embedded for Intelligent Fault Diagnosis

Han, G., Chen, J., Liu, L., Wang, Z., Zhang, F., & Abudurexiti, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3368479 · Cited by 15

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

Zhang, D., & Tao, H. (2024) · Applied Sciences · doi:10.3390/app14083289 · Cited by 15

Multimodel Fusion Health Assessment for Multistate Industrial Robot via Fuzzy Deep Residual Shrinkage Network and Versatile Cluster

Jiang, W., Wu, J., Zhu, H., & Gao, L. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3411389 · Cited by 15

Fault detection using Graph Neural Differential Auto-encoders (GNDAE)

Goswami, U., Kodamana, H., & Ramteke, M. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108775 · Cited by 15

Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating

Zhao, Z., Jiao, Y., Xu, Y., Chen, Z., & Zhao, R. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102875 · Cited by 15

The DRTS-Net Framework: Enhanced Probabilistic Health Perception for Wind Turbine Bearings Under Complex Operating Conditions With Improved Interpretability

Yang, T., Guanghong, L. I., Huo, J., Li, X., Zhou, X., & Han, Q. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3545504 · Cited by 15

A Fault Diagnosis Method With Bitask-Based Time- and Frequency-Domain Feature Learning

Zhang, Q., Huo, R., Zheng, H., Huang, T., & Jie, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3305652 · Cited by 15

A novel anomaly detection method for gas turbines using weight agnostic neural network search

Zhong, S., Liu, D., Lin, L., Zhao, M., Fu, X., & Guo, F. (2020) · Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) · doi:10.1109/aparm49247.2020.9209368 · Cited by 15

A supervised contrastive learning method based on online complement strategy for long-tailed fine-grained fault diagnosis

Zhao, Z., Jiao, Y., Xu, Y., & Zhao, R. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103079 · Cited by 14

Milling machine fault detection and identification based on a novel vitality index and temporal-residual network

Ahmad, Z., Ullah, S., Maliuk, A. S., & Kim, J.-M. (2025) · Applied Acoustics · doi:10.1016/j.apacoust.2025.110861 · Cited by 14

Short-impending decision-making for TBM tunneling control based on loading phase data

Fu, K., Xue, Y., Qiu, D., & Wang, P. (2025) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2025.106819 · Cited by 14

Multiscale calibration networks with pseudo label for bearing fault diagnosis under class-imbalanced data and multi-rate sampling scenarios

Liu, Z., Dong, Z., Liu, H., Zhong, P., Jia, W., & Tan, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103694 · Cited by 14

Wide Residual Relation Network-Based Intelligent Fault Diagnosis of Rotating Machines with Small Samples

Chen, Z., Wang, Y., Wu, J., Deng, C., & Jiang, W. (2022) · Sensors · doi:10.3390/s22114161 · Cited by 14

PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm

Li, K., Zhuang, Y., Lai, J., & Zeng, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3244981 · Cited by 14

ResGait: gait feature refinement based on residual structure for gait recognition

Gao, S., Tan, Z., Ning, J., Hou, B., & Li, L. (2023) · The Visual Computer · doi:10.1007/s00371-023-02973-0 · Cited by 14

Adaptive Multi-Dimensional Shrinkage Block for Automatic Modulation Recognition

Wei, T., Li, Z., Bi, D., Shao, Z., & Gao, J. (2023) · IEEE Communications Letters · doi:10.1109/lcomm.2023.3314623 · Cited by 14

Bearing Fault Diagnosis based on Convolution Neural Network with Logistic Chaotic Map

Zhang, F., Chen, L., Dai, Y., Kou, L., Ji, P., & Liu, Y. (2024) · Advanced Theory and Simulations · doi:10.1002/adts.202301090 · Cited by 14

Acoustic tomography temperature reconstruction based on improved sparse reconstruction model and multi-scale feature fusion network

Dong, X., Zhang, L., Qian, L., Wu, C., Tang, Z. G., & Li, A. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108168 · Cited by 14

Distribution Character-Guided Projection Replay Network for Class-Incremental Fault Diagnosis of Rotating Machinery

Zhou, Z., Su, Z., Jiang, W., Zhu, J., Yu, H. Z., & Hu, F. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3371695 · Cited by 14

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Wei, M. Z., Sun, X., & Zong, J. (2024) · Applied Sciences · doi:10.3390/app14114496 · Cited by 14

Semisupervised Graph Contrastive Learning for Process Fault Diagnosis

Jia, M., Yang, C., Liu, Q., Gao, Z., & Liu, Y. (2024) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c01337 · Cited by 14

EHOPN: A novel enhanced high-order pooling-based network for industrial fault detection

Zhang, T., Li, D., Li, C., & Ding, J. (2024) · Journal of Process Control · doi:10.1016/j.jprocont.2024.103296 · Cited by 14

SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM)

Xu, Z., Liu, T., Xia, Z., Fan, Y., Yan, M., & Dang, X. (2024) · Sensors · doi:10.3390/s24196237 · Cited by 14

An interpretable evolutionary broad learning system for damage identification in aircraft structures using Lamb waves

Chen, G., Shao, W., Tang, F., & Sun, H. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113577 · Cited by 14

Adaptive Transfer Learning Based on a Two-Stream Densely Connected Residual Shrinkage Network for Transformer Fault Diagnosis over Vibration Signals

Liu, X., He, Y., & Wang, L. (2021) · Electronics · doi:10.3390/electronics10172130 · Cited by 14

A multi-scale kernel-based network with improved attention mechanism for rotating machinery fault diagnosis under noisy environments

Weng, C., Lu, B., & Gu, Q. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac4598 · Cited by 14

Magnetotelluric Noise Attenuation Using a Deep Residual Shrinkage Network

Zuo, G., Ren, Z., Xiao, X., Tang, J., Zhang, L., & Li, G. (2022) · Minerals · doi:10.3390/min12091086 · Cited by 14

Semi-supervised fault diagnosis of bearings under noisy environments with limited labeled samples

Zhao, W., Gao, J., Cheng, Q., Yuan, Y., & Zhou, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130843 · Cited by 13

Lightweight and Robust Cross-Domain Microseismic Signal Classification Framework with Bi-Classifier Adversarial Learning

Song, D., Dai, F., Liu, Y., Tan, H. X., & Wei, M. (2025) · Engineering · doi:10.1016/j.eng.2025.10.023 · Cited by 13

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