Deep Residual Shrinkage Networks for Fault Diagnosis

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

Untitled work

(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

Untitled work

(2014) · arXiv (Cornell University) · Cited by 942

Supervised Contrastive Learning for Multisensor Signals Classification in Automobile Engine Manufacturing

Cho, Y. S., & Kim, S. B. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3363189 · Cited by 11

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Zuo, F., Zhang, D., Li, L., He, Q., & Deng, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32087 · Cited by 9

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Zhang, Y., Yan, X., Xiao, P., Zou, J., & Hu, L. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad7f74 · Cited by 9

A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis

Zhang, Z., Xue, C., Li, X., Wang, Y., & Wang, L. (2024) · Applied Sciences · doi:10.3390/app14199116 · Cited by 9

MSTD: a framework for rolling bearing fault diagnosis based on multi-scale and soft-threshold denoising

Qiao, Z., Yao, D., Yang, J., Zhou, T., & Ge, T. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2430384 · Cited by 9

Pollution Source Detection With Low‐Cost Low‐Accuracy Sensors Through Coupling Forward Data Assimilation and Inverse Optimization

Zhang, C., Zhu, Z., Li, Y., Du, E., Sun, Y., & Liu, Z. (2024) · Water Resources Research · doi:10.1029/2023wr036834 · Cited by 9

Severity Estimation of Inter-Turn Short-Circuit Fault in PMSM for Agricultural Machinery Using Bayesian Optimization and Enhanced Convolutional Neural Network Architecture

Wang, M., Lai, W., Sun, P., Hong, L., & Song, Q. (2024) · Agriculture · doi:10.3390/agriculture14122214 · Cited by 9

Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios

Zhang, C., Luo, S., Cao, S. C., Zhang, Y., Hua, C., Jiang, C., & Zhou, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2434767 · Cited by 9

A lightweight and rapidly converging transformer based on separable linear self-attention for fault diagnosis

Yin, K., Chen, C., Shen, Q., & Ji, D. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9f89 · Cited by 9

Spatiotemporal Transform Network-Based Anomaly Detection and Localization of Distributed Parameter Systems

Wei, P., Zhu, W., Yang, Y., Fei, Z., & Xie, C. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3435411 · Cited by 9

High Precision Defect Sizing Method for Capacitive Imaging Based on Physics- Informed Neural Network

Fan, G., Yin, X., Zhao, M., Mwelango, M., Yuan, X., & Li, W. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3523588 · Cited by 9

M²-Net: Multitask-Learning-Based Multiband Signal Recognition Network

Zhang, X., Wang, P., Ma, Y., Jiao, J., Wu, S., & Zhang, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3535744 · Cited by 9

A Systematic Review of Heart Sound Detection Algorithms: Experimental Results and Insights

Bi, X., Tang, S., Xiao, B., Li, W., Gao, X., & Lió, P. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547082 · Cited by 9

A Novel Dual-Domain Adversarial Method for Vibration Signal Denoising in Bearing Fault Diagnosis

Han, G., Shen, J., Wang, Z., Zhu, Y., & Xie, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551836 · Cited by 9

Rolling bearings fault diagnosis using a one-dimensional vision transformer with multi-scale residual convolution

Tang, Y., Jin, T., & Li, C. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/add0ce · Cited by 9

A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time

Ma, J., Wang, L., Wu, R., Zhang, N., Wei, J., Li, J., Li, Q., Tan, L., Li, G., Jiang, N., et al. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01639-3 · Cited by 9

Knowledge vortex network for continuous bearing remaining useful life prediction

Zhou, J., Chen, Y., & Qin, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103577 · Cited by 9

Physically interpretable Stockwell weight initialization and adaptive fusion average threshold for intelligent fault diagnosis of rolling bearing under noisy environment

Zhang, L., Hu, J., Liang, P., Xu, X., Li, G., Xie, Z., & Wang, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111916 · Cited by 9

A two-phase federated learning framework for machinery fault diagnosis with cloud-edge collaborative computing

Zhang, Y., Yin, H., Wang, R., & Zhang, T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129323 · Cited by 10

FDFNet: Feature-decision dual fusion network for intelligent fault diagnosis of rotating machinery under varying speed conditions

Deng, Q., Zhang, Z., Tu, X., Wang, Z., & Wu, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112423 · Cited by 9

Advancing underwater acoustic target recognition in low-SNR environments with UATR-DIFF-transformer

Deng, S., & Hong, F. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.122668 · Cited by 9

A novel multi-scale dense residual shrinkage GAN for data-limited rotating machinery fault diagnosis

Yi, T., Guo, J., Yuan, F., Shi, Y., Lei, W., Liu, D., Guo, Z., & Ling, Y. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.113906 · Cited by 9

ThingNet: A Lightweight Real-time Mirai IoT Variants Hunter through CPU Power Fingerprinting

Li, Z., & Zhao, D. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774536 · Cited by 9

Waveform Reconstruction of DSSS Signal Based on VAE‐GAN

Feng, Q., Zhang, J., Chen, L., & Liu, F. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/3667592 · Cited by 9

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

Du, D., Wu, L., Zhang, C., Fei, Z., Yang, L., Fei, M., & Zhou, H. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3206760 · Cited by 10

Variational Bayesian Learning With Reliable Likelihood Approximation for Accurate Process Quality Evaluation

Wang, S., Wang, Y., Yang, B. Y., Mo, F., & Zhang, Z. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3264288 · Cited by 9

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques

Gong, Z., Chen, C., Chen, C., Li, C., Tian, X., Gong, Z., & Lv, X. (2023) · Analytica Chimica Acta · doi:10.1016/j.aca.2023.341758 · Cited by 9

Hierarchical dynamic aggregation graph modeling of vibration for semi-supervised fault diagnosis of power transformer

Liu, X., & He, Y. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0a5b · Cited by 9

Research on Malware Detection Technology for Mobile Terminals Based on API Call Sequence

Yao, Y., Zhu, Y., Jia, Y., Shi, X., Zhang, L., Dong, Z., & Junhua, D. (2023) · Mathematics · doi:10.3390/math12010020 · Cited by 9

Novel multiple object tracking method for yellow feather broilers in a flat breeding chamber based on improved YOLOv3 and deep SORT

Zou, X., Yin, Z., Li, Y., Gong, F., Bai, Y., Zhao, Z., Zhang, W., Qian, Y., & Xiao, M. (2023) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20231605.7836 · Cited by 9

A Decision Fusion SWT-RF Method for Rolling Bearing Enhanced Diagnosis Under Low-Quality Data

Chen, J., Lin, C., Lu, Q., Yang, C., Li, P., Yu, P., & Ge, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3350130 · Cited by 9

A convolutional neural-network-based diagnostic framework for industrial bearing

Yu, B., & Xie, C.-L. (2024) · Mechanical sciences · doi:10.5194/ms-15-87-2024 · Cited by 9

Surface hardness determination of laser cladding using laser-induced breakdown spectroscopy and machine learning (PLSR, CNN, ResNet, and DRSN)

Yang, J., Kong, L., & Ye, H. (2024) · Applied Optics · doi:10.1364/ao.516603 · Cited by 9

Fault Diagnosis of Imbalance and Misalignment in Rotor-Bearing Systems Using Deep Learning

Liu, F., Li, W., Wu, Y., He, Y., & Li, T. (2024) · Polish Maritime Research · doi:10.2478/pomr-2024-0011 · Cited by 10

An Open-Set Classification Method With Small Samples for Rotating Machinery

Han, G., Xie, Y., Wang, Z., & Zhu, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3481570 · Cited by 9

A Noise-Resilient Fault Diagnosis Method Based on Optimized Residual Networks

Chen, Z., Liu, J., Du, Z., Fan, X., & Luo, H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3563056 · Cited by 9

Multi-Task Learning-Based Channel Estimation for RIS Assisted Multi-User Communication Systems

Xie, W., Xiao, J. M., Zhu, P. Y., & Yu, C. T. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3138082 · Cited by 9

Inception module and deep residual shrinkage network-based arc fault detection method for pantograph–catenary systems

Li, B., & Cui, F. (2022) · Journal of Power Electronics · doi:10.1007/s43236-022-00415-z · Cited by 9

Convolutional Shrinkage Neural Networks Based Model-Agnostic Meta-Learning for Few-Shot Learning

He, Y., Zang, C., Zeng, P., Dong, Q., Liu, D., & Liu, Y. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10894-7 · Cited by 9

Microseismic Events Recognition via Joint Deep Clustering With Residual Shrinkage Dense Network

Feng, Q., Han, L., Zhao, B., & Li, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3314149 · Cited by 9

Complex augmented representation network for transferable health prognosis of rolling bearing considering dynamic covariate shift

Cao, Y., Jia, M., Zhao, X., Yan, X., & Feng, K. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109692 · Cited by 9

Image Motion Deblurring Based on Deep Residual Shrinkage and Generative Adversarial Networks

Jiang, W., & Liu, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5605846 · Cited by 9

Efficient Conflict-Filtered Network for Defect Detection

Zheng, Y., Lyu, W., Wang, C., Guo, Q., Zhou, D., & Xu, W. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3293557 · Cited by 8

Deep learning for sub-Nyquist sampling scanning white light interferometry

Liang, H., Shen, H., Liu, P., Dong, M., Yan, C., Meng, L., & Yao, D. (2023) · Optics Letters · doi:10.1364/ol.503696 · Cited by 8

Identification of Partial Discharge Based on Composite Optical Detection and Transformer-Based Deep Learning Model

Guo, J., Zhao, S., Huang, B., Wang, H., He, Y., Zhang, C., Zhang, C., & Shao, T. (2024) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2024.3382320 · Cited by 8

Self-learning guided residual shrinkage network for intelligent fault diagnosis of planetary gearbox

Lv, X., Wang, J., Qin, R., Bao, J., Jiang, X., Zhang, Z., Han, B., & Jiang, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109603 · Cited by 8

FTAN: Feature Transform and Alignment Network for cross-domain specific emitter identification

Xiao, Z., Zhang, X., Sun, G., & Shao, H. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109800 · Cited by 8

Are Novel Deep Learning Methods Effective for Fault Diagnosis?

Jiang, D., He, C., Chen, Z., & Zhao, J. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3510387 · Cited by 8

ESTMST-ST: An End-to-End Soft Threshold and Multiloss Self-Distillation Based Swin Transformer for Underwater Acoustic Signal Recognition

Wu, F., Yao, H., Zhongda, Z., Zhao, X., Zang, Y., & Wang, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3520860 · Cited by 8

Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA model

Liu, L., Zhao, Y., Hu, Y., Ma, Y., & Guo, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-89576-y · Cited by 9

SMSDDNet: An enhanced network for slender and multi-scale defects detection in automated fiber placement using novel feature extraction and loss function

Wen, B., Jia, S., Han, M., Zhou, X., Zhang, H., Lin, Z., Wang, L., Li, Y., & Xu, Y. (2025) · Measurement · doi:10.1016/j.measurement.2025.117161 · Cited by 9

Deep learning-based adaptive denoising method for prediction of crack opening displacement of rock from noisy strain data

Zhao, S., Tan, D., Wang, J., & Yin, J. (2025) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2025.106112 · Cited by 8

Multi-variety monitoring of potato late blight severity using UAV data with improved SMOTE-CS for small sample modeling and deep feature learning

Sun, H., Mai, H., Mao, Y., Li, Q., Guo, M., Liu, Y., Feng, Z., Feng, H., Guo, W. F., Yang, G., et al. (2025) · European Journal of Agronomy · doi:10.1016/j.eja.2025.127702 · Cited by 8

A Dual-Attentive Multimodal Fusion Method for Fault Diagnosis Under Varying Working Conditions

Chu, Y., Zhu, L., & Lu, M. (2025) · Mathematics · doi:10.3390/math13111868 · Cited by 11

An order sparse filtering network for interpretable smart gear edge diagnosis under varying speed conditions

Wu, Q., Ding, X., Gao, S. H., Luo, J. T., & Huang, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103516 · Cited by 8

Multi-Scale Fusion Network Using Time-Division Fourier Transform for Rolling Bearing Fault Diagnosis

Wang, R., Sun, S., Zhao, P., Yang, X., Wei, X., & Hu, C. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.066212 · Cited by 8

Self-supervised time–frequency feature alignment for process monitoring of cyber–physical CNC machines

Xu, Y., Ji, J., Sun, Y., Huang, S., Zhao, Z., & Huang, G. Q. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.08.007 · Cited by 8

Research on noise-robust fault diagnosis method for rotating machinery in dynamic environments based on multi-scale convolution

Guo, J., Wang, S., Guan, W., Wang, G., Liu, Z., & Wu, S. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11722-y · Cited by 8

A Frequency Offset Decoupled Radio Frequency Fingerprint Identification Scheme for LoRa Devices

Yu, Z., Ge, H., Tang, X., & Tang, L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3612500 · Cited by 8

HDNAT: A Transformer-Based Fault Diagnosis Model for Rotating Machinery Across Varying Damage Degrees

Gao, W., Xie, X., Fu, D., Yu, J., & Akoudad, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3623791 · Cited by 8

A quantized subtraction-convolution network for industrial lightweight edge interpretable diagnosis

Wu, Q., Luo, J., Huang, W., & Ding, X. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112241 · Cited by 8

Weld signals driven abnormal quality detection based on a conditional differential denoising variational autoencoder for multi-pulse spot welding

Wu, Y.-L., Yang, B., Wang, S., Zhang, Y., Zhang, F., & Wang, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113615 · Cited by 8

Adaptive feature denoising based deep convolutional network for single image super-resolution

Cheng, R., Wu, Y., Wang, J., Ma, M., Niu, Y., & Shi, G. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103518 · Cited by 8

Grayscale self-adjusting network with weak feature enhancement for 3D lumbar anatomy segmentation

Liu, J., Cui, Z., Desrosiers, C., Lu, S., & Zhou, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102567 · Cited by 8

Image Reconstruction Based on Multilevel Densely Connected Network With Threshold for Electrical Capacitance Tomography

Wang, Z., Zhang, L., Zhang, B., Zhao, J., & Wu, S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3211708 · Cited by 8

Deep Learning for Inverse Design of Broadband Quasi-Yagi Antenna

Zhou, W.-Y., Mei, Z., Lu, M., & Zhu, Y.-B. (2023) · International Journal of RF and Microwave Computer-Aided Engineering · doi:10.1155/2023/7819156 · Cited by 8

Environmental Knowledge-Driven Over-the-Horizon Propagation Loss Prediction Based on Short- and Long-Term Parallel Double-Flow TrellisNets

Wu, J., Wei, Z., Zhang, J., Guo, X., Jia, D., Xu, J., Zhang, Y., Li, Q., & Zhou, H. (2023) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2023.3273332 · Cited by 9

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Yang, P., Li, W., Wen, C., & Liu, P. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0611 · Cited by 8

Cross-condition quantitative diagnosis method for bearing faults based on IDRSN-ECDAN

He, S., Zhu, L., Li, H., Hu, C., Bao, J., & Mo, Q. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0ca7 · Cited by 8

Liquid-filled pipeline leak detection and localization based on multi-scale residual networks

Zhao, S.-L., Zhou, L., Liu, S., Dong, L., Hong, Z., Zhao, D., & Guo, C. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad2740 · Cited by 8

A small sample bearing fault diagnosis method based on novel Zernike moment feature attention convolutional neural network

Zhao, Y., & Jun, X. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad3411 · Cited by 8

A novel approach based on spatio-temporal attention and multi-scale modeling for mechanical failure prediction

Zhai, W., Fu, W., Qin, J., Ma, Q., & Kang, Y. (2024) · Control Engineering Practice · doi:10.1016/j.conengprac.2024.105938 · Cited by 8

APEIOU Integration for Enhanced YOLOV7: Achieving Efficient Plant Disease Detection

Zhao, Y., Lin, C., Wu, N., & Xu, X. (2024) · Agriculture · doi:10.3390/agriculture14060820 · Cited by 8

Automatic Modulation Recognition Method Based on Phase Transformation and Deep Residual Shrinkage Network

Chen, H., Guo, W., Kang, K., & Hu, G. (2024) · Electronics · doi:10.3390/electronics13112141 · Cited by 8

Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

Kun, Z., Hongren, L., Wang, X., Xie, D., & Yang, S. (2024) · IEEE Access · doi:10.1109/access.2024.3434708 · Cited by 8

Multiscale dynamically parallel shrinkage network for fault diagnosis of aviation hydraulic pump and its generalizable applications

Fu, S., Lin, L., Wang, Y., Zhao, M., Guo, F., Zhong, B., & Zhong, S. (2024) · ISA Transactions · doi:10.1016/j.isatra.2024.08.009 · Cited by 8

Random convolution layer: an auxiliary method to improve fault diagnosis performance

Zhao, Z., Zhao, R., & Jiao, Y. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02458-4 · Cited by 8

Improving lithofacies prediction in lacustrine shale by combining deep learning and well log curve morphology in Sanzhao Sag, Songliao Basin, China

Wu, X., Xu, H., Zhou, H., Wang, L., Jiang, P., & Wu, H. (2024) · Computers & Geosciences · doi:10.1016/j.cageo.2024.105735 · Cited by 8

Fault diagnosis of the harmonic reducer based on dual-path convolutional network with multi-channel hybrid attention mechanism

Li, K., Yang, R., Wei, T., Yang, Y., & Xiang, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad8179 · Cited by 8

Multi-Scale Channel Mixing Convolutional Network and Enhanced Residual Shrinkage Network for Rolling Bearing Fault Diagnosis

Li, X., Chen, J., Wang, J., Wang, J., Wang, J., Li, X., & Kan, Y. (2025) · Electronics · doi:10.3390/electronics14050855 · Cited by 8

A Lightweight Multi-Angle Feature Fusion CNN for Bearing Fault Diagnosis

Li, H., Wang, G., Shi, N., Li, Y., Hao, W., & Pang, C. (2025) · Electronics · doi:10.3390/electronics14142774 · Cited by 8

Real-time monitoring of depth of cut in the multi-axis milling process with ball-end cutter

Shi, D.-M., Huang, T., Zhang, X.-M., & Zhao, S. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.09.049 · Cited by 8

Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

Chen, Y., Zhao, Z., Kim, E., Liu, H., Xu, J., Min, H., & Cui, Y. (2021) · Advances in Mechanical Engineering · doi:10.1177/16878140211067024 · Cited by 8

Dilated Residual Shrinkage Network for SAR Image Despeckling

Nie, L., Gao, C., Zhou, Q., & Liu, C. (2021) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/icsip52628.2021.9689024 · Cited by 8

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

Zheng, W., Zhou, L., Chai, Q.-W., Xu, J., & Liu, S. (2022) · Electronics · doi:10.3390/electronics11071093 · Cited by 8

Few‐shot multiscene fault diagnosis of rolling bearing under compound variable working conditions

Wang, S., Wang, D., Kong, D., Li, W., Wang, J., & Wang, H. (2022) · IET Control Theory and Applications · doi:10.1049/cth2.12315 · Cited by 9

DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion

Wang, H., Wang, J., Xu, H., Sun, Y., & Yu, Z. (2022) · Sensors · doi:10.3390/s22145149 · Cited by 8

Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes

Rismayanti, N., Naswin, A., Zaky, U., Zakariyah, M., & Purnamasari, D. A. (2023) · International Journal of Artificial Intelligence in Medical Issues · doi:10.56705/ijaimi.v1i2.99 · Cited by 8

TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement

Wei, X., Zhang, X.-S., & Li, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15934-3_24 · Cited by 8

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

Wei, L., Qian, L., Wang, M., Tong, M., Jiang, Y., & Li, M. (2023) · Defence Technology · doi:10.1016/j.dt.2023.11.021 · Cited by 7

Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments

Huang, W., Li, Y., Tang, J., & Qian, L. (2024) · Sensors · doi:10.3390/s24030847 · Cited by 7

Double-level discriminative domain adaptation network for cross-domain fault diagnosis

Li, Y., Xu, X., Hu, L., Sun, K., & Han, M. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06016-6 · Cited by 7

Automatic seismic event detection in low signal-to-noise ratio seismic signal based on a deep residual shrinkage network

Cao, H., Xu, B., Wang, C., Hu, J., Wang, Q., & Feng, J. (2025) · Computers & Geosciences · doi:10.1016/j.cageo.2025.105868 · Cited by 7

Multi-condition fault diagnosis method for rotating machinery based on whale optimization variational mode decomposition algorithm and deep residual network

Hu, H., Jiang, A., Wu, X., An, Z., & Zhang, S. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adda6a · Cited by 7

OSFSM: A Systematic Open Set Framework for Radar Automatic Target Recognition Using HRRP

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