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

Inferable Deep Distilled Attention Network for Diagnosing Multiple Motor Bearing Faults

Zhang, X., Hu, Y., Yin, A., Deng, J., Xu, H., & Si, J. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3211203 · Cited by 40

Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions

Xu, Y., Chen, Y., Zhang, H., Feng, K., Wang, Y., Yang, C., & Ni, Q. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110724 · Cited by 42

Efficient shrinkage temporal convolutional network model for photovoltaic power prediction

Wang, M., Rao, C., Xiao, X., Hu, Z., & Goh, M. (2024) · Energy · doi:10.1016/j.energy.2024.131295 · Cited by 39

An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

Zhao, B., Cheng, C., Tu, G., Peng, Z., He, Q., & Meng, G. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00564-5 · Cited by 39

Lightweight Interleaved Residual Dense Network for Gas Identification of Industrial Polypropylene Coupled With an Electronic Nose

Shi, Y. H., Yuan, H., Zhang, Q., Sun, A., Liu, J., & Men, H. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3117377 · Cited by 39

A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis

Zhu, P. Y., Dong, S., Pan, X., Hu, X., & Zhu, S. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac57ef · Cited by 39

One Dimensional Convolutional Neural Networks Using Sparse Wavelet Decomposition for Bearing Fault Diagnosis

Liu, X., Centeno, J., Alvarado, J., & Tan, L. (2022) · IEEE Access · doi:10.1109/access.2022.3199381 · Cited by 38

PeriodNet: Noise-Robust Fault Diagnosis Method Under Varying Speed Conditions

Li, R., Wu, J., Li, Y., & Cheng, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3274290 · Cited by 39

Deep Learning Methods for De Novo Peptide Sequencing

Bittremieux, W., Ananth, V., Fondrie, W. E., Melendez, C., Pominova, M., Sanders, J., Wen, B., Yilmaz, M., & Noble, W. S. (2024) · Mass Spectrometry Reviews · doi:10.1002/mas.21919 · Cited by 45

Domain constrained cascadic multireceptive learning networks for machine health monitoring in complex manufacturing systems

Xu, Y., Li, S., Feng, K., Huang, R., Sun, B., Yang, X., Zhao, Z., & Huang, G. Q. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.03.021 · Cited by 38

A Self-Adaptive Temporal-Spatial Self-Training Algorithm for Semisupervised Fault Diagnosis of Industrial Processes

Zheng, S., & Zhao, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3120686 · Cited by 38

MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings

Chen, Y., Zhang, D., & Zhang, W. (2021) · Control Engineering Practice · doi:10.1016/j.conengprac.2021.104969 · Cited by 40

Bearing fault diagnosis by combining a deep residual shrinkage network and bidirectional LSTM

Tong, Y., Wu, P., He, J., Zhang, X., & Zhao, X. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac37eb · Cited by 38

Developing a semi-supervised learning and ordinal classification framework for quality level prediction in manufacturing

Kim, G., Choi, J. G., Ku, M., & Lim, S. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109286 · Cited by 38

An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data

Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · IEEE Access · doi:10.1109/access.2022.3220265 · Cited by 37

A Novel Ensemble Learning-Based Multisensor Information Fusion Method for Rolling Bearing Fault Diagnosis

Tong, J., Liu, C., Bao, J., Pan, H., & Zheng, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3225910 · Cited by 40

A visual vibration characterization method for intelligent fault diagnosis of rotating machinery

Peng, C., Gao, H., Liu, X., & Liu, B. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110229 · Cited by 38

GAN-LSTM-Based ADS-B Attack Detection in the Context of Air Traffic Control

Yue, M., Zheng, H., Cui, H., & Wu, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3252809 · Cited by 37

CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive Sensing

Guo, Z., & Gan, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02370 · Cited by 37

Adversarial domain adaptation based on contrastive learning for bearings fault diagnosis

Pan, X., Chen, H., Wang, W., & Su, X. (2024) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.103058 · Cited by 44

Explainable artificial intelligence based intelligent fault diagnosis: A systematic review from applications to insights

Li, T., Chen, J., Liu, T., Sun, C., Zhao, Z., Chen, X., & Yan, R. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111935 · Cited by 44

An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Yang, P., Geng, H., Wen, C., & Liu, P. (2021) · Drones · doi:10.3390/drones5040133 · Cited by 38

A structurally re-parameterized convolution neural network-based method for gearbox fault diagnosis in edge computing scenarios

Wang, Y., Wu, J., Yu, Z., Hu, J., & Zhou, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107091 · Cited by 37

Deep Rational Attention Network With Threshold Strategy Embedded for Mechanical Fault Diagnosis

Zhao, D., Zhang, H., Liu, S., Wei, Y., & Xiao, S. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3085951 · Cited by 36

Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes

Li, Q., Tang, B., Deng, L., Xiong, P., & Zhao, M. (2022) · Measurement · doi:10.1016/j.measurement.2022.111570 · Cited by 36

Rolling bearing fault diagnosis method based on multi-sensor two-stage fusion

Liu, C., Tong, J., Zheng, J., Pan, H., & Bao, J. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac8894 · Cited by 36

MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines

Kim, H., Park, C. H., Suh, C., Chae, M., Yoon, H., & Youn, B. D. (2023) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwad031 · Cited by 35

A network structure for industrial process fault diagnosis based on hyper feature extraction and stacked LSTM

Ren, Y., Zhang, R., & Gao, F. (2024) · Chemical Engineering Science · doi:10.1016/j.ces.2024.119745 · Cited by 35

A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

Lu, Y., Yang, T., Zhao, C. K., Chen, W., & Zeng, R. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110454 · Cited by 35

Diffusion model-assisted cross-domain fault diagnosis for rotating machinery under limited data

Zhang, Y., Wang, Z., Fan, C., Jiang, Z., Yu, K., Ren, Z., & Feng, K. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111372 · Cited by 37

DecFFD: A Personalized Federated Learning Framework for Cross-Location Fault Diagnosis

Deng, D., Zhao, W., Wu, X., Zhang, T., Zheng, J., Kang, J., & Niyato, D. T. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3353920 · Cited by 34

Automatic Modulation Recognition of Underwater Acoustic Signals Using a Two-Stream Transformer

Li, J., Jia, Q., Cui, X., Gulliver, T. A., Jiang, B., Li, S., & Yang, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3367852 · Cited by 34

A hybrid semantic attribute-based zero-shot learning model for bearing fault diagnosis under unknown working conditions

Shang, Z., Tang, L., Pan, C., & Cheng, H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109020 · Cited by 34

Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system

Lin, L., Zhang, S., Fu, S., Liu, Y., Suo, S., & Hu, G. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129012 · Cited by 34

Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast‐Enhanced MRI

Sun, R., Hou, X., Li, X., Xie, Y., & Nie, S. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27955 · Cited by 34

Fault Diagnosis of Rolling Bearing Based on Knowledge Graph With Data Accumulation Strategy

Xiao, X., Li, C., Huang, J., & Tian, Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3201839 · Cited by 34

Recognition of penetration states based on arc sound of interest using VGG-SE network during pulsed GTAW process

Zhao, Z., Lv, N., Xiao, R., Liu, Q., & Chen, S. (2023) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2022.12.034 · Cited by 34

An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images

Lin, P., Yang, H., Cheng, S., Guo, F., Wang, L., & Lin, Y. (2023) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2023.112280 · Cited by 34

Modified Data Augmentation Integration Method for Robust Intrusion Events Recognition With Fiber Optic DAS System

Zhang, S.-X., He, T., Li, H., Fan, C., Yan, Z., & Sun, Q. (2023) · Journal of Lightwave Technology · doi:10.1109/jlt.2023.3301557 · Cited by 34

Multiattention-Based Feature Aggregation Convolutional Networks With Dual Focal Loss for Fault Diagnosis of Rotating Machinery Under Data Imbalance Conditions

Xu, Y., Li, S., Yan, X., He, J., Ni, Q., Sun, Y., & Wang, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3346532 · Cited by 33

SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition

Zhang, H., Zhou, F., Wu, Q., & Al‐Dhahir, N. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3385921 · Cited by 33

A clustered blueprint separable convolutional neural network with high precision for high-speed train bogie fault diagnosis

Jia, X., Qin, N., Huang, D., Zhang, Y., & Du, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.056 · Cited by 33

DPGCN Model: A Novel Fault Diagnosis Method for Marine Diesel Engines Based on Imbalanced Datasets

Wang, R., Chen, H., & Guan, C. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3228002 · Cited by 33

A hybrid deep learning model based on feature capture of water level influencing factors and prediction error correction for water level prediction of cascade hydropower stations under multiple time scales

Ma, X., Hu, H., & Ren, Y. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.129044 · Cited by 32

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

He, Y., Zang, C., Zeng, P., Wang, M., Dong, Q., Wan, G., & Dong, X. (2023) · Petroleum Science · doi:10.1016/j.petsci.2023.02.017 · Cited by 33

Elevator vibration signal denoising by deep residual U-Net

Xie, P., Zhang, L., Li, M., Lau, S. F. S., & Huang, J. (2023) · Measurement · doi:10.1016/j.measurement.2023.113976 · Cited by 32

Multimodal fusion fault diagnosis method under noise interference

Qiu, Z., Fan, S., Liang, H., & Liu, J. (2024) · Applied Acoustics · doi:10.1016/j.apacoust.2024.110301 · Cited by 36

Design of model fusion learning method based on deep bidirectional GRU neural network in fault diagnosis of industrial processes

Zhu, Y., Zhang, C., Zhang, R., & Gao, F. (2024) · Chemical Engineering Science · doi:10.1016/j.ces.2024.120884 · Cited by 32

Defect identification of wind turbine blade based on multi‐feature fusion residual network and transfer learning

Zhu, J., Wen, C., & Liu, J. (2021) · Energy Science & Engineering · doi:10.1002/ese3.1024 · Cited by 32

Deep Negative Correlation Multisource Domains Adaptation Network for Machinery Fault Diagnosis Under Different Working Conditions

Ye, Z., & Yu, J. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3191051 · Cited by 32

Improved deep residual shrinkage network on near infrared spectroscopy for tobacco qualitative analysis

Qin, Y., Liu, X., Zhang, F., Shan, Q., & Zhang, M. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104575 · Cited by 31

DPCCNN: A new lightweight fault diagnosis model for small samples and high noise problem

Zhang, J., Zhao, Z., Jiao, Y., Zhao, R., Hu, X., & Che, R. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129526 · Cited by 34

Research on rolling bearing fault diagnosis based on parallel depthwise separable ResNet neural network with attention mechanism

Zhang, X., Zhang, X., Wang, L., & He, F. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128105 · Cited by 31

G-GCSN: Global Graph Convolution Shrinkage Network for Emotion Perception from Gait

Zhuang, Y., Lin, L., Tong, R., Liu, J., Iwamoto, Y., & Chen, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69756-3_4 · Cited by 31

Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery

Kuang, J., Xu, G., Tao, T., Wu, Q., Han, C., & Wei, F. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196948 · Cited by 31

Multi-scale split dual calibration network with periodic information for interpretable fault diagnosis of rotating machinery

Chen, Y., Zhang, D., Ni, H., Cheng, J. S., & Karimi, H. R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106181 · Cited by 31

Data augmentation via variational mode reconstruction and its application in few-shot fault diagnosis of rolling bearings

Li, H., Zhang, Z., & Zhang, C. (2023) · Measurement · doi:10.1016/j.measurement.2023.113062 · Cited by 31

A multimodal deep neural network for prediction of the driver’s focus of attention based on anthropomorphic attention mechanism and prior knowledge

Fu, R., Huang, T., Li, M., Sun, Q., & Chen, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119157 · Cited by 30

SODAS-Net: Side-Information-Aided Deep Adaptive Shrinkage Network for Compressive Sensing

Song, J., & Zhang, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3304676 · Cited by 30

CGASNet: A Generalized Zero-Shot Learning Compound Fault Diagnosis Approach for Bearings

Xu, J., Zhang, H., Chen, W., Fan, Y., & Ding, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3373062 · Cited by 30

Remaining useful life prediction under variable operating conditions via multisource adversarial domain adaptation networks

Du, J., Song, L., Gui, X., Zhang, J., Guo, L., & Li, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111717 · Cited by 30

Fault Diagnosis of Rolling Bearing Using Convolutional Denoising Autoencoder and Siamese Neural Network With Small Sample

Zhao, X., Chen, Y., Yang, M., & Xiang, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3487989 · Cited by 33

Gearbox fault diagnosis based on temporal shrinkage interpretable deep reinforcement learning under strong noise

Wei, Z., Wang, H., Zhao, Z., Zhou, Z., & Yan, R. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109644 · Cited by 31

Multi-scale quadratic convolutional neural network for bearing fault diagnosis based on multi-sensor data fusion

Ji, Y., Gao, J. Y., Shao, X., & Wang, C. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-10918-6 · Cited by 32

A novel method for transformer fault diagnosis based on refined deep residual shrinkage network

Hu, H., Ma, X., & Shang, Y. (2021) · IET Electric Power Applications · doi:10.1049/elp2.12147 · Cited by 30

An intelligent diagnosis method of rolling bearing based on multi-scale residual shrinkage convolutional neural network

Zhao, X., & Zhang, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac68d1 · Cited by 30

TC-net: transformer combined with cnn for image denoising

Xue, T., & Ma, P. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03785-w · Cited by 30

Proportion-Extracting Chirplet Transform for Nonstationary Signal Analysis of Rotating Machinery

Zhang, D., & Feng, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3185771 · Cited by 30

Robust photon-efficient imaging using a pixel-wise residual shrinkage network

Yao, G., Chen, Y., Liu, Y., Hu, X., & Pan, Y. (2022) · Optics Express · doi:10.1364/oe.452597 · Cited by 29

Fault Diagnosis for Power Converters Based on Incremental Learning

Zhang, S., Wang, R., Wang, L., Si, Y., Lin, A., & Wang, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3265095 · Cited by 29

Open-Set Specific Emitter Identification Leveraging Enhanced Metric Denoising Autoencoders

Huang, S., Guo, L., Fu, X. G., Peng, Y., Guo, Y., Wang, Y., Zhang, Q., Gui, G., & Sari, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3404042 · Cited by 29

AMCW-DFFNSA: An interpretable deep feature fusion network for noise-robust machinery fault diagnosis

Han, Y., Lv, S., Huang, Q., & Zhang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112361 · Cited by 31

Attention activation network for bearing fault diagnosis under various noise environments

Zhang, Y., Lin, L., Wang, J., Zhāng, W., Gao, S., & Zhang, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-85275-w · Cited by 30

Deep learning and its applications in nuclear magnetic resonance spectroscopy

Luo, Y., Zheng, X., Qiu, M., Gou, Y., Yang, Z., Qu, X., Chen, Z., & Lin, Y. (2025) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2024.101556 · Cited by 31

Modeling and diagnosis of industrial system using hybrid deep residual shrinkage network and XGBoost

Zhang, R., Li, Z., & Gao, F. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.107438 · Cited by 29

Bearing Running State Recognition Method Based on Feature-to-Noise Energy Ratio and Improved Deep Residual Shrinkage Network

Pei, X., Dong, S., Tang, B., & Xuejiao, P. (2021) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2021.3120755 · Cited by 29

Noise-Aware Sparse Gaussian Processes and Application to Reliable Industrial Machinery Health Monitoring

Jing-yu, Y., Yue, Z., & Yuan, Y. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3200428 · Cited by 29

FFKD-CGhostNet: A Novel Lightweight Network for Fault Diagnosis in Edge Computing Scenarios

Huang, Q., Han, Y., Zhang, X., Sheng, J., Zhang, Y., & Xie, H. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3327480 · Cited by 29

A Lightweight Fault Diagnosis Method of Beam Pumping Units Based on Dynamic Warping Matching and Parallel Deep Network

Liu, S., Song, C., Wu, T., & Zeng, P. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3328731 · Cited by 29

Vibration-Signal-Based Deep Noisy Filtering Model for Online Transformer Diagnosis

Xing, Z., He, Y., Wang, X., Chen, J., Du, B., He, L., & Liu, X. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3245193 · Cited by 28

TFFormer: A Time–Frequency Information Fusion-Based CNN-Transformer Model for OSA Detection With Single-Lead ECG

Li, C., Shi, Z., Zhou, L., Zhang, Z., Wu, C., Ren, X., Hei, X., Zhao, M., Zhang, Y., Liu, H., et al. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3312472 · Cited by 28

A Deep Residual Shrinkage Neural Network-based Deep Reinforcement Learning Strategy in Financial Portfolio Management

Sun, R., Jiang, Z., & Su, J. (2021) · IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda51983.2021.9403210 · Cited by 30

A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

Zhang, C., Zha, D., Wang, L., & Mu, N. (2021) · Symmetry · doi:10.3390/sym13061096 · Cited by 28

Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution

Chen, K., Zhu, L., Chen, S., Zhang, S., & Zhao, H. (2021) · Measurement · doi:10.1016/j.measurement.2021.109945 · Cited by 28

Deep 3D point cloud classification and segmentation network based on GateNet

Liu, H., & Tian, S. (2023) · The Visual Computer · doi:10.1007/s00371-023-02826-w · Cited by 28

A Novel Data Augmentation and Composite Multiscale Network for Mechanical Fault Diagnosis

Wei, Y., Xiao, Z., Liu, S., Schröder, K., Peng, H., Sarr, A. M., & Gu, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3301902 · Cited by 28

A novel multi-scale competitive network for fault diagnosis in rotating machinery

Huang, Z., & Zhao, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107441 · Cited by 28

Research on Fault Diagnosis Method of Bearings in the Spindle System for CNC Machine Tools Based on DRSN-Transformer

Li, X., Chen, J., Wang, J., Wang, J., Li, X., & Kan, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3404968 · Cited by 27

Image recognition based on deep residual shrinkage Network

Li, Y., & Chen, H. (2021) · International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) · doi:10.1109/aiea53260.2021.00077 · Cited by 27

Health Status Recognition of Rotating Machinery Based on Deep Residual Shrinkage Network Under Time-Varying Conditions

Cao, X., Xu, X., Duan, Y. G., & Yang, X. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3197754 · Cited by 27

A multiscale convolution neural network for bearing fault diagnosis based on frequency division denoising under complex noise conditions

Wang, Y., & Cao, G. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00925-0 · Cited by 27

Fault diagnosis of pressure relief valve based on improved deep Residual Shrinking Network

Yin, H., Xu, H., Fan, W., & Sun, F. J. (2023) · Measurement · doi:10.1016/j.measurement.2023.113752 · Cited by 27

Selective Feature Reinforcement Network for Robust Remote Fault Diagnosis of Wind Turbine Bearing Under Non-Ideal Sensor Data

Tan, J., Wan, J., Chen, B., Safran, M., AlQahtani, S. A., & Zhang, R. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3375958 · Cited by 29

Rotating machinery fault classification based on one-dimensional residual network with attention mechanism and bidirectional gated recurrent unit

Dong, Z., Zhao, D., & Cui, L. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad41fb · Cited by 27

A multi-dimensional residual shrinking network combined with a long short-term memory network for state of charge estimation of Li-ion batteries

Quan, R., Liu, P., Li, Z., Li, Y., Chang, Y., & Yan, H. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106263 · Cited by 26

Transfer learning-based multiple digital twin-assisted intelligent mechanical fault diagnosis

Liu, S., Qi, Y., Gao, X., Liu, L., & Ma, R. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0683 · Cited by 26

MAMC—Optimal on Accuracy and Efficiency for Automatic Modulation Classification With Extended Signal Length

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