Deep Residual Shrinkage Networks for Fault Diagnosis

Deep Residual Learning for Image Recognition

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Fault Diagnosis of Wheeled Robot Based on Prior Knowledge and Spatial-Temporal Difference Graph Convolutional Network

Miao, Z., Xia, Y., Zhou, F., & Yuan, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3208001 · Cited by 63

Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System

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Fault Diagnosis for Limited Annotation Signals and Strong Noise Based on Interpretable Attention Mechanism

Chen, B., Liu, T., He, C., Liu, Z., & Zhang, L. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3169341 · Cited by 63

Joint attention feature transfer network for gearbox fault diagnosis with imbalanced data

Li, B., Tang, B., Deng, L., & Wei, J. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109146 · Cited by 62

Broad auto-encoder for machinery intelligent fault diagnosis with incremental fault samples and fault modes

Fu, Y., Cao, H., Chen, X., & Ding, J. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109353 · Cited by 60

Dynamic Balanced Domain-Adversarial Networks for Cross-Domain Fault Diagnosis of Train Bearings

Ren, H., Wang, J., Dai, J., Zhu, Z., & Liu, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3179468 · Cited by 60

Variable-Modulation Specific Emitter Identification With Domain Adaptation

Zhang, X., Li, T., Gong, P., Zha, X., & Liu, R. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3223794 · Cited by 60

Mask2Defect: A Prior Knowledge-Based Data Augmentation Method for Metal Surface Defect Inspection

Yang, B., Liu, Z., Duan, G., & Tan, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3126098 · Cited by 61

Multivariate Time-Series Prediction in Industrial Processes via a Deep Hybrid Network Under Data Uncertainty

Yao, Y., Yang, M., Wang, J., & Xie, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3198670 · Cited by 59

An Online Multiple Open-Switch Fault Diagnosis Method for T-Type Three-Level Inverters Based on Multimodal Deep Residual Filter Network

Xing, Z., He, Y., & Zhang, W. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3222663 · Cited by 58

Restricted Sparse Networks for Rolling Bearing Fault Diagnosis

Pu, H., Zhang, K., & An, Y. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3243929 · Cited by 60

Intelligent fault diagnosis of turbine blade cracks via multiscale sparse filtering and multi-kernel support vector machine for information fusion

Huang, X., Zhang, X., Xiong, Y., dai, F., & Zhang, Y. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101979 · Cited by 58

Few-shot aero-engine bearing fault diagnosis with denoising diffusion based data augmentation

Ping, Z., Wang, D., Zhang, Y., Ding, B., Duan, Y., & Zhou, W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2024.129327 · Cited by 59

Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing

Zhao, X., Jia, M., Bin, J., Wang, T., & Liu, Z. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3041087 · Cited by 57

Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors

Yao, Y., Wang, J., & Xie, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108064 · Cited by 56

PCWGAN-GP: A New Method for Imbalanced Fault Diagnosis of Machines

Yu, Y., Guo, L., Gao, H., & Liu, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3180431 · Cited by 56

Open Set Recognition of Communication Signal Modulation Based on Deep Learning

Zhang, X., Li, T., Gong, P., Liu, R., Zha, X., & Tang, W. (2022) · IEEE Communications Letters · doi:10.1109/lcomm.2022.3174035 · Cited by 56

Robust Automatic Modulation Classification in Low Signal to Noise Ratio

An, T. T., & Lee, B. M. (2023) · IEEE Access · doi:10.1109/access.2023.3238995 · Cited by 56

MPNet: A lightweight fault diagnosis network for rotating machinery

Liu, Y., Chen, Y., Li, X., Zhou, X., & Wu, D. (2024) · Measurement · doi:10.1016/j.measurement.2024.115498 · Cited by 55

Evaluating calibration of deep fault diagnostic models under distribution shift

Xiao, Y., Shao, H., & Liu, B. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104334 · Cited by 55

Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions

Li, W., Shang, Z., Gao, M., Qian, S., & Feng, Z. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108722 · Cited by 54

Noise-robust machinery fault diagnosis based on self-attention mechanism in wavelet domain

Tian, A., Zhang, Y., Ma, C., Chen, H., Sheng, W., & Zhou, S. (2022) · Measurement · doi:10.1016/j.measurement.2022.112327 · Cited by 54

YOLO-based marine organism detection using two-terminal attention mechanism and difficult-sample resampling

Zhou, Z., Hu, Y., Yang, X., & Yang, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111291 · Cited by 54

Collaborative deep learning framework for fault diagnosis in distributed complex systems

Wang, H., Liu, C., Jiang, D., & Jiang, Z. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107650 · Cited by 54

A label information vector generative zero-shot model for the diagnosis of compound faults

Xu, J., Li, K., Fan, Y., & Yuan, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120875 · Cited by 53

Prediction of breast cancer molecular subtypes using DCE-MRI based on CNNs combined with ensemble learning

Sun, R., Meng, Z., Hou, X., Chen, Y., Yang, Y., Huang, G., & Nie, S. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac195a · Cited by 53

Multi-block domain adaptation with central moment discrepancy for fault diagnosis

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2020) · Measurement · doi:10.1016/j.measurement.2020.108516 · Cited by 52

Optical Fiber Distributed Vibration Sensing Using Grayscale Image and Multi-Class Deep Learning Framework for Multi-Event Recognition

Sun, Z., Liu, K., Jiang, J., Xu, T., Wang, S., Guo, H., Zhou, Z., Xue, K., Huang, Y., & Liu, T. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3089004 · Cited by 52

A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis

Jiang, J., Li, H., Mao, Z., Liu, F., Zhang, J., Jiang, Z., & Li, H. (2022) · Scientific Reports · doi:10.1038/s41598-021-04545-5 · Cited by 53

A novel intelligent fault diagnosis method of rotating machinery based on signal-to-image mapping and deep Gabor convolutional adaptive pooling network

Li, W., Shang, Z., Qian, S., Zhang, B., Zhang, J., & Gao, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117716 · Cited by 52

Deep Spiking Residual Shrinkage Network for Bearing Fault Diagnosis

Xu, Z., Ma, Y., Pan, Z., & Zheng, X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3227363 · Cited by 52

Unsupervised Machine Anomaly Detection Using Autoencoder and Temporal Convolutional Network

Li, Z., Sun, Y. J., Yang, L., Zhao, Z., & Chen, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3212547 · Cited by 52

Multiscale Noise Reduction Attention Network for Aeroengine Bearing Fault Diagnosis

Wang, X., Zhang, H., & Du, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3268459 · Cited by 54

Advancing machine fault diagnosis: a detailed examination of convolutional neural networks

Vashishtha, G., Chauhan, S., Sehri, M., Hebda-Sobkowicz, J., Zimroz, R., Dumond, P., & Kumar, R. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ada178 · Cited by 56

Intelligent fault diagnosis of rolling bearing based on novel CNN model considering data imbalance

Xing, Z., Zhao, R., Wu, Y., & He, T. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03196-x · Cited by 53

A hybrid deep-learning model for fault diagnosis of rolling bearings in strong noise environments

Zhang, K., Fan, C., Zhang, X., Shi, H., & Li, S. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac4a18 · Cited by 51

DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau

Chen, X., Yao, X., Zhou, Z., Liu, Y., Yao, C., & Ren, K. (2022) · Remote Sensing · doi:10.3390/rs14081848 · Cited by 51

Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery

Liang, H., Cao, J., & Zhao, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3170973 · Cited by 51

Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management: Review and Case Study

Yan, R., Zhou, Z., Shang, Z., Wang, Z., Hu, C., Li, Y., Yang, Y., Chen, X., & Gao, R. X. (2025) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-024-01173-8 · Cited by 54

Residual Gated Dynamic Sparse Network for Gearbox Fault Diagnosis Using Multisensor Data

Huang, H., Tang, B., Luo, J., Pu, H., & Zhang, K. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3099060 · Cited by 51

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

Wu, W., Hu, S., Lin, D., & Liu, Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3100398 · Cited by 50

Multiscale dilated convolutional subdomain adaptation network with attention for unsupervised fault diagnosis of rotating machinery cross operating conditions

Xiao, Y., Shao, H., Min, Z., Cao, H., Chen, X., & Lin, J. (2022) · Measurement · doi:10.1016/j.measurement.2022.112146 · Cited by 50

A Layered sEMG–FMG Hybrid Sensor for Hand Motion Recognition From Forearm Muscle Activities

Chen, P., Li, Z., Togo, S., Yokoi, H., & Jiang, Y. (2023) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3287594 · Cited by 50

Adulteration quantification of cheap honey in high-quality Manuka honey by two-dimensional correlation spectroscopy combined with deep learning

Wu, X.-J., Xu, B., Luo, H., Ma, R., Du, Z., Zhang, X., Liu, H., & Zhang, Y. (2023) · Food Control · doi:10.1016/j.foodcont.2023.110010 · Cited by 50

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

Xin, Z., Zhang, J., Zheng, J., Jin, Y. Q., & Liu, Q. (2022) · ISIJ International · doi:10.2355/isijinternational.isijint-2021-251 · Cited by 49

Open-Set Classification for Signal Diagnosis of Machinery Sensor in Industrial Environment

Jian-ming, C., Wang, G., Lv, J., He, Z., Yang, T., & Tang, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3169459 · Cited by 49

An intelligent chatter detection method for high-speed milling under variable tool-workpiece systems and cutting parameters

Sun, L., Huang, X., Zhao, J., Wang, X., & Ma, M. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111960 · Cited by 49

Bearing fault diagnosis under heavy noise: A multi-scale dilated convolution and dense temporal convolutional network

Si, X., Yan, H., Hu, Y., Duan, J., & Shi, T. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131092 · Cited by 56

Multiple wavelet regularized deep residual networks for fault diagnosis

Zhao, M., Tang, B., Deng, L., & PECHT, M. G. (2019) · Measurement · doi:10.1016/j.measurement.2019.107331 · Cited by 49

Multi-perspective deep transfer learning model: A promising tool for bearing intelligent fault diagnosis under varying working conditions

Li, X., Jiang, X., Wang, Q., Yang, L., Wang, Z., Shen, C., & Zhu, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108443 · Cited by 48

Prognostics and health management for induction machines: a comprehensive review

Huang, C., Bu, S., Lee, H. H., Chan, K. W., & Yung, W. K. C. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02103-6 · Cited by 48

Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network

Zheng, X., Yang, P., Yan, K., He, Y., Yu, Q., & Li, M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108087 · Cited by 48

GMA-DRSNs: A novel fault diagnosis method with global multi-attention deep residual shrinkage networks

Zhang, Z., Chen, L., Zhang, C., Shi, H., & Li, H. (2022) · Measurement · doi:10.1016/j.measurement.2022.111203 · Cited by 47

Complex domain extension network with multi-channels information fusion for remaining useful life prediction of rotating machinery

Cao, Y., Jia, M., Ding, Y., Zhao, X., Ding, P., & Gu, L. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110190 · Cited by 46

Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms

Luo, Z., Zuo, R., Xiong, Y., & Zhou, B. (2023) · Natural Resources Research · doi:10.1007/s11053-023-10200-9 · Cited by 46

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models

Song, Y., Yang, D., Wu, W., Zhang, X., Zhou, J. N., Tian, Z., Wang, C., & Song, Y. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12050197 · Cited by 46

Weighting Factors Optimization for FCS-MPC in PMSM Drives Using Aggregated Residual Network

Yao, C., Ma, G., Sun, Z., Luo, J., Ren, G., & Xu, S. (2023) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2023.3323627 · Cited by 46

SA-FPN: Scale-aware attention-guided feature pyramid network for small object detection on surface defect detection of steel strips

Han, L., Li, N., Li, J., Gao, B., & Niu, D. (2025) · Measurement · doi:10.1016/j.measurement.2025.117019 · Cited by 51

Enhanced hierarchical symbolic dynamic entropy and maximum mean and covariance discrepancy-based transfer joint matching with Welsh loss for intelligent cross-domain bearing health monitoring

Yang, C., Jia, M., Li, Z. N., & Gabbouj, M. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108343 · Cited by 46

Multitask Residual Shrinkage Convolutional Neural Network for Sleep Apnea Detection Based on Wearable Bracelet Photoplethysmography

Shen, Q., Yang, X. S., Zou, L., Wei, K., Wang, C., & Liu, G. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3195777 · Cited by 46

A Robust Mean-Field Actor-Critic Reinforcement Learning Against Adversarial Perturbations on Agent States

Zhou, Z., Liu, G., & Zhou, M. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3278715 · Cited by 46

Empirical mode reconstruction: Preserving intrinsic components in data augmentation for intelligent fault diagnosis of civil aviation hydraulic pumps

Meng, L., Zhao, M., Cui, Z., Zhang, X., & Zhong, S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103557 · Cited by 47

Juggler-ResNet: A Flexible and High-Speed ResNet Optimization Method for Intrusion Detection System in Software-Defined Industrial Networks

Zhu, Z., Zhai, W., Liu, H., Geng, J., Zhou, M., Ji, C., & Jia, G. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3121783 · Cited by 45

Reconstruction Domain Adaptation Transfer Network for Partial Transfer Learning of Machinery Fault Diagnostics

Guo, L., Yu, Y., Liu, Y., Gao, H., & Chen, T. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3129213 · Cited by 45

Health evaluation of power transformer using deep learning neural network

Xing, Z., He, Y., Chen, J., Wang, X., & Du, B. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.109016 · Cited by 45

Cavitation state identification of centrifugal pump based on CEEMD-DRSN

Dai, C., Hu, S., Zhang, Y., Chen, Z., & Dong, L. (2023) · Nuclear Engineering and Technology · doi:10.1016/j.net.2023.01.009 · Cited by 45

A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction

Kong, Z., Jin, X., Xu, Z., & Chen, Z. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109163 · Cited by 46

A deep learning-based approach with anti-noise ability for identification of rock microcracks using distributed fibre optic sensing data

Zhao, S., Tan, D., Lin, S.-Q., Yin, Z., & Yin, J. (2023) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2023.105525 · Cited by 45

Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction

Li, B., Li, J., Wu, X., & Tang, H. (2024) · Measurement · doi:10.1016/j.measurement.2024.115546 · Cited by 45

Fault Diagnosis of Electric Motors by a Channel-Wise Regulated CNN and Differential of STFT

Mohammad‐Alikhani, A., Jamshidpour, E., Dhale, S., Akrami, M., Pardhan, S., & Nahid‐Mobarakeh, B. (2025) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2025.3532556 · Cited by 53

Patient-specific method for predicting epileptic seizures based on DRSN-GRU

Xu, X. Y., Zhang, Y., Zhang, R., & Xu, T. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104449 · Cited by 44

Noise-Boosted Convolutional Neural Network for Edge-Based Motor Fault Diagnosis With Limited Samples

Lv, C., An, K., Huang, D., Wang, X., Xia, M., & Lu, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3228902 · Cited by 43

Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines

Li, P., Wang, X., Jiang, C., Bi, H., Liu, Y., Yan, W., Zhang, C., Dong, T., & Sun, Y. W. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109685 · Cited by 43

Towards integrated and fine-grained traffic forecasting: A Spatio-Temporal Heterogeneous Graph Transformer approach

Li, G., Zhao, Z., Guo, X., Tang, L., Zhang, H., & Wang, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102063 · Cited by 48

Unsupervised deep frequency-channel attention factorization to non-linear feature extraction: A case study of identification and functional connectivity interpretation of Parkinson’s disease

Ke, H., Wang, F., Bi, H., Ma, H., Wang, G., & Yin, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122853 · Cited by 45

Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis

Gao, H., Zhang, X., Gao, X., Li, F., & Han, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112478 · Cited by 43

Time–frequency fully-connected graph neural network: An effective multiscale spatiotemporal dependency learning method for multisource machine fault diagnosis

Xu, Y., Li, S., Li, Z., Wu, K., Huang, R., Sun, B., & Ji, J. C. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104023 · Cited by 43

A novel sleep staging network based on multi-scale dual attention

Wang, H., Lu, C., Zhang, Q., Hu, Z., Yuan, X., Zhang, P., & Liu, W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103486 · Cited by 43

Deep Coupled Visual Perceptual Networks for Motor Fault Diagnosis Under Nonstationary Conditions

Xu, Y., Yan, X., Sun, B., & Liu, Z. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3166839 · Cited by 44

A compressed sensing and CNN‐based method for fault diagnosis of photovoltaic inverters in edge computing scenarios

Wang, X., Yang, B., Wang, Z., Liu, Q., Chen, C., & Guan, X. (2022) · IET Renewable Power Generation · doi:10.1049/rpg2.12383 · Cited by 42

Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions

Xu, Y., Yan, X., Feng, K., Zhang, Y., Zhao, X., Sun, B., & Liu, Z. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108972 · Cited by 42

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

Xin, Z., Zhang, J., Jin, Y. Q., Zheng, J., & Liu, Q. (2022) · International Journal of Minerals Metallurgy and Materials · doi:10.1007/s12613-021-2409-9 · Cited by 43

Rotor Dynamics Informed Deep Learning for Detection, Identification, and Localization of Shaft Crack and Unbalance Defects

Deng, W., Nguyen, K. T. P., Medjaher, K., Gogu, C., & Morio, J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102128 · Cited by 42

Data Augmentation via Randomized Wavelet Expansion and Its Application in Few-Shot Fault Diagnosis of Aviation Hydraulic Pumps

Zhao, M., Fu, X., Zhang, Y., Meng, L., & Zhong, S. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3130300 · Cited by 42

Dually attentive multiscale networks for health state recognition of rotating machinery

Xu, Y., Yan, X., Sun, B., & Liu, Z. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108626 · Cited by 42

Attack and Defense: Adversarial Security of Data-Driven FDC Systems

Zhuo, Y., Yin, Z., & Ge, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3197190 · Cited by 43

Deep Transfer Learning With Self-Attention for Industry Sensor Fusion Tasks

Zhang, Z., Farnsworth, M., Song, B., Tiwari, D., & Tiwari, A. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3186505 · Cited by 41

Joint Prediction of Li-ion Battery State of Charge and State of Health Based on the DRSN-CW-LSTM Model

Wang, X., Hao, Z., Chen, Z., & Zhang, J. (2023) · IEEE Access · doi:10.1109/access.2023.3293726 · Cited by 41

Deep continuous convolutional networks for fault diagnosis

Huang, X., Xie, T., Wu, J., Zhou, Q., & Hu, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111623 · Cited by 41

A machine learning and CFD based approach for fouling rapid prediction in shell-and-tube heat exchanger

He, S., Ye, Y., Wang, M., Zhang, J., Tian, W., Qiu, S., & Su, G. H. (2024) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2024.113759 · Cited by 43

Collaborative human-computer fault diagnosis via calibrated confidence estimation

Shao, H., Xiao, Y., Leng, J., Zhao, X., & Liu, B. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103349 · Cited by 41

Transformer fault diagnosis based on improved deep coupled dense convolutional neural network

Li, Z., He, Y., Xing, Z., & Duan, J. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107969 · Cited by 41

Toward Small Sample Challenge in Intelligent Fault Diagnosis: Attention-Weighted Multidepth Feature Fusion Net With Signals Augmentation

Zhang, T., He, S., Chen, J., Pan, T., & Zhou, Z. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3134999 · Cited by 41

Online Knowledge Distillation-Based Multiscale Threshold Denoising Networks for Fault Diagnosis of Transmission Systems

Xu, Y., Yan, X., Sun, B., Feng, K., Kou, L., Chen, Y., Li, Y., Chen, H., Tian, E., Ni, Q., et al. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3313986 · Cited by 41

Embedded-Adaptive-Observer-Based $H_\infty$ Integrated Fault Estimation and Memory Fault-Tolerant Control for Nonlinear Delayed Implicit Robotic Arm

Zhuang, G., Wang, Z., Xie, X., & Xia, J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3372009 · Cited by 40

Health evaluation techniques towards rotating machinery: A systematic literature review and implementation guideline

Jiang, W., Wu, J., Yang, Y., Li, X., & Zhu, H. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110924 · Cited by 40

Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network

Wang, X., Zheng, Z., Jiang, G., He, Q., & Xie, P. (2022) · Energies · doi:10.3390/en15082864 · Cited by 40

A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation

Chen, Y., Zhang, D., Karimi, H. R., Deng, C., & Yin, W. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.04.017 · Cited by 41

Stator Turn Fault Diagnosis and Severity Assessment in Converter-Fed Induction Motor Using Flat Diagnosis Structure Based on Deep Learning Approach

Husari, F. M., & Seshadrinath, J. (2022) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2022.3184754 · Cited by 40

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