Deep Residual Shrinkage Networks for Fault Diagnosis

Deep Residual Learning for Image Recognition

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Squeeze-and-Excitation Networks

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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

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Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network

Shao, H., Jiang, H., Zhang, H., & Liang, T. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2745473 · Cited by 489 · see citing works

Deep residual learning-based fault diagnosis method for rotating machinery

Zhang, W., Li, X., & Ding, Q. (2019) · ISA Transactions · doi:10.1016/j.isatra.2018.12.025 · Cited by 416 · see citing works

Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes

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Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis

Sun, W., Zhao, R., Yan, R., Shao, S., & Chen, X. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2672988 · Cited by 295 · see citing works

Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform

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Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction

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Sparse Deep Stacking Network for Fault Diagnosis of Motor

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Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

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Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis

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Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems

Razavi‐Far, R., Hallaji, E., Farajzadeh-Zanjani, M., Saif, M., Kia, S. H., Henao, H., & Capolino, G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2873546 · Cited by 139 · see citing works

Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions

Ma, S., Chu, F., & Han, Q. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.02.055 · Cited by 121 · see citing works

An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes

Han, Y., Tang, B., & Deng, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.01.012 · Cited by 111 · see citing works

Kurtogram manifold learning and its application to rolling bearing weak signal detection

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Drivetrain Gearbox Fault Diagnosis: Vibration- and Current-Based Approaches

Jin, X., Cheng, F., Peng, Y., Qiao, W., & Qu, L. (2018) · IEEE Industry Applications Magazine · doi:10.1109/mias.2017.2740470 · Cited by 34 · see citing works

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Shao, H., Xia, M., Han, G., Zhang, Y., & Wan, J. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3005965 · Cited by 471 · see citing works

Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis

Xiao, Y., Shao, H., Wang, J., Yan, S., & Liu, B. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110936 · Cited by 278 · see citing works

Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings

Ding, Y., Zhuang, J., Ding, P., & Jia, M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108126 · Cited by 256 · see citing works

Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions

Ding, Y., Jia, M., Zhuang, J., Cao, Y., Zhao, X., & Lee, C.-G. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108890 · Cited by 244 · see citing works

One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis

Yu, J., & Zhou, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2966326 · Cited by 236 · see citing works

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

Zhang, K., Tang, B., Deng, L., & Liu, X. (2021) · Measurement · doi:10.1016/j.measurement.2021.109491 · Cited by 212 · see citing works

The Fusion of Deep Learning and Fuzzy Systems: A State-of-the-Art Survey

Zheng, Y., Xu, Z., & Wang, X. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3062899 · Cited by 205 · see citing works

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 204 · see citing works

Dense Convolutional Network and Its Application in Medical Image Analysis

Zhou, T., Ye, X., Lu, H., Zheng, X., Qiu, S., & Liu, Y. (2022) · BioMed Research International · doi:10.1155/2022/2384830 · Cited by 201 · see citing works

Physics-informed interpretable wavelet weight initialization and balanced dynamic adaptive threshold for intelligent fault diagnosis of rolling bearings

He, C., Shi, H., Si, J., & Li, J. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.08.014 · Cited by 198 · see citing works

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo, Z., Martínez-García, M., Zhang, Y., Yan, R., & Shu, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2981220 · Cited by 193 · see citing works

CLFormer: A Lightweight Transformer Based on Convolutional Embedding and Linear Self-Attention With Strong Robustness for Bearing Fault Diagnosis Under Limited Sample Conditions

Fang, H., Deng, J. Y., Bai, Y., Feng, B., Li, S., Shao, S., & Chen, D. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3132327 · Cited by 193 · see citing works

CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery

Xu, Y., Feng, K., Yan, X., Yan, R., Ni, Q., Sun, B., Lei, Z., Zhang, Y., & Liu, Z. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.012 · Cited by 185 · see citing works

Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

Zhao, X., Jia, M., & Liu, Z. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3034189 · Cited by 177 · see citing works

Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis

Zhang, S., Liu, Z., Chen, Y., Jin, Y., & Bai, G. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.06.035 · Cited by 165 · see citing works

Few-Shot Object Detection: A Comprehensive Survey

Köhler, M., Eisenbach, M., & Groß, H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3265051 · Cited by 155 · see citing works

Universal source-free domain adaptation method for cross-domain fault diagnosis of machines

Zhang, Y., Ren, Z., Feng, K., Yu, K., Beer, M., & Liu, Z. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110159 · Cited by 151 · see citing works

Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics

Wang, Y., Deng, L., Zheng, L., & Gao, R. X. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.07.008 · Cited by 150 · see citing works

Feature-Level Attention-Guided Multitask CNN for Fault Diagnosis and Working Conditions Identification of Rolling Bearing

Wang, H., Liu, Z., Peng, D., Yang, M., & Qin, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3060494 · Cited by 149 · see citing works

Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM

Keshun, Y., Puzhou, W., & Gu, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3377731 · Cited by 147 · see citing works

FD-LLM: Large language model for fault diagnosis of complex equipment

Lin, L., Zhang, S., Fu, S., & Liu, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103208 · Cited by 141 · see citing works

A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels

Zhang, K., Tang, B., Deng, L., Tan, Q., & Yu, H. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107963 · Cited by 138 · see citing works

Interpretation of Electrocardiogram (ECG) Rhythm by Combined CNN and BiLSTM

Xu, X. J., Jeong, S., & Li, J. (2020) · IEEE Access · doi:10.1109/access.2020.3006707 · Cited by 135 · see citing works

A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal

Qiu, X., Yan, F., & Liu, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104652 · Cited by 129 · see citing works

Dual-Path Mixed-Domain Residual Threshold Networks for Bearing Fault Diagnosis

Chen, Y., Zhang, D., Zhang, H., & Wang, Q. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3144572 · Cited by 128 · see citing works

A Fault Diagnosis Method for Rotating Machinery Based on CNN With Mixed Information

Zhao, Z., & Jiao, Y. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3224979 · Cited by 127 · see citing works

Transfer Learning for Remaining Useful Life Prediction Across Operating Conditions Based on Multisource Domain Adaptation

Ding, Y., Ding, P., Zhao, X., Cao, Y., & Jia, M. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3147534 · Cited by 122 · see citing works

Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data

Kuang, J., Xu, G., Tao, T., & Wu, Q. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3136175 · Cited by 121 · see citing works

Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations

He, C., Shi, H., Li, R., Li, J., & Yu, Z. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102568 · Cited by 118 · see citing works

Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery

Xu, Y., Yan, X., Feng, K., Sheng, X., Sun, B., & Liu, Z. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108714 · Cited by 117 · see citing works

Denoising Fault-Aware Wavelet Network: A Signal Processing Informed Neural Network for Fault Diagnosis

Shang, Z., Zhao, Z., & Yan, R. (2023) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-023-00838-0 · Cited by 117 · see citing works

FedCAE: A New Federated Learning Framework for Edge-Cloud Collaboration Based Machine Fault Diagnosis

Yu, Y., Guo, L., Gao, H., He, Y., You, Z., & Duan, A. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3273272 · Cited by 117 · see citing works

Cross-Modal Fusion Convolutional Neural Networks With Online Soft-Label Training Strategy for Mechanical Fault Diagnosis

Xu, Y., Feng, K., Yan, X., Sheng, X., Sun, B., Liu, Z., & Yan, R. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3256400 · Cited by 115 · see citing works

One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines

Mohammad‐Alikhani, A., Nahid‐Mobarakeh, B., & Hsieh, M. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3265054 · Cited by 115 · see citing works

Model-driven deep unrolling: Towards interpretable deep learning against noise attacks for intelligent fault diagnosis

Zhao, Z., Li, T., An, B., Wang, S., Ding, B., Yan, R., & Chen, X. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.02.027 · Cited by 114 · see citing works

Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples

Yang, D., Karimi, H. R., & Sun, K. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.04.003 · Cited by 112 · see citing works

Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions

Liao, J.-X., He, C., Li, J., Sun, J., Zhang, S., & Zhang, X. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111750 · Cited by 112 · see citing works

Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework

Feng, H., Li, Q., Wang, W. W., Bashir, A. K., Singh, A. K., Xu, J., & Fang, K. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102555 · Cited by 111 · see citing works

Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis

Yu, K., Fu, Q., Ma, H., Lin, T. R., & Li, X. (2020) · Structural Health Monitoring · doi:10.1177/1475921720980718 · Cited by 110 · see citing works

Multiscale Residual Attention Convolutional Neural Network for Bearing Fault Diagnosis

Jia, L., Chow, T. W. S., Wang, Y., & Yuan, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196742 · Cited by 110 · see citing works

FedRUL: A New Federated Learning Method for Edge-Cloud Collaboration Based Remaining Useful Life Prediction of Machines

Guo, L., Yu, Y., Qian, M., Zhang, R., Gao, H., & Cheng, Z. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3195524 · Cited by 110 · see citing works

A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis

Min, H., Fang, Y., Wu, X., Lei, X., Chen, S., Teixeira, R., Zhu, B., Zhao, X., & Xu, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120002 · Cited by 110 · see citing works

Explainable Graph Wavelet Denoising Network for Intelligent Fault Diagnosis

Li, T., Sun, C., Li, S., Wang, Z., Chen, X., & Yan, R. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3230458 · Cited by 108 · see citing works

MgNet: A fault diagnosis approach for multi-bearing system based on auxiliary bearing and multi-granularity information fusion

Deng, J. Y., Liu, H., Fang, H., Shao, S., Wang, D., Hou, Y., Chen, D., & Tang, M. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110253 · Cited by 108 · see citing works

Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction

Yao, L., Zhang, Y., He, T., & Luo, H. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121975 · Cited by 107 · see citing works

You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis

Fang, H., Deng, J. Y., Chen, D., Jiang, W., Shao, S., Tang, M., & Liu, J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101890 · Cited by 106 · see citing works

Multi-sensor information fusion and coordinate attention-based fault diagnosis method and its interpretability research

Tong, J., Liu, C., Zheng, J., & Pan, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106614 · Cited by 105 · see citing works

Wavelet Kernel based Convolutional Neural Network for Localization of Partial Discharge Sources within a Power Apparatus

Ganguly, B., Chaudhuri, S., Biswas, S., Dey, D., Munshi, S., Chatterjee, B., Dalai, S., & Chakravorti, S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2991686 · Cited by 103 · see citing works

An unsupervised feature learning based health indicator construction method for performance assessment of machines

Guo, L., Yu, Y., Duan, A., Gao, H., & Zhang, J. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108573 · Cited by 103 · see citing works

A novel self-training semi-supervised deep learning approach for machinery fault diagnosis

Long, J., Chen, Y., Yang, Z., Huang, Y., & Li, C. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2032860 · Cited by 103 · see citing works

Incipient Interturn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model

Husari, F., & Seshadrinath, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3067321 · Cited by 101 · see citing works

On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

Song, Q., Wang, M., Lai, W., & Zhao, S. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3207181 · Cited by 99 · see citing works

Improved Fault Diagnosis Method for Permanent Magnet Synchronous Machine System Based on Lightweight Multisource Information Data Layer Fusion

Hang, J., Qiu, G., Hao, M., & Ding, S. (2024) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2024.3432163 · Cited by 98 · see citing works

Multireceptive Field Denoising Residual Convolutional Networks for Fault Diagnosis

Xu, Y., Yan, X., Sun, B., Zhai, J., & Liu, Z. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3125666 · Cited by 97 · see citing works

Feature-level SMOTE: Augmenting fault samples in learnable feature space for imbalanced fault diagnosis of gas turbines

Liu, D., Zhong, S., Lin, L., Zhao, M., Fu, X., & Liu, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122023 · Cited by 97 · see citing works

WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis

Li, S., Li, T., Sun, C., Chen, X., & Yan, R. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3282599 · Cited by 95 · see citing works

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works

Multi-task learning for hand heat trace time estimation and identity recognition

Yu, X., Liang, X., Zhou, Z., & Zhang, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124551 · Cited by 92 · see citing works

A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction

Siddique, M., Saleem, F., Umar, M. I., Kim, C. H., & Kim, J.-M. (2025) · Sensors · doi:10.3390/s25092712 · Cited by 89 · see citing works

Deep convolution stack for waveform in underwater acoustic target recognition

Tian, S., Chen, D., Wang, H., & Liu, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-88799-z · Cited by 86 · see citing works

ISEANet: An interpretable subdomain enhanced adaptive network for unsupervised cross-domain fault diagnosis of rolling bearing

Liu, B., Yan, C., Liu, Y., Lv, M., Huang, Y., & Wu, L. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102610 · Cited by 86 · see citing works

A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment

Lyu, P., Zhang, K., Yu, W., Wang, B., & Liu, C. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101564 · Cited by 85 · see citing works

Attention-Embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis

Liao, J.-X., Dong, H., Sun, Z., Sun, J., Zhang, S., & Fan, F. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3259031 · Cited by 85 · see citing works

IDSN: A one-stage interpretable and differentiable STFT domain adaptation network for traction motor of high-speed trains cross-machine diagnosis

He, C., Shi, H., & Li, J. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110846 · Cited by 85 · see citing works

Multisource Domain Feature Adaptation Network for Bearing Fault Diagnosis Under Time-Varying Working Conditions

Wang, R., Huang, W., Wang, J., Shen, C., & Zhu, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3168903 · Cited by 83 · see citing works

A CNN-LSTM hybrid network for automatic seizure detection in EEG signals

Shanmugam, S., & Dharmar, S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08832-2 · Cited by 83 · see citing works

Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks

Zhao, M., Fu, X., Zhang, Y., Meng, L., & Tang, B. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101535 · Cited by 82 · see citing works

EEG analysis of Parkinson's disease using time–frequency analysis and deep learning

Zhang, R., Jia, J., & Zhang, R. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103883 · Cited by 82 · see citing works

Highly imbalanced fault diagnosis of gas turbines via clustering-based downsampling and deep siamese self-attention network

Liu, D., Zhong, S., Lin, L., Zhao, M., Fu, X., & Liu, X. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101725 · Cited by 82 · see citing works

Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery

Chen, G., Yuan, J., Zhang, Y., Zhu, H., Huang, R., Wang, F., & Li, W. (2024) · IEEE Access · doi:10.1109/access.2024.3430010 · Cited by 82 · see citing works

Model Fusion and Multiscale Feature Learning for Fault Diagnosis of Industrial Processes

Liu, K., Lu, N., Wu, F., Zhang, R., & Gao, F. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3176475 · Cited by 81 · see citing works

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

Luo, H. S., Du, J., Yang, P., Shi, Y., Liu, Z., Yang, D., Zheng, L., Chen, X., & Wang, Z. L. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c00566 · Cited by 81 · see citing works

Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data

Li, Q., Tang, B., Deng, L., Wu, Y., & Wang, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.107570 · Cited by 79 · see citing works

A novel feature adaptive extraction method based on deep learning for bearing fault diagnosis

Zhang, T., Liu, S., Wei, Y., & Zhang, H. (2021) · Measurement · doi:10.1016/j.measurement.2021.110030 · Cited by 79 · see citing works

Multi-scale dynamic adaptive residual network for fault diagnosis

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Noise-robust multi-view graph neural network for fault diagnosis of rotating machinery

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