Deep Residual Shrinkage Networks for Fault Diagnosis

Deep Residual Learning for Image Recognition

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

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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

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

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

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 246

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

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 218

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 207

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

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

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

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 197

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 194

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 186

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

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 168

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 164

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

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

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 152

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

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 151

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

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 137

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 133

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

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

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

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 122

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

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

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 118

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

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

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

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 117

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

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 117

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

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 111

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

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

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

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 110

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

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

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

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 106

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

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

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 104

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

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

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

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

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

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

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

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

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

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

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

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 87

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

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 86

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

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

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

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

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

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 87

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 82

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

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 81

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

Multi-scale dynamic adaptive residual network for fault diagnosis

Liang, H., Cao, J., & Zhao, X. (2021) · Measurement · doi:10.1016/j.measurement.2021.110397 · Cited by 76

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

Li, C., Mo, L., Kwoh, C. K., Li, X., Chen, Z., Wu, M., & Yan, R. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.112025 · Cited by 76

A novel cross-domain fault diagnosis method based on model agnostic meta-learning

Yang, T., Tang, T., Wang, J., Qiu, C., & Chen, M. (2022) · Measurement · doi:10.1016/j.measurement.2022.111564 · Cited by 74

Elevator fault diagnosis based on digital twin and PINNs-e-RGCN

Wang, Q., Chen, L., Xiao, G., Wang, P., Gu, Y., & Lu, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-78784-7 · Cited by 73

Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning

Ma, X., Yu, H., Wang, M., Li, F., & Wang, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3091504 · Cited by 71

Rolling bearing fault diagnosis based on information fusion and parallel lightweight convolutional network

Guan, Y., Meng, Z., Sun, D., Liu, J., & Fan, F. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.11.012 · Cited by 71

Self-Adaptive Weight Based on Dual-Attention for Differentiable Neural Architecture Search

Xue, Y., Han, X., & Wang, Z. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3348843 · Cited by 72

A survey on machine learning in ship radiated noise

Hummel, H. I., van der Mei, R., & Bhulai, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117252 · Cited by 76

Semisupervised Deep Sparse Auto-Encoder With Local and Nonlocal Information for Intelligent Fault Diagnosis of Rotating Machinery

Zhao, X., Jia, M., & Liu, Z. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3016045 · Cited by 71

One-stage self-supervised momentum contrastive learning network for open-set cross-domain fault diagnosis

Wang, W., Li, C., Li, A., Li, F., Chen, J., & Zhang, T. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110692 · Cited by 69

MDensNet201-IDRSRNet: Efficient cardiovascular disease prediction system using hybrid deep learning

Mandava, M., & Vinta, S. R. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106147 · Cited by 69

A New Method for Transformer Fault Prediction Based on Multifeature Enhancement and Refined Long Short-Term Memory

Ma, X., Hu, H., & Shang, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3098383 · Cited by 68

Online quality inspection of resistance spot welding for automotive production lines

Dai, W., Li, D., Zheng, Y., Wang, D., Tang, D., Wang, H., & Peng, Y. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.04.008 · Cited by 71

A Novel Method of Rolling Bearing Fault Diagnosis Under Strong Noise Backgrounds

Tong, J., Tang, S., Wu, Y., Pan, H., & Zheng, J. (2022) · Measurement · doi:10.1016/j.measurement.2022.112282 · Cited by 68

A novel rolling bearing fault diagnosis method based on Adaptive Denoising Convolutional Neural Network under noise background

Wang, Q., & Xu, F. (2023) · Measurement · doi:10.1016/j.measurement.2023.113209 · Cited by 68

Multiscale Fusion Attention Convolutional Neural Network for Fault Diagnosis of Aero-Engine Rolling Bearing

Liu, X., Lu, J., & Li, Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3298712 · Cited by 68

Rolling bearing fault diagnosis method based on improved residual shrinkage network

Wang, L., Zou, T., Cai, K., & Liu, Y. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-04729-w · Cited by 68

A review for control theory and condition monitoring on construction robots

Shi, H., Li, R., Bai, X., Zhang, Y., Min, L., Wang, D., Lu, X., Yang, Y., & Lei, Y. (2023) · Journal of Field Robotics · doi:10.1002/rob.22156 · Cited by 67

Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition

Liu, H., Xu, Q., Han, X., Wang, B., & Yi, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111479 · Cited by 67

Multi-head attention fusion networks for multi-modal speech emotion recognition

Zhang, J., Xing, L., Tan, Z., Wang, H., & Wang, K. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108078 · Cited by 66

Res-HSA: Residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery

Zhu, J., Jiang, Q., Shen, Y., Xu, F., & Zhu, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106491 · Cited by 65

Fault Diagnosis Method of Low-Speed Rolling Bearing Based on Acoustic Emission Signal and Subspace Embedded Feature Distribution Alignment

Chen, R., Tang, L., Hu, X., & Wu, H. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3028103 · Cited by 64

A Meta-Invariant Feature Space Method for Accurate Tool Wear Prediction Under Cross Conditions

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