A novel fault diagnosis method for rotating machinery based on a convolutional neural network

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An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions

Wang, C., Tao, L., Ding, Y., Lü, C., & Ma, J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06732-x · Cited by 29 · see citing works

Group normalization-based 2D-convolutional neural network for intelligent bearing fault diagnosis

Chandra, D. G., Reddy, U. S., Uma, G., & Umapathy, M. (2023) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-023-04491-5 · Cited by 28 · see citing works

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Tang, S., Zhu, Y., Yuan, S., & Li, G. (2020) · Sensors · doi:10.3390/s20247152 · Cited by 28 · see citing works

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Cui, W., Meng, G., Wang, A., Zhang, X., & Ding, J. (2021) · Shock and Vibration · doi:10.1155/2021/3083190 · Cited by 30 · see citing works

Decision Tree Method for Fault Causes Classification Based on RMS-DWT Analysis in 275 kV Transmission Lines Network

Asman, S. H., Aziz, N. F. A., Amirulddin, U. A. U., & Kadir, M. Z. A. A. (2021) · Applied Sciences · doi:10.3390/app11094031 · Cited by 26 · see citing works

Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

Hajnayeb, A. (2021) · Shock and Vibration · doi:10.1155/2021/6988949 · Cited by 26 · see citing works

Wind Turbine Gearbox Fault Diagnosis Using Cyclostationary Analysis and Interpretable CNN

Amin, A., Bibo, A., Panyam, M., & Tallapragada, P. (2023) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-023-00937-1 · Cited by 26 · see citing works

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

Yang, J., Yin, S., Chang, Y., & Gao, T. (2020) · Sensors · doi:10.3390/s20143837 · Cited by 26 · see citing works

Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

Kaji, M., Parvizian, J., & van de Venn, H. W. (2020) · Applied Sciences · doi:10.3390/app10248948 · Cited by 25 · see citing works

Deep Learning-Based Model Architecture for Time-Frequency Images Analysis

Alaskar, H. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.091268 · Cited by 25 · see citing works

Data-Driven Fault Diagnosis Method Based on Second-Order Time-Reassigned Multisynchrosqueezing Transform and Evenly Mini-Batch Training

Sun, G., Gao, Y., Xu, Y. F., & Feng, W. (2020) · IEEE Access · doi:10.1109/access.2020.3006152 · Cited by 25 · see citing works

Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Liu, J., Zhang, M., Wang, H., Zhao, W., & Liu, Y. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/5367217 · Cited by 24 · see citing works

A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

Wang, Y., Huang, S., Dai, J., & Tang, J. (2020) · Shock and Vibration · doi:10.1155/2020/1850286 · Cited by 24 · see citing works

Intelligent fault diagnosis of belt conveyor rollers using a polar KNN algorithm with audio features

Liu, J., Fu, S., Liu, F., & Cheng, X. (2024) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2024.109101 · Cited by 23 · see citing works

Fault Diagnosis of Shaft Misalignment and Crack in Rotor System Based on MI-CNN

Zhao, W., Hua, C., Wang, D., & Dong, D. (2019) · Lecture notes in mechanical engineering · doi:10.1007/978-981-13-8331-1_39 · Cited by 23 · see citing works

Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification

Aljemely, A. H., Xuan, J., Xu, L., Jawad, F. K. J., & Al-Azzawi, O. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02252-2 · Cited by 24 · see citing works

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Hu, Y., Taha, O. W., & Yang, K. (2024) · Applied Sciences · doi:10.3390/app14041384 · Cited by 22 · see citing works

Heterogeneous Federated Learning Framework for IIoT Based on Selective Knowledge Distillation

Guo, S., Chen, H., Liu, Y., Yang, C., Li, Z., & Jin, C. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3452229 · Cited by 22 · see citing works

Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning

Li, X., Li, J., He, D., & Qu, Y. (2019) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2019.3.6 · Cited by 22 · see citing works

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

Yan, J., Liu, T., Ye, X., Jing, Q., & Dai, Y. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256287 · Cited by 21 · see citing works

Improved variational mode decomposition for combined imbalance-and-misalignment fault recognition and severity quantification

Martins, D. H. C. D. S. S., Lima, A. A. D., Gutiérrez, R. H. R., Pestana-Viana, D., Netto, S. L., Vaz, L. A. P., da Silva, E. A. B., & Haddad, D. B. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106516 · Cited by 21 · see citing works

Deep Learning in Fault Diagnosis of Induction Motor Drives

Chattopadhyay, P., Saha, N., Delpha, C., & Sil, J. (2018) · Prognostics and System Health Management Conference · doi:10.1109/phm-chongqing.2018.00189 · Cited by 21 · see citing works

An Improved MSCNN and GRU Model for Rolling Bearing Fault Diagnosis

Wang, T., Tang, Y., Wang, T., & Lei, N. (2023) · Strojniški vestnik – Journal of Mechanical Engineering · doi:10.5545/sv-jme.2022.459 · Cited by 20 · see citing works

Fine‐Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Sun, G., Gao, Y., Lin, K. Y., & Hu, Y. (2019) · Shock and Vibration · doi:10.1155/2019/1531079 · Cited by 20 · see citing works

Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network

Cheng, Y., Lin, M., Wu, J., Zhu, H., & Shao, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106796 · Cited by 350 · see citing works

Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

Liu, D., Lai, X., Xiao, Z., Liu, D., Hu, X., & Zhang, P. (2020) · Shock and Vibration · doi:10.1155/2020/6542913 · Cited by 11 · see citing works

Feature extraction based on DWT and CNN for rotating machinery fault diagnosis

Xie, Y., & Zhang, T. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7979176 · Cited by 21 · see citing works

ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK

LiZhi, Z., WeiXiao, X., Jing, L., & JiWen, T. (2020) · DOAJ (DOAJ: Directory of Open Access Journals)

Fault Diagnosis and Noise Robustness Comparison of Rotating Machinery using CWT and CNN

Kim, B.-W., & Lee, J. (2021) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj0601146 · Cited by 3 · see citing works