A deep learning approach for fault diagnosis of induction motors in manufacturing

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Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities

Ayman, A., Onsy, A., Attallah, O., Brooks, H., & Morsi, I. (2024) · Measurement · doi:10.1016/j.measurement.2024.116589 · Cited by 31 · see citing works

Turn‐to‐turn short circuit of motor stator fault diagnosis in continuous state based on deep auto‐encoder

Wang, B., Shen, C., Xu, K., & Zheng, T. (2019) · IET Electric Power Applications · doi:10.1049/iet-epa.2019.0101 · Cited by 31 · see citing works

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Yang, Z., Gjorgjevikj, D., Long, J., Zi, Y., Zhang, S., & Li, C. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00569-0 · Cited by 31 · see citing works

A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition

Jiang, S. B., Wong, P. K., & Liang, Y. C. (2019) · IEEE Access · doi:10.1109/access.2019.2950240 · Cited by 30 · see citing works

Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

An, Z., Jiang, X., Yang, R., Zhang, H., Liu, J., & Shen, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3218737 · Cited by 30 · see citing works

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

Wang, H., Zhang, W., Han, C., Fu, Z., & Song, L. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad1708 · Cited by 29 · see citing works

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

Pan, X., Lu, J., Huo, J., Gao, J., & Wu, H. (2020) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-020-00514-7 · 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

Equipment activity recognition and early fault detection in automated construction through a hybrid machine learning framework

Harichandran, A., Raphael, B., & Mukherjee, A. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12848 · Cited by 27 · see citing works

Bald eagle search optimization with deep transfer learning enabled age-invariant face recognition model

Alsubai, S., Hamdi, M., Abdel‐Khalek, S., Alqahtani, A., Binbusayyis, A., & Mansour, R. F. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104545 · Cited by 26 · see citing works

Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network

Lee, D., Jeong, J.-H., Yoon, S. H., & Chae, Y. T. (2019) · Energies · doi:10.3390/en12173247 · Cited by 25 · see citing works

Fault classification in the process industry using polygon generation and deep learning

Elhefnawy, M., Ragab, A., & Ouali, M.-S. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01742-x · Cited by 23 · see citing works

Fault Diagnosis of Motor Vibration Signals by Fusion of Spatiotemporal Features

Wang, L., Zhang, C., Zhu, J., & Xu, F. (2022) · Machines · doi:10.3390/machines10040246 · Cited by 22 · see citing works

Asymmetric Identification Model for Human-Robot Contacts via Supervised Learning

Al-Haija, Q. S. A., & Saraireh, J. A. (2022) · Symmetry · doi:10.3390/sym14030591 · Cited by 21 · see citing works

DSMT-1DCNN: Densely supervised multitask 1DCNN for fault diagnosis

Shao, X., Li, D., Ra, I., & Kim, C. S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111609 · Cited by 19 · see citing works

Multi-stream convolutional neural network-based fault diagnosis for variable frequency drives in sustainable manufacturing systems

Grezmak, J. E., Zhang, J., Wang, P. E., & Gao, R. X. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.02.181 · Cited by 19 · see citing works

Time series forecasting for dynamic scheduling of manufacturing processes

Morariu, C., & Borangiu, T. (2018) · IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) · doi:10.1109/aqtr.2018.8402748 · Cited by 18 · see citing works

Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features

Shao, X., Wang, L., Kim, C. S., & Ra, I. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.05.002 · Cited by 18 · see citing works

Intelligent fault diagnosis for rotating machinery using L 1/2 -SF under variable rotational speed

Wang, J., Ji, S., Han, B., & Bao, H. (2020) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/0954407020964625 · Cited by 17 · see citing works

Low-Frequency Adaptation-Deep Neural Network-Based Domain Adaptation Approach for Shaft Imbalance Fault Diagnosis

Arora, J. K., Rajagopalan, S., Singh, J., & Purohit, A. (2023) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-022-00848-7 · Cited by 16 · see citing works

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospectPreprint

Serradilla, O., Zugasti, E., & Zurutuza, U. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03207 · Cited by 16 · see citing works

A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing

Ahmed, Y. S., & Milani, A. S. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100591 · Cited by 17 · see citing works

Recent Advances in Vibration Analysis for Predictive Maintenance of Modern Automotive Powertrains

Shah, R., Mittal, V., & Lotwin, M. (2025) · Vibration · doi:10.3390/vibration8040068 · Cited by 15 · see citing works

A literature review on the artificial intelligence in manufacturing systems under industry 5.0

Pan, Y., Huang, Z., Lev, B., Xu, L., & Olson, D. L. (2026) · International Journal of Production Research · doi:10.1080/00207543.2026.2623537 · Cited by 15 · see citing works

Dealing with missing usage data in defect prediction: A case study of a welding supplier

Gashi, M., Ofner, P., Ennsbrunner, H., & Thalmann, S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103505 · Cited by 15 · see citing works

Invariant Feature Purification Method for Domain Generalization of Rolling Bearing Fault Diagnosis

Xie, Y., Yang, G., Chen, H., Zhao, Z., & Leng, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522623 · Cited by 14 · see citing works

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

Chen, L., Zhang, Z., & Cao, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228324 · Cited by 14 · see citing works

Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning

Zhang, H., Zhang, Q., Shao, S., Niu, T., Yang, X., & Ding, H. (2020) · Shock and Vibration · doi:10.1155/2020/8888627 · Cited by 14 · see citing works

Attention-Enhanced AGRU Framework for Induction Motor Incipient Fault Diagnosis in Electric Vehicles

Saleh, M. A., Ghrayeb, A., Refaat, S. S., Abu‐Rub, H., Khatri, S. P., & Kammermann, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3502781 · Cited by 13 · see citing works

A multi-scale feature fusion network-based fault diagnosis method for wind turbine bearings

Ma, M., Hou, Y., & Li, Y. (2022) · Wind Engineering · doi:10.1177/0309524x221114621 · Cited by 11 · see citing works

Machine Fault Diagnosis Using Random Forest with Recursive Feature Elimination and Cross Validation

Vetrithangam, D., Palit, S., Mehta, A., Saranya, G., Joseph, D., & Pathak, A. (2025) · Journal of Machine and Computing · doi:10.53759/7669/jmc202505134 · Cited by 10 · see citing works

Unsupervised Deep Learning for Fault Detection on Spacecraft Using Improved Variational Autoencoder

Xiang, G., Tao, R., Peng, Y., Tian, K., & Qu, C. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9327846 · Cited by 10 · see citing works

Comparison of model selection and data bias on the prediction performance of purpleback flying squid (Sthenoteuthis oualaniensis) fishing ground in the Northwest Indian Ocean

Han, H., Jiang, B., Xiang, D., Shi, Y., Liu, S., Shang, C., Zhao, X., Zhang, H., & Sun, Y. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2023.111526 · Cited by 9 · see citing works

Integrating Deep Learning Into Mechanical Engineering: A Systematic Review of Applications and Educational Implications

Kollu, G. S., & Kittur, J. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70048 · Cited by 9 · see citing works

Prediction of Failure Categories in Plastic Extrusion Process with Deep Learning

Keskin, F. D., ÇİÇEKLİ, U. G., & İÇLİ, D. (2022) · Journal of Intelligent Systems Theory and Applications · doi:10.38016/jista.878854 · Cited by 9 · see citing works

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Badr, B. E. A., Altawil, I., Almomani, M., Al-Saadi, M., & Alkhurainej, M. (2023) · Mathematical Modelling and Engineering Problems · doi:10.18280/mmep.100523 · Cited by 9 · see citing works

Channel Attention-Based Conditional Diffusion Model Applied to Fault Diagnosis Under Imbalanced Data

Zhang, P., Lin, Y., Cui, H., & Gu, J. (2024) · Electronics · doi:10.3390/electronics13234807 · Cited by 8 · see citing works

Deep Transfer Learning Remaining Useful Life Prediction of Different Bearings

Xu, J., Fang, M., Zhao, W., Fan, Y., & Ding, X. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533676 · Cited by 8 · see citing works

The arc loss challenge: A novel industrial benchmark for process analytics and machine learning

Yousef, I., Rippon, L. D., Prévost, C., Shah, S. L., & Gopaluni, R. B. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103023 · Cited by 8 · see citing works

Condition Monitoring and Fault Diagnosis of Induction Motor Faults using Deep Learning : A Review

Sikinyi, L. A., Muriithi, C. M., & Ngoo, L. M. H. (2023) · IEEE AFRICON · doi:10.1109/africon55910.2023.10293578 · Cited by 8 · see citing works

Deep-Learning and Dynamic Time Warping-Based Approaches for the Diagnosis of Reactor Systems

Jeong, H. Y., Kim, J., Jung, D., & Kwon, J.-W. (2024) · Sensors · doi:10.3390/s24237865 · Cited by 7 · see citing works

Concise convolutional neural network model for fault detection

Firdausi, M., & Ahmad, S. (2022) · Communications in Science and Technology · doi:10.21924/cst.7.1.2022.746 · Cited by 7 · see citing works

Multisensor Data Fusion and Its Application

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Fault Diagnosis of Induction Motors under Limited Data for Across Loading by Residual VGG-Based Siamese Network

Chang, H., Liu, R.-G., Li, C.-C., & Kuo, C. (2024) · Applied Sciences · doi:10.3390/app14198949 · Cited by 9 · see citing works

Deep Belief Networks for Intelligent Fault Diagnosis

Yan, R., & Zhao, Z. (2024) · CRC Press eBooks · doi:10.1201/9781003474463-4

Learning features from vibration signals for induction motor fault diagnosis

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Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor

Katta, P., Karunanithi, K., Raja, S. P., Ramesh, S., Prakash, S. V. J., & Joseph, D. (2024) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31616 · Cited by 5 · see citing works

Multiclass Fault Diagnosis and Novelty Detection of Induction Motor Using Deep Learning Algorithm Based on Frequency Domain Signal

Gupta, A., Tiwari, R., & Bordoloi, D. J. (2023) · Mechanisms and machine science · doi:10.1007/978-981-99-4721-8_8

Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique

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