Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Zhong, R. Y., Xu, X. W., Klotz, E., & Newman, S. T. (2017) · Engineering · doi:10.1016/j.eng.2017.05.015 · Cited by 3,045

Convolutional Neural Network Based Fault Detection for Rotating Machinery

Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Rik Van De Walle, B., & Hoecke, S. V. (2016) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2016.05.027 · Cited by 1,283

Hyper-Parameter Optimization: A Review of Algorithms and ApplicationsPreprint

Yu, T., & Zhu, H. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05689 · Cited by 894

A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., & Si, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2818678 · Cited by 596

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Delgado-Prieto, M., Cirrincione, G., Espinosa, A. G., Ortega, J. A., & Henao, H. (2012) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2219838 · Cited by 462

Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

Hu, Q., He, Z., Zhang, Z., & Zi, Y. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.01.007 · Cited by 402

Challenges, Opportunities and Future Directions of Smart Manufacturing: A State of Art Review

Phuyal, S., Bista, D., & Bista, R. (2020) · Sustainable Futures · doi:10.1016/j.sftr.2020.100023 · Cited by 401

Recent advances in decision trees: an updated survey

Costa, V. G., & Pedreira, C. E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10275-5 · Cited by 648

Advanced Applications of Industrial Robotics: New Trends and Possibilities

Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228

Review and empirical analysis of sparrow search algorithm

Yue, Y., Cao, L. J., Lu, D., Hu, Z., Xu, M., Wang, S., Li, B., & Ding, H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10435-1 · Cited by 226

Industry 4.0 digital transformation and opportunities for supply chain resilience: a comprehensive review and a strategic roadmap

Ghobakhloo, M., Iranmanesh, M., Foroughi, B., Tseng, M., Nikbin, D., & Khanfar, A. A. A. (2023) · Production Planning & Control · doi:10.1080/09537287.2023.2252376 · Cited by 219

Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0—A Survey

de Azambuja, A. J. G., Plesker, C., Schützer, K., Anderl, R., Schleich, B., & Almeida, V. R. (2023) · Electronics · doi:10.3390/electronics12081920 · Cited by 194

Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties

Elsisi, M., Tran, M., Mahmoud, K., Mansour, D. A., Lehtonen, M., & Darwish, M. M. F. (2022) · Measurement · doi:10.1016/j.measurement.2021.110686 · Cited by 171

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare, P., Meshram, C. Y., Lee, C., Ramteke, R. J., & Imoize, A. L. (2021) · Sensors · doi:10.3390/s22010224 · Cited by 157

Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects

Tran, M., Doan, H.-P., Vu, V. Q., & Lien, V. T. (2022) · Measurement · doi:10.1016/j.measurement.2022.112351 · Cited by 149

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Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025) · Applied Sciences · doi:10.3390/app15063166 · Cited by 149

Insulator defect detection with deep learning: A survey

Liu, Y., Liu, D., Huang, X., & Li, C. (2023) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12916 · Cited by 141

Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification

Tran, M., Elsisi, M., Liu, M., Vu, V. Q., Mahmoud, K., Darwish, M. M. F., Abdelaziz, A. Y., & Lehtonen, M. (2022) · IEEE Access · doi:10.1109/access.2022.3153471 · Cited by 123

Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning

Cao, D., Zhao, J., Hu, W., Ding, F., Yu, N., Huang, Q., & Chen, Z. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117982 · Cited by 112

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Awaisi, K. S., Ye, Q., & Sampalli, S. (2024) · IEEE Access · doi:10.1109/access.2024.3426279 · Cited by 95

Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles

Elsisi, M., & Tran, M. (2021) · Sensors · doi:10.3390/s21248467 · Cited by 87

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Lilhore, U. K., Poongodi, M., Simaiya, S., Alroobaea, R., Alsafyani, M., Baqasah, A. M., Dalal, S., Sharma, A. K., & Raahemifar, K. (2023) · Sensors · doi:10.3390/s23187856 · Cited by 87

Intelligent fault diagnostic system for rotating machinery based on IoT with cloud computing and artificial intelligence techniques: a review

Maurya, M., Panigrahi, I., Dash, D., & Malla, C. (2023) · Soft Computing · doi:10.1007/s00500-023-08255-0 · Cited by 75

Multi-modal information analysis for fault diagnosis with time-series data from power transformer

Xing, Z., & He, Y. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108567 · Cited by 74

IoT Security Based on Machine Learning

Vanakamamidi, R. K., Ramalingam, L., Abirami, N., Priyanka, S., Kumar, C. S., & Murugan, S. (2023) · International Conference On Smart Technologies For Smart Nation (SmartTechCon) · doi:10.1109/smarttechcon57526.2023.10391727 · Cited by 69

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Espina-Romero, L. C., Hurtado, H. G., Parra, D. E. R., Pirela, R. A. V., Talavera-Aguirre, R., & Ochoa-Díaz, A. (2024) · Sci · doi:10.3390/sci6040060 · Cited by 64

Trends in Smart Grid Cyber-Physical Security: Components, Threats, and Solutions

Manias, D. M., Saber, A. M., Radaideh, M. I., Gaber, A. T., Maniatakos, M., Zeineldin, H. H., Svetinović, D., & El-Saadany, E. F. (2024) · IEEE Access · doi:10.1109/access.2024.3477714 · Cited by 51

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Ruan, H., Dorneanu, B., Arellano‐García, H., Xiao, P., & Zhang, L. (2022) · IEEE Access · doi:10.1109/access.2022.3144333 · Cited by 49

Applications of deep learning algorithms for Supervisory Control and Data Acquisition intrusion detection system

Balla, A., Habaebi, M. H., Islam, M. R., & Mubarak, S. (2022) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2022.100532 · Cited by 48

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Singh, S. K., Tiwari, A. K., & Paliwal, H. K. (2023) · Engineering Analysis with Boundary Elements · doi:10.1016/j.enganabound.2023.06.003 · Cited by 48

Pollution Severity Monitoring of High Voltage Transmission Line Insulators Using Wireless Device Based on Leakage Current Bursts

Gouda, O. E.-S., Darwish, M. M. F., Mahmoud, K., Lehtonen, M., & Elkhodragy, T. M. (2022) · IEEE Access · doi:10.1109/access.2022.3175515 · Cited by 45

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Hamzah, M. L., Purwati, A. A., Sutoyo, S., Marsal, A., Sarbani, S., & Nazaruddin, N. (2022) · Computer Science and Information Technologies · doi:10.11591/csit.v3i3.p194-202 · Cited by 40

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Fuertes, G., Zamorano, J., Alfaro, M. D., Vargas, M., Sabattin, J., Durán, C., Ternero, R., & Rivera, R. (2022) · Sustainability · doi:10.3390/su141811118 · Cited by 38

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Issa, R., Clerc, G., Hologne, M., Michaud, R., Lorca, E., Magnette, C., & Messadi, A. (2024) · Energies · doi:10.3390/en17112728 · Cited by 36

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Toma, R. N., Piltan, F., Im, K., Shon, D., Yoon, T. H., Yoo, D.-S., & Kim, J.-M. (2022) · Sensors · doi:10.3390/s22134881 · Cited by 37

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Rudenko, R., Pires, I. M., Oliveira, P., Barroso, J., & Reis, A. (2022) · Electronics · doi:10.3390/electronics11111742 · Cited by 33

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Awasthi, M. A., Mahesh, T. R., Joshi, R. C., Pandey, A. K., Saxena, R. M., & Goswami, S. (2022) · International Journal of Communication Networks and Information Security (IJCNIS) · doi:10.17762/ijcnis.v14i3.5600 · Cited by 33

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Ioannides, M. G., Stamelos, A. P., Papazis, S. A., Stamataki, E. E., & Stamatakis, M. E. (2024) · Energies · doi:10.3390/en17030645 · Cited by 29

A hybrid fuzzy based approach for industry 4.0 framework implementation strategy and its sustainability in Indian automotive industry

Jena, A., & Patel, S. K. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.138369 · Cited by 28

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Hanzelik, P. P., Kummer, A., & Abonyi, J. (2022) · Sensors · doi:10.3390/s22114268 · Cited by 26

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Calderon-Uribe, U., Lizarraga-Morales, R. A., & Guryev, I. V. (2024) · Machines · doi:10.3390/machines12080497 · Cited by 25

Extracting interpretable building control rules from multi-objective model predictive control data sets

Yu, M., & Pavlak, G. S. (2021) · Energy · doi:10.1016/j.energy.2021.122691 · Cited by 25

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Sharma, G., Kaur, T., Mangal, S. K., & Kohli, A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103499 · Cited by 21

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Singh, D., Biswal, A. K., Samanta, D., Singh, D., & Lee, H.-N. (2022) · Sustainability · doi:10.3390/su14020939 · Cited by 20

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Hussain, R., Saleh, M. A., & Refaat, S. S. (2025) · IEEE Access · doi:10.1109/access.2025.3600570 · Cited by 19

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Aswad, R. A. K., Jassim, B. M. H., Zahawi, B., & Gadoue, S. M. (2023) · IEEE Access · doi:10.1109/access.2023.3297096 · Cited by 19

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Roy, P. P., Teju, V., Kandula, S. R., Sowmya, K. V., Stan, A., & Stan, O. P. (2024) · Electronics · doi:10.3390/electronics13030669 · Cited by 19

A hybrid approach for gearbox fault diagnosis based on deep learning techniques

Bessaoudi, M., Habbouche, H., Benkedjouh, T., & Mesloub, A. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13873-5 · Cited by 19

The effect of Ni on Fe and Al impurities by MIVM model for the silicon production

Zhu, Y., Chen, Z., Zhang, H., Ma, W., & Wu, J. (2022) · Energy · doi:10.1016/j.energy.2022.124459 · Cited by 19

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images

Liu, Y., & Huang, X. (2024) · Sensors · doi:10.3390/s24020428 · Cited by 18

Securing Cloud-Encrypted Data: Detecting Ransomware-as-a-Service (RaaS) Attacks through Deep Learning Ensemble

Singh, A. S. G., Abosaq, H. A., Arif, S., Mushtaq, Z., Irfan, M., Abbas, G., Ali, A., & Mazroa, A. A. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048036 · Cited by 16

Fuzzy entropy and hesitancy entropy in probabilistic hesitant fuzzy information and their applications

Xu, T., Zhang, H., & Li, B.-Q. (2022) · Soft Computing · doi:10.1007/s00500-022-07309-z · Cited by 16

A design of predictive manufacturing system in IoT‐assisted Industry 4.0 using heuristic‐derived deep learning

Murugiah, P., Muthuramalingam, A., & Anandamurugan, S. (2023) · International Journal of Communication Systems · doi:10.1002/dac.5432 · Cited by 16

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Kaid, H., Al-Ahmari, A. M., & Alqahtani, K. N. (2023) · Machines · doi:10.3390/machines11020173 · Cited by 15

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Hadraoui, H. E., Maghraoui, A. E., Laayati, O., Sabani, E., Zegrari, M., & Chebak, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106157 · Cited by 16

A test-time adaptation method using evidential deep learning for online machinery fault diagnosis

Tian, J., Yu, Y., Karimi, H. R., & Lin, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114831 · Cited by 14

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Jiang, S., Li, Q., Gan, R., & Chen, W. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12040255 · Cited by 14

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Alaguraj, R., & Kathirvel, C. (2024) · Electric Power Components and Systems · doi:10.1080/15325008.2023.2293944 · Cited by 14

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Hsu, S.-H., Lee, C.-H., Wu, W.-F., & Jiang, J. (2024) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2024.3405897 · Cited by 14

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 14

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Hadraoui, H. E., Laayati, O., Maghraoui, A. E., Sabani, E., Zegrari, M., & Chebak, A. (2023) · Global Power, Energy and Communication Conference · doi:10.1109/gpecom58364.2023.10175674 · Cited by 14

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Kang, R., Wang, J., Chen, J., Zhou, J., Pang, Y., & Cheng, J. (2021) · IEEE Access · doi:10.1109/access.2021.3113381 · Cited by 13

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Muthurajkumar, S., & Nisha, S. R. (2023) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2023.1.4478 · Cited by 13

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Nastro, F., Sorrentino, M., & Trifirò, A. (2022) · Energy · doi:10.1016/j.energy.2022.123266 · Cited by 13

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Vlachou, E. I., Vlachou, V. I., Efstathiou, D. E., & Karakatsanis, T. S. (2024) · Machines · doi:10.3390/machines12120839 · Cited by 12

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Ansari, M. J. A., Alahmed, Y., & Hesham, H. (2024) · International Conference on Software Defined Systems (SDS) · doi:10.1109/sds64317.2024.10883898 · Cited by 12

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Sharma, R. A., & Singh, A. J. (2022) · Advances in Technology Innovation · doi:10.46604/aiti.2022.8538 · Cited by 12

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Bedi, P., Goyal, S. B., Rajawat, A. S., Bhaladhare, P., Aggarwal, A., & Prasad, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.059 · Cited by 9

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Barrera, K., Sapena-Bañó, Á., Martínez‐Román, J., & Puche‐Panadero, R. (2023) · International Journal of Electrical and Computer Engineering Research · doi:10.53375/ijecer.2023.319 · Cited by 6

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