EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 27 (2026) Mechanics & Industry, 27 (2026) 5 References
Issue
Mechanics & Industry
Volume 27, 2026
Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer
Article Number 5
Number of page(s) 13
DOI https://doi.org/10.1051/meca/2026001
Published online 27 February 2026
  1. Y. Wang, J. Yan, Z. Yang, Z. Qi, J. Wang, Y. Geng, A novel hybrid meta-learning for few-shot gas-insulated switchgear insulation defect diagnosis, Expert Syst. Appl. 233, 120956 (2023) [Google Scholar]
  2. G.V. Xavier, H. S. Silva, E.G. Da Costa, A.J. Serres, N.B. Carvalho, A.S. Oliveira, Detection, classification and location of sources of partial discharges using the radiometric method: trends, challenges and open issues, IEEE Access 9, 110787–110810 (2021) [Google Scholar]
  3. T. Shahsavarian, Y. Pan, Z. Zhang, C. Pan, H. Naderiallaf, J. Guo, Y. Cao, A review of knowledge-based defect identification via PRPD patterns in high voltage apparatus, IEEE Access 9, 77705–77728 (2021) [Google Scholar]
  4. Z. Faizol, F. Zubir, N.M. Saman, M.H. Ahmad, M.K.A. Rahim, O. Ayop, Z. Yusoff, Detection method of partial discharge on transformer and gas-insulated switchgear: a review, Appl. Sci. 13, 9605 (2023) [Google Scholar]
  5. N. Rosle, N.A. Muhamad, M.N.K.H. Rohani, M.K.M. Jamil, Partial discharges classification methods in XLPE cable: a review, IEEE Access 9, 133258–133273 (2021) [Google Scholar]
  6. N.A. Muhamad, I.V. Musa, Z.A. Malek, A.S. Mahdi, Classification of partial discharge fault sources on SF6 insulated switchgear based on twelve by-product gases random forest pattern recognition, IEEE Access 8, 212659–212674 (2020) [Google Scholar]
  7. A.S. Mahdi, Z. Abdul-Malek, R.N. Arshad, SF6 decomposed component analysis for partial discharge diagnosis in GIS: A review, IEEE Access 10, 27270–27288 (2022) [Google Scholar]
  8. Z. Wu, B. Lyu, Q. Zhang, L. Liu, J. Zhao, Phase-space joint resolved PD characteristics of defects on insulator surface in GIS, IEEE Trans. Dielectr. Electr. Insul. 27, 156–163 (2020) [Google Scholar]
  9. F. Zeng, B. Xie, D. Su, C. Li, Z. Lei, G. Ma, J. Tang, Breakdown characteristics of eco-friendly gas C5F1₀O/CO2 under switching impulse in nonuniform electric field, IEEE Trans. Dielectr. Electr. Insul. 29, 866–873 (2022) [Google Scholar]
  10. H. Li, K. Zhao, J. Yang, S. Gao, Contact defect detection of gas insulated line via thermal-vibration feature fusion and deep neural network technique, IEEE Trans. Instrum. Meas. (2023) [Google Scholar]
  11. D. Nguyen, Analytical Modeling and Simulation of Capacitive Micromachined Ultrasonic Transducer, 2020 [Google Scholar]
  12. H.K. Sandhu, S.S. Bodda, A. Gupta, A future with machine learning: review of condition assessment of structures and mechanical systems in nuclear facilities, Energies 16, 2628 (2023) [Google Scholar]
  13. S. Mantach, Supervised and Unsupervised Deep Learning Models for Partial Discharge Source Detection and Classification in Electrical Insulation, 2023 [Google Scholar]
  14. L. Duan, J. Hu, G. Zhao, K. Chen, J. He, S.X. Wang, Identification of partial discharge defects based on deep learning method, IEEE Trans. Power Deliv. 34, 1557–1568 (2019) [Google Scholar]
  15. Z. Wu, Q. Zhang, J. Ma, X. Li, T. Wen, Effectiveness of on-site dielectric test of GIS equipment, IEEE Trans. Dielectr. Electr. Insul. 25, 1454–1460 (2018) [Google Scholar]
  16. A. H. Alshalawi, F. S. Al-Ismail, Partial discharge detection based on ultrasound using optimized deep learning approach, IEEE Access (2024) [Google Scholar]
  17. Y. Jin, H. Wu, J. Zheng, J. Zhang, Z. Liu, Power transformer fault diagnosis based on improved BP neural network, Electronics 12, 3526 (2023) [Google Scholar]
  18. V.N. Tuyet-Doan, Y.W. Youn, H. S. Choi, Y.H. Kim, Shared knowledge-based contrastive federated learning for partial discharge diagnosis in gas-insulated switchgear, IEEE Access (2024) [Google Scholar]
  19. A.S. Karandaev, I.M. Yachikov, A.A. Radionov, I.V. Liubimov, N.N. Druzhinin, E. A. Khramshina, Fuzzy algorithms for diagnosis of furnace transformer insulation condition, Energies 15, 3519 (2022) [Google Scholar]
  20. J. Li, A. Zhang, W. Yang, Research on state prediction of gas insulated switchgear based on CNN-GRU combinatorial neural network, in: 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2), 2022, pp. 2294–2298 [Google Scholar]
  21. Y.A.M. Alsumaidaee, C.T. Yaw, S.P. Koh, S. K. Tiong, C.P. Chen, T. Yusaf, A.A. Raj, Detection of corona faults in switchgear by using 1D-CNN, LSTM, and 1D-CNN-LSTM methods, Sensors 23, (2023) [Google Scholar]
  22. X. Bampoula, N. Nikolakis, K. Alexopoulos, Condition monitoring and predictive maintenance of assets in manufacturing using LSTM-autoencoders and transformer encoders, Sensors 24, 3215 (2024) [Google Scholar]
  23. W. Hu, J. Li, X. Liu, G. Li, Partial discharge fault identification method for GIS equipment based on improved deep learning, J. Eng. 2024, e12386 (2024). [Google Scholar]
  24. W. Sun, H. Ma, S. Wang, (2024). A Novel Fault Diagnosis of GIS Partial Discharge Based on Improved Whale Optimization Algorithm, IEEE Access. (2024) [Google Scholar]
  25. Y. Cong, K. Tang, Z. Li, Z. Liu, B. Chen, Y. Liu, Y. Fang, GIS equipment fault identification based on BP neural network and improved DS evidence fusion, J. Electr. Eng. 18, 361–369 (2024) [Google Scholar]
  26. A. Pulikkathodi, E. Lacazedieu, L. Chamoin et al., A neural network-based data-driven local modeling of spotwelded plates under impact, Mech. Ind. 24, 34 (2023) [Google Scholar]
  27. T. Xia, L. Zhou, L. Quan, The study on surface defect detection of stamped parts based on improved deep learning, Mech. Ind. 26, 27 (2025) [Google Scholar]
  28. https://data.mendeley.com/datasets/cz8gwg9d2v/1 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.