Issue
Mechanics & Industry
Volume 27, 2026
Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer
Article Number 13
Number of page(s) 15
DOI https://doi.org/10.1051/meca/2026007
Published online 03 April 2026
  1. R. Bayindir, I. Colak, G. Fulli, K. Demirtas, Smart grid technologies and applications, Renew. Sustain. Energy Rev. 66, 499–516 (2016) [Google Scholar]
  2. H. Ge, S. Asgarpoor, Reliability and maintainability improvement of substations with aging infrastructure, IEEE Trans. Power Delivery, 27, 1868–1876 (2012) [Google Scholar]
  3. Y. Chen, M. Rao, K. Feng, M.J. Zuo, Physics-informed LSTM hyperparameters selection for gearbox fault detection, Mech. Syst. Signal Process. 171, 108907 (2022) [Google Scholar]
  4. C. Chen, T. Wang, K. Lu, Y. Liu, L. Cheng, Compact convolutional transformers-generative adversarial network for compound fault diagnosis of industrial robot, Eng. Appl. Artif. Intell. 138, 109315 (2024) [Google Scholar]
  5. T. Wang, W. Xu, C. Chen, Z. Wang, Z. Chen, Progressive hypergraph structure learning for fault diagnosis of industrial robots, IEEE Trans. Instrum. Meas. (2025) [Google Scholar]
  6. M. Massaoudi, H. Abu-Rub, S.S. Refaat, I. Chihi, F.S. Oueslati, Deep learning in smart grid technology: a review of recent advancements and future prospects, IEEE Access, 9, 54558–54578 (2021) [Google Scholar]
  7. S.M. Ribeiro, C. Castro, Missing data in time series: a review of imputation methods and case study, Learn. Nonlinear Model, 20, 31–46 (2022) [Google Scholar]
  8. S. Wang et al., Timemixer++: a general time series pattern machine for universal predictive analysis, arXiv preprint arXiv:2410.16032, 2024 [Google Scholar]
  9. Q. Team, Qwen2 technical report, arXiv preprint arXiv:2407.10671, 2, (2024) [Google Scholar]
  10. Y. Xiao, H. Shao, S. Han, Z. Huo, J. Wan, Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis from Simulation Domain to Experimental Domain, IEEE/ASME Transactions on Mechatronics, 2022 [Google Scholar]
  11. K. Lu, C. Chen, T. Wang, L. Cheng, J. Qin, Fault diagnosis of industrial robot based on dual-module attention convolutional neural network, Auton. Intell. Syst. 2, 1–12 (2022) [Google Scholar]
  12. G. Helbing, M. Ritter, Deep learning for fault detection in wind turbines, Renew. Sustain. Energy Rev. 98, 189–198 (2018) [Google Scholar]
  13. S.R. Saufi, Z.A.B. Ahmad, M.S. Leong, M.H. Lim, Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review, IEEE Access, 7, 122644–122662 (2019) [CrossRef] [Google Scholar]
  14. R. Iqbal, T. Maniak, F. Doctor, C. Karyotis, Fault detection and isolation in industrial processes using deep learning approaches, IEEE Trans. Ind. Inform. 15, 3077–3084 (2019) [Google Scholar]
  15. G. Bode, S. Thul, M. Baranski, D. Müller, Real-world application of machine-learning-based fault detection trained with experimental data, Energy, 198, 117323 (2020) [Google Scholar]
  16. S.U. Jan, Y.D. Lee, I.S. Koo, A distributed sensor-fault detection and diagnosis framework using machine learning, Inform. Sci. 547, 777–796 (2021) [Google Scholar]
  17. J. Sun, C. Yan, J. Wen, Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning, IEEE Trans. Instrum. Meas. 67, 185–195 (2017) [Google Scholar]
  18. C. Zhang, S.R. Kuppannagari, R. Kannan, V.K. Prasanna, Generative adversarial network for synthetic time series data generation in smart grids, in: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, 2018, pp. 1–6 [Google Scholar]
  19. M.-C. Wang, C.-F. Tsai, W.-C. Lin, Towards missing electric power data imputation for energy management systems, Exp. Syst. Appl. 174, 114743 (2021) [Google Scholar]
  20. M. Weber, M. Turowski, H.K. Çakmak, R. Mikut, U. Kühnapfel, V. Hagenmeyer, Data-driven copy-paste imputation for energy time series, IEEE Trans. Smart Grid, 12, 5409–5419 (2021) [Google Scholar]
  21. J.F. Schreiber, A. Sausen, M. De Campos, P.S. Sausen, M.T.D.S. Ferreira Filho, Data imputation techniques applied to the smart grids environment, IEEE Access, 11, 31931–31940 (2023) [Google Scholar]
  22. D. Vasenin, M. Pasetti, D. Astolfi, N. Savvin, S. Rinaldi, A. Berizzi, Incorporating Seasonal Features in Data Imputation Methods for Power Demand Time Series, IEEE Access, 2024 [Google Scholar]
  23. R. Razavi-Far, M. Farajzadeh-Zanjani, M. Saif, S. Chakrabarti, Correlation clustering imputation for diagnosing attacks and faults with missing power grid data, IEEE Trans. Smart Grid, 11, 1453–1464 (2019) [Google Scholar]
  24. C. Fu, M. Quintana, Z. Nagy, C. Miller, Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation, Appl. Thermal Eng. 236, 121545 (2024) [Google Scholar]
  25. S. Nayak et al., Data imputation using self attention based model for enhancing distribution grid monitoring and protection systems, IEEE Trans. Instrum. Meas. 73, 1–11 (2024) [Google Scholar]
  26. J. Fattahi, Real-time data imputation for low inertia nanogrid digital twins, in: 2024 IEEE 10th World Forum on Internet of Things (WF-IoT), IEEE, 2024, pp. 654–659 [Google Scholar]

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