Open Access
Issue
EPJ Web Conf.
Volume 343, 2025
1st International Conference on Advances and Innovations in Mechanical, Aerospace, and Civil Engineering (AIMACE-2025)
Article Number 05014
Number of page(s) 9
Section Artificial Intelligence & Machine Learning in Engineering
DOI https://doi.org/10.1051/epjconf/202534305014
Published online 19 December 2025
  1. S.B. Lee, et al., Condition monitoring of industrial electric machines: State of the art and future challenges. IEEE Ind. Electron. Mag. 14, 158 (2020). https://doi.org/10.1109/mie.2020.3016138 [Google Scholar]
  2. O. AlShorman, et al., Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors. Eng. Appl. Artif. Intell. 130 (2024). https://doi.org/10.1016/j.engappai.2023.107724 [Google Scholar]
  3. K.S. Charan, An auto-encoder based tinyml approach for real-time anomaly detection. SAE Int. J. Adv. Curr. Prac. Mobil. 5, 1496 (2023). https://doi.org/10.4271/2022-28-0406 [Google Scholar]
  4. E. Njor, M.A. Hasanpour, J. Madsen, X. Fafoutis, A holistic review of the tinyml stack for predictive maintenance. IEEE Access 12, 184861 (2024). https://doi.org/10.1109/ACCESS. 2024.3512860 [Google Scholar]
  5. R.T. Nguimfack, et al., Domain adaptation between heterogeneous time series data: A case study on real-time rotary machinery fault diagnosis. Manuf. Lett. 41, 1535 (2024). https://doi.org/10.1016/j.mfglet.2024.09.180 [Google Scholar]
  6. Y. Abadade, A. Temouden, H. Bamoumen, N. Benamar, Y. Chtouki, A.S. Hafid, A comprehensive survey on tinyml. IEEE Access 11, 96892 (2023). https://doi.org/10.1109/ACCESS. 2023.3294111 [Google Scholar]
  7. F. Chen, S. Li, J. Han, et al., Review of lightweight deep convolutional neural networks. Arch. Comput. Methods Eng. 31, 1915 (2024). https://doi.org/10.1007/s11831-023-10032-z [Google Scholar]
  8. F. de Almeida Florencio, E.D. Moreno, H.T. Macedo, R.J. de Britto Salgueiro, F.B. do Nascimento, F.A.O. Santos, Intrusion detection via MLP neural network using an arduino embedded system, in Proceedings of the 2018 VIII Brazilian symposium on computing systems engineering (SBESC) (IEEE, 2018), pp. 190–195 [Google Scholar]
  9. P.E. Novac, G. Boukli Hacene, A. Pegatoquet, B. Miramond, V. Gripon, Quantization and deployment of deep neural networks on microcontrollers. Sensors 21 (2021). https://doi.org/10.3390/s21092984 [Google Scholar]
  10. W. Ayadi, A. Saidi, I. Channoufi, Exploring Human Activity Patterns: Investigating Feature Extraction Techniques for Improved Recognition with ANN, in Proceedings of the IEEE International Conference on Advanced Technologies, Signal and Image Processing (ATSIP) (2024), pp. 188–193 [Google Scholar]
  11. D. Mourtzis, J. Angelopoulos, N. Panopoulos, Design and development of an iot enabled platform for remote monitoring and predictive maintenance of industrial equipment. Procedia Manuf. 54, 166 (2021). https://doi.org/10.1016/j.promfg.2021.07.025 [Google Scholar]
  12. P. Gangsar, A.R. Bajpei, R. Porwal, A review on deep learning based condition monitoring and fault diagnosis of rotating machinery. Noise Vib. Worldw. 53, 550 (2022). https://doi.org/10.1177/09574565221139638 [Google Scholar]
  13. S. Tang, S. Yuan, Y. Zhu, Deep learning-based intelligent fault diagnosis methods toward rotating machinery. IEEE Access 8, 9335 (2020). https://doi.org/10.1109/ACCESS. 2019.2963092 [Google Scholar]
  14. S. Lu, G. Qian, Q. He, F. Liu, Y. Liu, Q. Wang, In situ motor fault diagnosis using enhanced convolutional neural network in an embedded system. IEEE Sens. J. 20, 8287 (2020). https://doi.org/10.1109/JSEN.2019.2911299 [Google Scholar]
  15. S. Arciniegas, D. Rivero, J. Pinan, et al., lot device for detecting abnormal vibrations in motors using tinyml. Discovery Internet Things 5, 41 (2025). https://doi.org/10.1007/s43926-025-00142-4 [Google Scholar]
  16. C.Y. Lee, Y.H. Cheng, Motor fault detection using wavelet transform and improved pso-bp neural network. Processes 8 (2020). https://doi.org/10.3390/pr8101322 [Google Scholar]
  17. H. Helmi, A. Forouzantabar, M. Azadi, Intelligent fault detection of electric motor bearing using wavelet packet transform and ed-rbfnn. J. Vib. Control (0). https://doi.org/10.1177/10775463251364314 [Google Scholar]
  18. A. Prudhom, J. Antonino-Daviu, H. Razik, V. Climente-Alarcon, Timefrequency vibration analysis for the detection of motor damages caused by bearing currents. Mech. Syst. Signal Process. 84, 747 (2017). https://doi.org/10.1016/j.ymssp.2015.12.008 [Google Scholar]
  19. N. Ceylan, E. Sönmez, S. Kaçar, Cost effective detection of uneven mounting fault in rotary wing drone motors with a cnn based method. Signal Image Video Process. 18, 8049 (2024). https://doi.org/10.1007/s11760-024-03063-4 [Google Scholar]
  20. D. Papaioannou, et al., Lp-optima: A framework for prescriptive maintenance and optimization of iot resources for low-power embedded systems. Sensors 24, 2125 (2024). https://doi.org/10.3390/s24062125 [Google Scholar]

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