Issue
Mechanics & Industry
Volume 27, 2026
Robotic Process Automation for Smarter Devices in Manufacturing
Article Number 12
Number of page(s) 13
DOI https://doi.org/10.1051/meca/2026010
Published online 03 April 2026
  1. T. Arjunan, A comparative study of deep neural networks and support vector machines for unsupervised anomaly detection in cloud computing environments, Int. J. Res. Appl. Sci. Eng. Technol. 12, 10–22214 (2024) [Google Scholar]
  2. A.F. Bonatti, G. Vozzi, C.K. Chua, C. De Maria, A deep learning approach for error detection and quantification in extrusion-based bioprinting, Mater. Today Proc. 70, 131–135 (2022) [Google Scholar]
  3. W.H. Aljuaid, S.S. Alshamrani, A deep learning approach for intrusion detection systems in cloud computing environments, Appl. Sci. 14, 5381 (2024) [Google Scholar]
  4. M. Li, Y. Li, T. Wang, F. Chu, Z. Peng, Adaptive synchronous demodulation transform with application to analyzing multicomponent signals for machinery fault diagnostics, Mech. Syst. Signal Process. 191, 110208 (2023) [CrossRef] [Google Scholar]
  5. I. Ahmed, M. Ahmad, A. Chehri, G. Jeon, A smart-anomaly-detection system for industrial machines based on feature autoencoder and deep learning, Micromachines 14, 154 (2023) [Google Scholar]
  6. O.R. Polu, AI-driven prognostic failure analysis for autonomous resilience in cloud data centers, J. ID 2563, 4512 (2024) [Google Scholar]
  7. W. He, J. Hang, S. Ding, L. Sun, W. Hua, Robust diagnosis of partial emagnetization fault in PMSMs using radial air-gap flux density under complex working conditions, IEEE Trans. Ind. Electron. 71, 12001–12010 (2024) [Google Scholar]
  8. Y. Li et al., Load profile inpainting for missing load data restoration and baseline estimation, IEEE Trans. Smart Grid 15, 2251–2260 (2024) [Google Scholar]
  9. SK. Baduge et al., Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications, Autom. Constr. 141, 104440 (2022) [CrossRef] [Google Scholar]
  10. F. Harrou, A. Dairi, B. Taghezouit, B. Khaldi, Y. Sun, Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring, Energy Convers. Manag. 314, 118665 (2024) [Google Scholar]
  11. A.M. Abdallah, A.S.R.O. Alkaabi, G.B.N. Douman Alameri, S.H. Rafique, N.S. Musa, T. Murugan, Cloud network anomaly detection using machine and deep learning techniques—recent research advancements, IEEE Access 12, 56749–56773 (2024) [Google Scholar]
  12. Y. Chen, H. Li, Y. Song, X. Zhu, Recoding hybrid stochastic numbers for preventing bit width accumulation and fault tolerance, IEEE Trans. Circuits Syst. I 72, 1243–1255 (2025) [Google Scholar]
  13. S. Ahmad, I. Shakeel, S. Mehfuz, J. Ahmad, Deep learning models for cloud, edge, fog, and IoT computing paradigms: survey, recent advances, and future directions, Comput. Sci. Rev. 49, 100568 (2023) [Google Scholar]
  14. M.M. Khan, Developing AI-powered intrusion detection system for cloud infrastructure, J. Artif. Intell. Mach. Learn. Data Sci. 2, 1074–1080 (2024) [Google Scholar]
  15. M.A. Duhayyim et al., Evolutionary-based deep stacked autoencoder for intrusion detection in a cloud-based cyber-physical system, Appl. Sci. 12, 146875 (2022) [Google Scholar]
  16. Y. Liu, M. Huo, M. Li, L. He, N. Qi, Establishing a digital twin diagnostic model based on cross-device transfer learning, IEEE Trans. Instrum. Meas. 74, 1–10 (2025) [Google Scholar]
  17. F. Harrou, B. Bouyeddou, A. Dairi, Y. Sun, Exploiting autoencoder-based anomaly detection to enhance cybersecurity in power grids, Future Internet 16, 184 (2024) [Google Scholar]
  18. C. Nwachukwu, K. Durodola-Tunde, A.-U. Chukwuebuka, AI-driven anomaly detection in cloud computing environments, Int. J. Sci. Res. Arch. 13, 692–710 (2024) [Google Scholar]
  19. S.M. Rajagopal, S.M.R. Buyya, FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments, Internet Things 22, 100784 (2023) [Google Scholar]
  20. A.A. Soomro et al., Insights into modern machine learning approaches for bearing fault classification: A systematic literature review, Results Eng. 23, 102700 (2024) [Google Scholar]
  21. H. Wang, Y.-F. Li, T. Men, L. Li, Physically interpretable wavelet-guided networks with dynamic frequency decomposition for machine intelligence fault prediction, IEEE Trans. Syst. Man Cybern. Syst. 54, 4863–4875 (2024) [Google Scholar]
  22. H. Chen, X.-B. Wang, Z.-X. Yang, J. Li, Privacy-preserving intelligent fault diagnostics for wind turbine clusters using federated stacked capsule autoencoder, Expert Syst. Appl. 254, 124256 (2024) [Google Scholar]
  23. S.S. Raoof, M.A.S. Durai, A Comprehensive Review on Smart Health Care: Applications, paradigms, and challenges with case studies, Contrast Media Mol. Imaging 2022, 4822235 (2022) [Google Scholar]
  24. Z. Tian, A. Lee, S. Zhou, Adaptive tempered reversible jump algorithm for Bayesian curve fitting, Inverse Probl. 40, 045024 (2024) [Google Scholar]
  25. S. Ghazimoghadam, S.A.A. Hosseinzadeh, A novel unsupervised deep learning approach for vibration-based damage diagnosis using a multi-head self-attention LSTM autoencoder, Measurement 229, 114410 (2024) [Google Scholar]
  26. S. Zhang et al., UAV based defect detection and fault diagnosis for static and rotating wind turbine blade: a review, Nondestruct. Test. Eval. 40, 1691–1729 (2025) [Google Scholar]
  27. M. Shimizu, S. Perinpanayagam, B. Namoano, A real-time fault detection framework based on unsupervised deep learning for prognostics and health management of railway assets, IEEE Access 10, 96442–96458 (2022) [Google Scholar]
  28. H. Wang, S. Wu, F. Yu, Y. Bi, Z. Xu, Study on remaining useful life prediction of sliding bearings in nuclear power plant shielded pumps based on nearest similar distance particle filtering, SSRN 5071931 (2024) [Google Scholar]
  29. A.A. Soomro et al., A situation based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of industry 4.0, IEEE Access 12, 34800–34819 (2024) [Google Scholar]
  30. A. Ucar, M. Karakose, N. Kırımça, Artificial intelligence for predictive maintenance applications: key components, trustworthiness, and future trends, Appl. Sci. 14, 898 (2024) [Google Scholar]
  31. N. Li, H. Wang, Variable filtered-waveform variational mode decomposition and its application in rolling bearing fault feature extraction, Entropy 27, 30277 (2025) [Google Scholar]
  32. H. Wu, A. Huang, J.W. Sutherland, Condition-based monitoring and novel fault detection based on incremental learning applied to rotary systems, Procedia CIRP 105, 788–793 (2022) [Google Scholar]
  33. H. Nagarajan, Integrating cloud computing with big data: Novel techniques for fault detection and secure checker design, Int. J. Inf. Technol. Comput. Eng. 12, 928–939 (2024) [Google Scholar]
  34. K.Y. Chan et al., Deep neural networks in the cloud: Review, applications, challenges and research directions, Neurocomputing 545, 126327 (2023) [CrossRef] [Google Scholar]
  35. F. Ning, Z. Li, J. Lu, Y. Wang, Y. Niu, Y. Shi, 3D CAD model dynamic clustering based on inertial feature encoder, Appl. Soft Comput. 182, 113627 (2026) [Google Scholar]
  36. A.L. Yaser, H.M. Mousa, M. Hussein, Improved DDoS detection utilizing deep neural networks and feedforward neural networks as autoencoder, Future Internet 14, 240 (2022) [Google Scholar]
  37. K. Jain, K. Mahant, Intelligent network optimization: A machine learning approach to dynamic network management in telecommunications, Sarcouncil J. Multidiscip. (2024) [Google Scholar]
  38. M.J. Pasha, K.P. Rao, A. MallaReddy, V. Bande, LRDADF: An AI enabled framework for detecting low-rate DDoS attacks in cloud computing environments, Meas. Sens. 28, 100828 (2023) [Google Scholar]
  39. X. Bi, R. Qin, D. Wu, S. Zheng, J. Zhao, One step forward for smart chemical process fault detection and diagnosis, Comput. Chem. Eng. 164, 107884 (2022) [Google Scholar]
  40. M.N. Hasan, S.U. Jan, I. Koo, Sensor fault detection and classification using multi-step-ahead prediction with an long short-term Memory (LSTM) autoencoder, Appl. Sci. 14, 14177717 (2024) [Google Scholar]
  41. M. Ozkan-Okay et al., A comprehensive survey: evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions, IEEE Access 12, 12229–12256 (2024) [Google Scholar]
  42. T.H.H. Aldhyani, H. Alkahtani, Artificial intelligence algorithm-based economic denial of sustainability attack detection systems: cloud computing environments, Sensors 22, 34685 (2022) [Google Scholar]
  43. T.R. Mahesh, S. Chandrasekaran, V.A. Ram, V.V. Kumar, V. Vivek, S. Guluwadi, Data-driven intelligent condition adaptation of feature extraction for bearing fault detection using deep responsible active learning, IEEE Access 12, 45381–45397 (2024) [Google Scholar]
  44. M.R. Islam et al., Deep learning and computer vision techniques for enhanced quality control in manufacturing processes, IEEE Access 12, 121449–121479 (2024) [Google Scholar]
  45. F. Hodavand, I.J. Ramaji, N. Sadeghi, Digital twin for fault detection and diagnosis of building operations: a systematic review, Buildings 13, 1426 (2023) [Google Scholar]

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