Kiavash Fathi

ZHAW School of Engineering
Institute of Mechatronic Systems
Technikumstrasse 5
8400 Winterthur

+41 (0) 58 934 77 60
kiavash.fathi@zhaw.ch

Network

ORCID digital identifier

ORCID ID: 0000-0002-1844-8001

Projects

Publications

Articles in scientific journal, peer-reviewed

Fathi, K., van de Venn, H. W., & Honegger, M. (2021). Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot. Sensors, 21(21), 6979. https://doi.org/10.3390/s21216979

Book chapters, peer-reviewed

Fathi, K., & van de Venn, H. W. (2024). Data, models, and performance : a comprehensive guide to predictive maintenance in industrial settings. In T. Bányai (Ed.), Recent topics in maintenance management. IntechOpen. https://doi.org/10.5772/intechopen.1005511

Written conference contributions, peer-reviewed

Fathi, K., Sadurski, M., Waskow, S., Kleinert, T., & van de Venn, H. W. (2026). Next-gen contextual anomaly detection for sustainable and efficient production in Industry 4.0 (J. Barata, K. Madani, & H. Panetto, Eds.; pp. 191–203) [Conference paper]. Springer. https://doi.org/10.1007/978-3-032-15576-4_13
Fathi, K., Sadurski, M., Waskow, S., Kleinert, T., & van de Venn, H. W. (2025). Towards Industry 5.0 : AAS/MLOps-driven model maintenance for data-centric production [Conference paper]. 495–502. https://doi.org/10.5220/0013716300003982
Fathi, K., Stramaglia, M., Ristin, M., Sadurski, M., Kleinert, T., Schönfelder, R., & van de Venn, H. W. (2024). Sustainability in semiconductor production via interpretable and reliable predictions [Conference paper]. IFAC-PapersOnLine, 58(4), 174–179. https://doi.org/10.1016/j.ifacol.2024.07.213
Tinnes, C., Ristin, M., Hohenstein, U., Fathi, K., & van de Venn, H. W. (2024, October 26). From unstructured product descriptions to structured data for Industry 4.0 with ChatGPT. 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). https://doi.org/10.1109/icps59941.2024.10639992
Fathi, K., Corradini, F., Sadurski, M., Silvestri, M., Ristin, M., Laghaei, A., Valtorta, D., Kleinert, T., & van de Venn, H. W. (2024). Domain adaptation via simulation parameter and data perturbation for predictive maintenance [Conference paper]. In P. Do & C. Ezhilarasu (Eds.), Proceedings of the PHM Society European Conference 2024 (Vol. 8, Issue 1, pp. 389–399). PHM Society. https://doi.org/10.36001/phme.2024.v8i1.3985
Fathi, K., Kleinert, T., & van de Venn, H. W. (2024). Trustworthy machine learning operations for predictive maintenance solutions [Conference paper]. In P. Do & C. Ezhilarasu (Eds.), Proceedings of the European Conference of the PHM Society 2024 (Vol. 8, Issue 1, p. 4). Prognostics and Health Management Society. https://doi.org/10.36001/phme.2024.v8i1.3966
Fathi, K., Ristin, M., Sadurski, M., Kleinert, T., & van de Venn, H. W. (2024). Detection of novel asset failures in predictive maintenance using classifier certainty [Conference paper]. 2024 32nd Mediterranean Conference on Control and Automation (MED), 50–56. https://doi.org/10.1109/med61351.2024.10566204
Fathi, K., Sadurski, M., Kleinert, T., & van de Venn, H. W. (2023). Source component shift detection & classification for improved remaining useful life estimation in alarm-based predictive maintenance [Conference paper]. 2023 23rd International Conference on Control, Automation and Systems (ICCAS), 975–980. https://doi.org/10.23919/iccas59377.2023.10316874
Fathi, K., Rezayati, M., & van de Venn, H. W. (2022). Human-robot contact detection in assembly tasks [Conference paper]. 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 224–230. https://doi.org/10.1109/ICMERR56497.2022.10097827
Fathi, K., Darvishy, A., & van de Venn, H. W. (2022). Augmented reality for the visually impaired : navigation aid and scene semantics for indoor use cases. 2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin). https://doi.org/10.1109/ICCE-Berlin56473.2022.9937109

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