Kiavash Fathi

Arbeit an der ZHAW

Arbeits- und Forschungsschwerpunkte

Industrial data-driven solutions for production plants​

Lehrtätigkeit in der Weiterbildung

WBK Digitaler Produktpass (DPP)

Netzwerk

ORCID digital identifier

ORCID ID: 0000-0002-1844-8001

Projekte

• PRIME – Process Refinement for Improved Manufacturing Efficiency / Stellv. Projektleiter:in / laufend
• SmartAssets - Digital Transformation in Industry 4.0 / Teammitglied / laufend
• Cybathlon 2024 / Teammitglied / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

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

Buchbeiträge, peer-reviewed

Fathi, K. and van de Venn, H. W. (2024) 'Data, models, and performance : a comprehensive guide to predictive maintenance in industrial settings', in Bányai, T. (ed.) Recent topics in maintenance management. London: IntechOpen. doi: 10.5772/intechopen.1005511.

Schriftliche Konferenzbeiträge, peer-reviewed

• Fathi, K. et al. (2026) 'Next-gen contextual anomaly detection for sustainable and efficient production in Industry 4.0', in Barata, J., Madani, K., and Panetto, H. (eds). Cham: Springer, pp. 191–203. doi: 10.1007/978-3-032-15576-4_13.
• Fathi, K. et al. (2025) 'Towards Industry 5.0 : AAS/MLOps-driven model maintenance for data-centric production'. SciTePress, pp. 495–502. doi: 10.5220/0013716300003982.
• Fathi, K. et al. (2024) 'Sustainability in semiconductor production via interpretable and reliable predictions', in IFAC-PapersOnLine. Elsevier, pp. 174–179. doi: 10.1016/j.ifacol.2024.07.213.
• Tinnes, C. et al. (2024) 'From unstructured product descriptions to structured data for Industry 4.0 with ChatGPT', in 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE. doi: 10.1109/icps59941.2024.10639992.
• Fathi, K., Kleinert, T. and van de Venn, H. W. (2024) 'Trustworthy machine learning operations for predictive maintenance solutions', in Do, P. and Ezhilarasu, C. (eds) Proceedings of the European Conference of the PHM Society 2024. Prognostics and Health Management Society, p. 4. doi: 10.36001/phme.2024.v8i1.3966.
• Fathi, K. et al. (2024) 'Detection of novel asset failures in predictive maintenance using classifier certainty', in 2024 32nd Mediterranean Conference on Control and Automation (MED). IEEE, pp. 50–56. doi: 10.1109/med61351.2024.10566204.
• Fathi, K. et al. (2024) 'Domain adaptation via simulation parameter and data perturbation for predictive maintenance', in Do, P. and Ezhilarasu, C. (eds) Proceedings of the PHM Society European Conference 2024. PHM Society, pp. 389–399. doi: 10.36001/phme.2024.v8i1.3985.
• Fathi, K. et al. (2023) 'Source component shift detection & classification for improved remaining useful life estimation in alarm-based predictive maintenance', in 2023 23rd International Conference on Control, Automation and Systems (ICCAS). IEEE, pp. 975–980. doi: 10.23919/iccas59377.2023.10316874.
• Fathi, K., Darvishy, A. and van de Venn, H. W. (2022) 'Augmented reality for the visually impaired : navigation aid and scene semantics for indoor use cases', in 2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin). IEEE. doi: 10.1109/ICCE-Berlin56473.2022.9937109.
• Fathi, K., Rezayati, M. and van de Venn, H. W. (2022) 'Human-robot contact detection in assembly tasks', in 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR). IEEE, pp. 224–230. doi: 10.1109/ICMERR56497.2022.10097827.

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