Dr. Lilach Goren Huber

Work at ZHAW

Position

• Senior Lecturer
• R&D Projects Leader

Teaching

• Master Course "AI for Anomaly Detection in Complex Systems: a Hands-On Tutorial.
• CAS Machine Intelligence: Deep Learning Module

Professional development teaching

CAS Machine Intelligence

Projects

• AI-based Health Prognostics for Battery Energy Storage Systems with Operational Condition Monitoring Data / Project leader / ongoing
• Fault Prognostics under Data Scarcity: Data Augmentation using Transfer Learning / Project leader / ongoing
• Physics-informed machine learning for measurement error correction in elastomer-based sensor systems / Deputy project leader / completed
• Smart Digitalization Demonstrator – predictive calibration and maintenance / Project leader / completed
• An end-to-end fault prognostics solution for reliable power grids using acoustic sensors / Co-project leader / completed
• ZHAW-PARC Hybrid Prognostics Research / Team member / completed
• End-to-End Data Driven Design of After-Sales-Services for Digital Cutters / Team member / completed
• Data Driven Energy Efficiency / Team member / completed
• Automatic Data Selection for Machine Learning based Anomaly Detection / Project leader / completed
• Intelligent Diagnostics of Performance Degradation in Solar Power Plants / Project leader / completed
• Expert Group Smart Maintenance / Project leader / completed
• Convolutional Neural Network Algorithms for Wind Turbine Fault Detection / Project leader / completed
• Data analysis of the potential reduction of the utilization energy in the city of Winterthur. / Project leader / completed
• Machine Learning Based Fault Detection for Wind Turbines / Project leader / completed
• Decision Support System for Predictive Maintenance of Laser Cutting Machines / Project leader / completed
• Optimization of Maintenance Scheduling for Hydroelectric Power Plants / Project leader / completed
• Risk Based Maintenance for safety equipment of Swiss National roads / Team member / completed
• Energy Optimization for Vessel Operations / Team member / completed
• Development of a method for optimized fleet management / Team member / completed
• RENERG2 – RENewable enERGies in future energy supply / Team member / completed
• Optimum asset management of infrastructure networks / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Wernli, S. et al. (2026) ‘Enhancing vortex-flow-meter precision using physics-informed contrastive learning’, Flow Measurement and Instrumentation, 109(103207). doi: 10.1016/j.flowmeasinst.2026.103207.
• Wernli, S. et al. (2025) ‘Coriolis massflow measurement errors due to inhomogeneous entrained particles : an analytical model’, Flow Measurement and Instrumentation, 103(102847). doi: 10.1016/j.flowmeasinst.2025.102847.
• Ulmer, M., Zgraggen, J. and Goren Huber, L. (2024) ‘A generic machine learning framework for fully-unsupervised anomaly detection with contaminated data’, International Journal of Prognostics and Health Management, 15(1). doi: 10.36001/ijphm.2024.v15i1.3589.
• Lehmann, C. et al. (2020) ‘Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms’, Journal of Big Data, 7(1). doi: 10.1186/s40537-020-00340-7.
• Goren Huber, L., Kunz, S. and Dettling, M. (2018) ‘Condition-based maintenance decision making : a practical approach for marine vessels’, International Journal of COMADEM, 21(3), pp. 15–20. Available at: https://apscience.org/comadem/index.php/comadem/article/view/93.

Written conference contributions, peer-reviewed

• Wüest, M. and Goren Huber, L. (2025) ‘Fully unsupervised anomaly detection in industrial images with unknown data contamination’, in 2025 IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 40–47. doi: 10.1109/SDS66131.2025.00013.
• Lüscher, M. F. et al. (2024) ‘Data scarcity in fault detection for solar tracking systems : the power of physics-informed artificial intelligence’, in Do, P. and Ezhilarasu, C. (eds) Proceedings of the PHM Society European Conference 2024. PHM Society, pp. 286–293. doi: 10.36001/phme.2024.v8i1.4059.
• Zgraggen, J. et al. (2023) ‘Fully unsupervised fault detection in solar power plants using physics-informed deep learning’, in Brito, M. P. et al. (eds) Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023). Singapore: Research Publishing, pp. 1737–1745. doi: 10.3850/978-981-18-8071-1_P652-cd.
• Goren Huber, L., Palmé, T. and Arias Chao, M. (2023) ‘Physics-informed machine learning for predictive maintenance : applied use-cases’, in 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 66–72. doi: 10.1109/SDS57534.2023.00016.
• Ulmer, M. et al. (2022) ‘Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study’, in 2022 9th Swiss Conference on Data Science (SDS). IEEE, pp. 40–46. doi: 10.1109/SDS54800.2022.00014.
• Zgraggen, J. et al. (2022) ‘Physics informed deep learning for tracker fault detection in photovoltaic power plants’, in Kulkarni, C. and Saxena, A. (eds) Proceedings of the Annual Conference of the PHM Society 2022. PHM Society. doi: 10.36001/phmconf.2022.v14i1.3235.
• Zgraggen, J., Pizza, G. and Goren Huber, L. (2022) ‘Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data’, in Do, P., Michau, G., and Ezhilarasu, C. (eds) Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022. State College: PHM Society, pp. 530–540. doi: 10.36001/phme.2022.v7i1.3342.
• Zgraggen, J. et al. (2021) ‘Transfer learning approaches for wind turbine fault detection using deep learning’, in Proceedings of the European Conference of the PHM Society 2021. PHM Society, p. 12. doi: 10.21256/zhaw-22774.
• Ulmer, M. et al. (2021) ‘Deep learning for fault detection : the path to predictive maintenance of wind turbines’, in Sammelband zu den 6. Energieforschungsgesprächen Disentis. Disentis: Stiftung Alpines Energieforschungscenter AlpEnForCe, pp. 24–26.
• Ulmer, M. et al. (2020) ‘Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines’, in Proceedings of the Annual Conference of the PHM Society 2020. PHM Society. doi: 10.36001/phmconf.2020.v12i1.1205.
• Ulmer, M. et al. (2020) ‘Early fault detection based on wind turbine SCADA data using convolutional neural networks’, in PHME 2020 : Proceedings of the 5th European Conference of the PHM Society. PHM Society. doi: 10.36001/phme.2020.v5i1.1217.
• Pizza, G. et al. (2020) ‘An AI-based fault detection model using alarms and warnings from the SCADA system’, in Proceedings of the WindEurope Technology Workshop 2020. WindEurope.
• Goren Huber, L., Kunz, S. and Dettling, M. (2017) ‘Condition-based maintenance decision making : a practical approach for marine vessels’, in 30th Conference for Condition Monitoring and Diagnostic Engineering Management, Lancashire, United Kingdom, July 2017. Lancashire: Jost Institute for Tribotechnology, pp. 357–365.
• Heitz, C., Goren, L. and Sigrist, J. (2016) ‘Decision making in asset management : optimal allocation of resources for maximizing value realization’, in Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015). Cham: Springer, pp. 259–268. doi: 10.1007/978-3-319-27064-7_25.

Other publications

• Goren Huber, L. (2024) ‘KI-Trick zur Bereinigung von Maschinendaten’, Aktuelle Technik. Available at: https://www.aktuelle-technik.ch/ki-trick-zur-bereinigung-von-maschinendaten-a-201dd57454851223b1f74e9f5c047dae/?cmp=beleg-mail&pt=673c351e81d26.
• Goren Huber, L. (2024) ‘Kann die KI bei der Fehlererkennung unter realen Bedingungen helfen?’, fmpro service, (4), pp. 6–7. doi: 10.21256/zhaw-31758.
• Goren Huber, L., Palmé, J. T. and Arias Chao, M. (2023) ‘Hybride Instandhaltung : wie fliesst das Fachwissen in die KI?’, fmpro service, 2023(6), pp. 5–7. doi: 10.21256/zhaw-29515.
• Goren Huber, L. and Notaristefano, A. (2022) ‘Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen’, fmpro service, 2022(3), pp. 24–25. doi: 10.21256/zhaw-25292.
• Goren Huber, L. and Pizza, G. (2021) ‘Deep Learning und Predictive Maintenance : Anwendungsfall Windturbinen’, fmpro service, 2021(6), pp. 6–8.
• Schtalheim, U. et al. (2019) Risk Based Maintenance (RBM) : Minimierung der Nutzerrisiken und Betriebskosten mit einer risikobasierten Methode für den Unterhalt der BSA. Bern: Bundesamt für Strassen. doi: 10.21256/zhaw-3341.
• Goren Huber, L. et al. (2017) ‘Predictive Maintenance zum effizienten Betrieb von Hochsee-Schiffen’, fmpro service, pp. 9–11.
• Heitz, C. and Goren Huber, L. (2014) ‘On the economics of asset management’, in Grubbström, R. W. and Hinterhuber, H. H. (eds) Eighteenth international working seminar on production economics : pre-prints. Innsbruck: Kongresszentrum IGLS, pp. 89–102. doi: 10.21256/zhaw-1893.
• Goren Huber, L., Heitz, C. and Sigrist, J. (2014) ‘Anlagenbewirtschaftung und Nutzenmaximierung’, fmpro service, 2014(2), pp. 22–23. doi: 10.21256/zhaw-1886.

Oral conference contributions and abstracts

• Goren Huber, L. (2025) ‘AI for real machines : bridging the research-industry gap’, in 12th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 26-27 June 2025.
• Goren Huber, L. (2024) ‘A data-centric-AI trick to clean your dirty data’, in Detecting unusual or abnormal patterns in data is one of the common tasks of AI algorithms in commercial applications. In some applications, such as fraud detection, defect detection or medical diagnostics, anomaly detection is the main objective. In other applications, detecting abnormal data points is part of the data cleaning and preparation pipeline. In all cases, the use of AI-based methods relies on having a training dataset which can represent the normal behaviour, and must therefore be free of anomalies. Problems arise when we realize that having an anomaly-free training dataset is not always possible in practice: most real-world datasets are contaminated with unknown anomalies or mislabeled data.
• Goren Huber, L. (2023) ‘Deep learning for predictive maintenance : scalable implementation in operational setups’, in 10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023. Available at: https://sds2023.ch/deep-learning-for-predictive-maintenance.
• Goren Huber, L. (2022) ‘Scalable deployment of deep learning algorithms for predictive maintenance in commercial machine fleets : bridging the research-industry gap’, in 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022. Available at: https://phm2022.phmsociety.org/north-america/tutorials/.
• Goren Huber, L., Acquaviva, M. and Pizza, G. (2021) ‘Implementing AI-based innovation in industry’, in Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021.
• Goren Huber, L. (2021) ‘Implementing smart maintenance in industry : deep learning for wind turbine fault detection’, in Expert Group ‘Smart Maintenance’ Meeting, online, 18 March 2021.
• Goren Huber, L. (2020) ‘Intelligente Instandhaltung : Chancen und Herausförderungen für die Umsetzung in der Praxis’, in Smart Maintenance Konferenz, Maintenance Messe, Zürich Oerlikon, 12. - 13. Februar 2020. Available at: https://www.maintenance-schweiz.ch/en/smart-maintenance-conference/.

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