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

Projects

• Physics-informed machine learning for measurement error correction in elastomer-based sensor systems / Deputy project leader / ongoing
• An end-to-end fault prognostics solution for reliable power grids using acoustic sensors / Co-project leader / ongoing
• 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
• ZHAW-PARC Hybrid Prognostics Research / Team member / ongoing
• Smart Digitalization Demonstrator – predictive calibration and maintenance / Project leader / 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

• Wernli, Stephan; Goren Huber, Lilach; Avdelidis, Nicolas P.; Rieder, Alfred,

  2025.

  Coriolis massflow measurement errors due to inhomogeneous entrained particles : an analytical model.

  Flow Measurement and Instrumentation.

  103(102847).

  Available from: https://doi.org/10.1016/j.flowmeasinst.2025.102847

• Ulmer, Markus; Zgraggen, Jannik; Goren Huber, Lilach,

  2024.

  A generic machine learning framework for fully-unsupervised anomaly detection with contaminated data.

  International Journal of Prognostics and Health Management.

  15(1).

  Available from: https://doi.org/10.36001/ijphm.2024.v15i1.3589

• Lehmann, Claude; Goren Huber, Lilach; Horisberger, Thomas; Scheiba, Georg; Sima, Ana-Claudia; Stockinger, Kurt,

  2020.

  Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms.

  Journal of Big Data.

  7(1).

  Available from: https://doi.org/10.1186/s40537-020-00340-7

• Goren Huber, Lilach; Kunz, Simon; Dettling, Marcel,

  2018.

  Condition-based maintenance decision making : a practical approach for marine vessels.

  International Journal of COMADEM.

  21(3), pp. 15-20.

  Available from: https://apscience.org/comadem/index.php/comadem/article/view/93

• Lüscher, Mila Francesca; Zgraggen, Jannik; Guo, Yuyan; Notaristefano, Antonio; Goren Huber, Lilach,

  2024.

  Data scarcity in fault detection for solar tracking systems : the power of physics-informed artificial intelligence[paper].

  In:

  Do, Phuc; Ezhilarasu, Cordelia, eds.,

  Proceedings of the PHM Society European Conference 2024.

  8th European Conference of the Prognostics and Health Management Society (PHME), Prague, Czech Republic, 3-5 July 2024.

  PHM Society.

  pp. 286-293.

  Available from: https://doi.org/10.36001/phme.2024.v8i1.4059

• Zgraggen, Jannik; Guo, Yuyan; Notaristefano, Antonio; Goren Huber, Lilach,

  2023.

  Fully unsupervised fault detection in solar power plants using physics-informed deep learning[paper].

  In:

  Brito, Mário P.; Aven, Terje; Baraldi, Piero; Čepin, Marko; Zio, Enrico, eds.,

  Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023).

  33rd European Safety and Reliability Conference (ESREL), Southampton, United Kingdom, 3-7 September 2023.

  Singapore:

  Research Publishing.

  pp. 1737-1745.

  Available from: https://doi.org/10.3850/978-981-18-8071-1_P652-cd

• Goren Huber, Lilach; Palmé, Thomas; Arias Chao, Manuel,

  2023.

  Physics-informed machine learning for predictive maintenance : applied use-cases[paper].

  In:

  2023 10th IEEE Swiss Conference on Data Science (SDS).

  10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023.

  IEEE.

  pp. 66-72.

  Available from: https://doi.org/10.1109/SDS57534.2023.00016

• Zgraggen, Jannik; Guo, Yuyan; Notaristefano, Antonio; Goren Huber, Lilach,

  2022.

  Physics informed deep learning for tracker fault detection in photovoltaic power plants[paper].

  In:

  Kulkarni, Chetan; Saxena, Abhinav, eds.,

  Proceedings of the Annual Conference of the PHM Society 2022.

  14th Annual Conference of the Prognostics and Health Management Society, Nashville, USA, 1-4 November 2022.

  PHM Society.

  Available from: https://doi.org/10.36001/phmconf.2022.v14i1.3235

• Zgraggen, Jannik; Pizza, Gianmarco; Goren Huber, Lilach,

  2022.

  Uncertainty informed anomaly scores with deep learning : robust fault detection with limited data[paper].

  In:

  Do, Phuc; Michau, Gabriel; Ezhilarasu, Cordelia, eds.,

  Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022.

  7th European PHM, Turin, Italy, 6-8 July 2022.

  State College:

  PHM Society.

  pp. 530-540.

  PHM Society European Conference ; 7.

  Available from: https://doi.org/10.36001/phme.2022.v7i1.3342

• Ulmer, Markus; Zgraggen, Jannik; Pizza, Gianmarco; Goren Huber, Lilach,

  2022.

  Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study[paper].

  In:

  2022 9th Swiss Conference on Data Science (SDS).

  9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022.

  IEEE.

  pp. 40-46.

  Available from: https://doi.org/10.1109/SDS54800.2022.00014

• Zgraggen, Jannik; Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach,

  2021.

  Transfer learning approaches for wind turbine fault detection using deep learning[paper].

  In:

  Proceedings of the European Conference of the PHM Society 2021.

  6th European Conference of the Prognostics and Health Management Society, online, 28 June - 2 July 2021.

  PHM Society.

  pp. 12.

  Available from: https://doi.org/10.21256/zhaw-22774

• Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach,

  2021.

  Deep learning for fault detection : the path to predictive maintenance of wind turbines[paper].

  In:

  Sammelband zu den 6. Energieforschungsgesprächen Disentis.

  Energieforschungsgespräche Disentis 2021, online, 20.-22. Januar 2021.

  Disentis:

  Stiftung Alpines Energieforschungscenter AlpEnForCe.

  pp. 24-26.

• Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Goren Huber, Lilach,

  2020.

  Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines[paper].

  In:

  Proceedings of the Annual Conference of the PHM Society 2020.

  12th Annual Conference of the PHM Society, virtual, 9-13 November 2020.

  PHM Society.

  Available from: https://doi.org/10.36001/phmconf.2020.v12i1.1205

• Ulmer, Markus; Jarlskog, Eskil; Pizza, Gianmarco; Manninen, Jaakko; Goren Huber, Lilach,

  2020.

  Early fault detection based on wind turbine SCADA data using convolutional neural networks[paper].

  In:

  PHME 2020 : Proceedings of the 5th European Conference of the PHM Society.

  5th European Conference of the Prognostics and Health Management Society, Virtual Conference, 27-31 July 2020.

  PHM Society.

  Available from: https://doi.org/10.36001/phme.2020.v5i1.1217

• Pizza, Gianmarco; Notaristefano, Antonio; Fabbri, Gregory Sean; Goren Huber, Lilach,

  2020.

  An AI-based fault detection model using alarms and warnings from the SCADA system[poster].

  In:

  Proceedings of the WindEurope Technology Workshop 2020.

  WindEurope Technology Workshop 2020 : Resource Assessment & Analysis of Operating Wind Farms, online, 8-11 June 2020.

  WindEurope.

• Goren Huber, Lilach; Kunz, Simon; Dettling, Marcel,

  2017.

  Condition-based maintenance decision making : a practical approach for marine vessels[paper].

  In:

  30th Conference for Condition Monitoring and Diagnostic Engineering Management, Lancashire, United Kingdom, July 2017.

  Lancashire:

  Jost Institute for Tribotechnology.

  pp. 357-365.

• Heitz, Christoph; Goren, Lilach; Sigrist, Jörg,

  2016.

  Decision making in asset management : optimal allocation of resources for maximizing value realization[paper].

  In:

  Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015).

  WCEAM 2015, Tampere, Finland, 28-30 September 2015.

  Cham:

  Springer.

  pp. 259-268.

  Lecture Notes in Mechanical Engineering.

  Available from: https://doi.org/10.1007/978-3-319-27064-7_25

• Goren Huber, Lilach,

  2024.

  KI-Trick zur Bereinigung von Maschinendaten.

  Aktuelle Technik.

  Available from: https://www.aktuelle-technik.ch/ki-trick-zur-bereinigung-von-maschinendaten-a-201dd57454851223b1f74e9f5c047dae/?cmp=beleg-mail&pt=673c351e81d26

• Goren Huber, Lilach,

  2024.

  Kann die KI bei der Fehlererkennung unter realen Bedingungen helfen?.

  fmpro service.

  (4), pp. 6-7.

  Available from: https://doi.org/10.21256/zhaw-31758

• Goren Huber, Lilach; Palmé, Jan Thomas; Arias Chao, Manuel,

  2023.

  Hybride Instandhaltung : wie fliesst das Fachwissen in die KI?.

  fmpro service.

  2023(6), pp. 5-7.

  Available from: https://doi.org/10.21256/zhaw-29515

• Goren Huber, Lilach; Notaristefano, Antonio,

  2022.

  Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen.

  fmpro service.

  2022(3), pp. 24-25.

  Available from: https://doi.org/10.21256/zhaw-25292

• Goren Huber, Lilach; Pizza, Gianmarco,

  2021.

  Deep Learning und Predictive Maintenance : Anwendungsfall Windturbinen.

  fmpro service.

  2021(6), pp. 6-8.

• Schtalheim, Uri; Ott, Stefan; Heitz, Christoph; Goren Huber, Lilach,

  2019.

  Risk Based Maintenance (RBM) : Minimierung der Nutzerrisiken und Betriebskosten mit einer risikobasierten Methode für den Unterhalt der BSA.

  Forschungsbericht

  ; 1648.

  Bern:

  Bundesamt für Strassen.

  Available from: https://doi.org/10.21256/zhaw-3341

• Goren Huber, Lilach; Dettling, Marcel; Kunz, Simon; Gsponer, Daniel,

  2017.

  Predictive Maintenance zum effizienten Betrieb von Hochsee-Schiffen.

  fmpro service.

  pp. 9-11.

• Goren Huber, Lilach; Heitz, Christoph; Sigrist, Jörg,

  2014.

  Anlagenbewirtschaftung und Nutzenmaximierung.

  fmpro service.

  2014(2), pp. 22-23.

  Available from: https://doi.org/10.21256/zhaw-1886

• Heitz, Christoph; Goren Huber, Lilach,

  2014.

  On the economics of asset management[paper].

  In:

  Grubbström, Robert W.; Hinterhuber, Hans H., eds.,

  Eighteenth international working seminar on production economics : pre-prints.

  18th International Working Seminar on Production Economics, Innsbruck, 24-28 February 2014.

  Innsbruck:

  Kongresszentrum IGLS.

  pp. 89-102.

  Available from: https://doi.org/10.21256/zhaw-1893

• Goren Huber, Lilach,

  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, Lilach,

  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 from: https://sds2023.ch/deep-learning-for-predictive-maintenance

• Goren Huber, Lilach,

  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 from: https://phm2022.phmsociety.org/north-america/tutorials/

• Goren Huber, Lilach; Acquaviva, Michele; Pizza, Gianmarco,

  2021.

  Implementing AI-based innovation in industry.

  In:

  Live-Case-Workshop for EMBA Digital Transformation, University Zurich, 6 July 2021.

• Goren Huber, Lilach,

  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, Lilach,

  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 from: https://www.maintenance-schweiz.ch/en/smart-maintenance-conference/

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