Dr. Lilach Goren Huber

Projects

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

Publications

• 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

• 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; 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,

  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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