Prof. (asst. TUD) Dr. Manuel Arias Chao

Arbeit an der ZHAW

Tätigkeit

• Senior Lecturer & Researcher
• Director of studies CAS Instandhaltungsmanagement

Lehrtätigkeit

• Master Course "AI for Anomaly Detection in Complex Systems: a Hands-On Tutorial.
• Bachelor Course "Maintenance"
• Bachelor Course "Traffic Systems Operations"
• Certificate of Advanced Studies (CAS) in Predictive Maintenance
• Certificate of Advanced Studies (CAS) Instandhaltungsmanagement

Lehrtätigkeit in der Weiterbildung

CAS Instandhaltungsmanagement

Berufserfahrung

• Assistant Professor at the Facualty of Aerospace, Operation and Environment Group
  Delft University of Technology
  05 / 2025 - heute
• Visiting Researcher, Operation and Environment Group
  Delft University of Technology
  10 / 2022 - 04 / 2025
• PhD Candidate/Scientific Assistant on Predictive Maintenance
  ETH Zurich
  07 / 2017 - 08 / 2021
• Visiting Scientist, Diagnostics & Prognostics Group
  NASA Ames Research Center
  01 / 2019 - 03 / 2019
• Research Associate, Predictive Maintenance
  Zurich University of Applied Sciences
  07 / 2017 - 09 / 2018
• Lead Engineer, Thermo-Economic Optimization
  GE Power
  11 / 2016 - 06 / 2017
• Lead Engineer, Gas Turbine Thermodynamics
  GE Power
  11 / 2015 - 11 / 2016
• R&D Performance Owner / Work Package Coordinator
  ALSTOM Power Ltd
  10 / 2013 - 11 / 2015
• R&D Performance Engineer
  ALSTOM Power Ltd
  10 / 2008 - 10 / 2013
• Computational Fluid Dynamics (CFD) Engineer
  FLUENT France SAS
  07 / 2006 - 09 / 2007
• Aero-engine Maintenance Engineer
  Industria de Turbopropulsores S.A.
  08 / 2005 - 08 / 2006

Aus- und Weiterbildung

Ausbildung

• Doctor of Science (Dr. sc.)
  ETH Zurich
  2017 - 2021
• CAS in Risk Management for Banking and Finance
  University of Zurich
  2015 - 2016
• MSc. in Thermal Power
  Cranfield University
  2007 - 2008

Netzwerk

Auszeichnungen

• Best Paper Award at PHM Society Conference 2023 in Salt Lake City
  Prognostics and Health Management Society
  10 / 2023
• Early Career Award at PHM Society Conference 2022 in Nashville
  Prognostics and Health Management Society
  11 / 2022
• Best Paper Award 2021
  Journal "Data" by MDPI
  09 / 2021
• Best Papers Award at 2015 Turbo Expo in Montreal
  Structures & Dynamics Probabilistic Methods Committee.
  06 / 2015

Social Media

Linkedin

Projekte

• AI-Enhanced Acoustic Monitoring for Pumps and Transformer Components / Projektleiter:in / laufend
• Hybrid Aircraft Maintenance Program / Projektleiter:in / laufend
• KI-basierte Prognosen für Batterie-Energiespeichersysteme mit Zustandsüberwachungsdaten / Stellv. Projektleiter:in / laufend
• Zustandsüberwachung von Generatoren / Stellv. Projektleiter:in / abgeschlossen
• An end-to-end fault prognostics solution for reliable power grids using acoustic sensors / Projektleiter:in / abgeschlossen
• Smart Maintenance Nuclear Power Plants / Projektleiter:in / abgeschlossen
• ZHAW-PARC Hybrid Prognostics Research / Projektleiter:in / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Navidi, S. et al. (2025) 'Forecasting battery capacity for second-life applications using physics-informed recurrent neural networks', eTransportation, 25(100432). doi: 10.1016/j.etran.2025.100432.
• Pierre, D. et al. (2025) 'Analytical health indices : towards reliability-informed deep learning for PHM', International Journal of Prognostics and Health Management, 16(2). doi: 10.36001/ijphm.2025.v16i2.4262.
• Llasag Rosero, R. et al. (2025) 'Label synchronization strategies for hybrid federated learning', Reliability Engineering & System Safety, 256(110751). doi: 10.1016/j.ress.2024.110751.
• Basora, L. et al. (2025) 'A benchmark on uncertainty quantification for deep learning prognostics', Reliability Engineering & System Safety, 253(110513). doi: 10.1016/j.ress.2024.110513.
• Bajarunas, K. et al. (2024) 'Health index estimation through integration of general knowledge with unsupervised learning', Reliability Engineering & System Safety, 251(110352). doi: 10.1016/j.ress.2024.110352.

Schriftliche Konferenzbeiträge, peer-reviewed

• Goglio, D., Zarouchas, D. and Arias Chao, M. (2025) 'PhD Symposium : Interpretable and uncertainty-aware hybrid prognostics using multimodal knowledge for RUL prediction', in Kulkarni, C. S. and Orchard, M. E. (eds) Proceedings of the Annual Conference of the PHM Society 2025. New York: PHM Society. doi: 10.36001/phmconf.2025.v17i1.4605.
• Käslin, B. et al. (2025) 'Integrated stochastic optimization of maintenance scheduling and tail assignment with health-aware models in aviation', in Annual Conference of the PHM Society. PHM Society. doi: 10.36001/phmconf.2025.v17i1.4610.
• Chawla, P., Kulkarni, C. and Arias Chao, M. (2025) 'Ensuring UAS airworthiness : deep learning-based acoustic health monitoring of motor health'. EUROCONTROL. doi: 10.5281/zenodo.20159666.
• 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.

Weitere Publikationen

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.

Publikationen vor Tätigkeit an der ZHAW

• M. Arias Chao, C. Kulkarni, K. Goebel, O. Fink (2022). Fusing Physics-based and Deep Learning Models for Prognostics, Reliability Engineering & System Safety 217, p. 107961. issn: 0951-8320.
• Y. Tian, M. Arias Chao, C. Kulkarni, K. Goebel, O. Fink (2022). Real-Time Model Calibration with Deep Reinforcement Learning, Mechanical Systems and Signal rocessing. 165, p. 108284. issn: 0888-3270
• M. Arias Chao (2021). Combining Deep Learning and Physics-Based Performance Models for Diagnostics and Prognostics. Diss. ETH Zurich, 2021.
• L. Biggio, A. Wieland, M. Arias Chao, I. Kastanis, O. Fink, Iason Kastanis, and Olga Fink (2021). Uncertainty-Aware Prognosis via Deep Gaussian Process, IEEE Access 9, pp. 123517–123527
• U. Unagar, Y. Tian, M. Arias Chao, O. Fink (2021). Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning. Energies, 14, 1361. https://doi.org/10.3390/en14051361
• M. Arias Chao, C. Kulkarni, K. Goebel, O. Fink (2021). Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics. Data, 6, 5. https://doi.org/10.3390/data6010005
• M. Arias Chao, B. T. Adey, O. Fink (2021). Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders, Neurocomputing. issn: 0925-2312
• L. Biggio, M. Arias Chao, O. Fink (2020). Uncertainly-aware Remaining Useful Life Predictors, NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation and Design
• M. Arias Chao, C. Kulkarni, K. Goebel, O. Fink (2019). Hybrid Deep Fault Detection and Isolation: Combining Deep Neural Networks and System Performance Models, International Journal of Prognostics and health Management, 10, 033
• M. Arias Chao, W. Reiter, and D. S. Lilley (2017). Method for operating a power plant. U.S. Patent Application 15/241,269, filed February 23, 2017.
• M. Arias Chao, B. Wippel, C. Balmer, and R. Jakoby (2017). Method for operating a gas turbine plant and gas turbine plant for implementing the method. U.S. Patent 9,752,504, issued September 5, 2017.
• M. Arias Chao, P. Mathé, et al. (2015). Calibration and Uncertainty Quantification of Gas Turbines Performance Models, ASME paper GT2015-42392
• M. Arias Chao, A. Nemet, U. R. Steiger, and D. S. Lilley (2015). Method for determining at least one firing temperature for controlling a gas turbine and gas turbine for performing the method. U.S. Patent Application 14/491,172, filed 2015.
• R. C. Payne, M. Arias Chao, V. Stefanis (2014). A Novel Intake Concept for Flue Gas Recirculation to Enhance CCS in an Industrial Gas Turbine, ASME paper GT2014-25469
• L. Gallar, M. Arias Chao, V. Pachidis, R. Singh (2011). Stochastic Axial Compressor Variable Geometry Schedule Optimisation, Aerospace Science and Technology, 15 (5) 366-374
• L. Gallar, M. Arias Chao, V. Pachidis, P. Pilidis (2009). Compressor Variable Geometry Schedule Optimisation Using Genetic Algorithms, ASME paper GT2009-60049

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