Dr. Olga Fink
Tätigkeit an der ZHAW als
Senior Lecturer Reliability and Maintenance Engineering
Lehrtätigkeit in der Weiterbildung
Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse
- Condition-based and Predictive Maintenance of Energy and Transportation Systems
- Prognostics and Health Management (PHM)
- Data-driven Algorithms for Fault Detection and Diagnostics
- Reliability, Availability, Maintainability and Safety (RAMS) of Railway Systems
- Reliable Operation of Public Transportation Systems
Aus- und Fortbildung
04/12 PhD in Civil Engineering, ETH Zürich
PhD thesis “Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems”
Reliability and Maintenance Expert Pöyry Switzerland AG
Research associate at the Institute for Transport Planning and Systems (IVT) ETH Zürich
Reliability Engineer at Stadler Bussnang AG
Mitglied in Netzwerken
- Member of the Board Risk and Safety Association
- Chair of Technical Committee Land Transportation European Safety and Reliability Association (ESRA)
- Decision Support System For Predictive Maintenance of Laser Cutting Machines / Stellv. ProjektleiterIn / 01.05.2017
- Decision support system for predictive maintenance of laser cutting machines / Teammitglied / 01.05.2017
- 1st European COST Conference on Mathematics for Industry in Switzerland / Teammitglied / 01.02.2016
Assessment of maintenance strategies for railway vehicles using Petri-Nets
In: Proceedings of the Euro Working Group on Transportation Meeting. Budapest, Hungary: Euro Working Group on Transportation. Peer reviewed.
2017).; ; (
Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis with Integrated Feature Extraction
IEEE Transactions on Industrial Electronics Peer reviewed.
Michau, Gabriel; ; (2017).
Deep Feature Learning Network for Fault Detection and Isolation
In: Annual conference of the PHM society. St. Petersburg, FL (US): PHM society. Peer reviewed.
2017).; ; (
Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines
Engineering Applications of Artificial Intelligence, 57 105-117. Peer reviewed.
2017).; ; ; ; (
Prognostics and Health Management in Railways
In: Safety & Reliability. Theory and Applications. Portoroz, Slovenia: CRC Press. Peer reviewed.
2016).; ; ; ; ; (
Cluster Analysis of Condition Monitoring Data
In: Risk Reliability and Safety. Innovating Theory and Practice. London: Taylor & Francis. Peer reviewed.
2016).; ; (
Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines
Annual conference of the PHM society Peer reviewed.
2016).; ; (
Maximal information-based nonparametric exploration for condition monitoring data
Third European Conference of the Prognostics and Health Management Society Peer reviewed.
2016).; ; (
Online Sequential Extreme Learning Machines for Fault Detection
In: IEEE International Conference on Prognostics and Health Management. Ottawa: IEEE PHM2016. Peer reviewed.
2015).; ; (
A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data
IEEE Transactions on Reliability, 64, 3. 1049 - 1056. Peer reviewed.
2015).; ; (
Development and Application of Deep Belief Networks for Predicting Railway Operations Disruptions
International Journal of Performability Engineering, 11, 2. 121 - 134. Peer reviewed.
2015).; ; (
Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures
IEEE Transactions on Reliability, 64, 3. 861 - 868. Peer reviewed.
2015).; ; (
Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction
IEEE Transactions on Neural Networks and Learning Systems Peer reviewed.
Beiträge, nicht peer-reviewed
Vorausschauende Instandhaltung der Eisenbahnsysteme mit Hilfe der künstlichen Intelligenz
Eisenbahn-Technische Rundschau, 63(9). 184 – 186.
Publikationen vor Tätigkeit an der ZHAW
PEER REVIEWED SCIENTIFIC JOURNALS
Fink, O., E. Zio, U. Weidmann (2015): Novelty detection by multivariate kernel density estimation and growing neural gas algorithm, Mechanical Systems and Signal Processing, 50-51, 427-436
Fink, O., E. Zio, U. Weidmann (2014): Quantifying the reliability of fault classifiers, Information Sciences, 266, 65-74
Fink, O., E. Zio, U. Weidmann (2014): Predicting component reliability and level of degradation with complex-valued neural networks, Reliability Engineering & System Safety, 121, 198–206
Fink, O., A. Nash, U. Weidmann (2013): Predicting potential railway operational disruptions with echo state networks, Transportation Research Record, 2374, 66-72.
Fink, O., E. Zio, U. Weidmann (2013): Predicting time series of railway speed restrictions with time-dependent machine learning techniques, Expert Systems with Applications, 40 (15), 6033–6040
Fink, O., E. Zio (2013) Semi-Markov processes with semi-regenerative states for the availability analysis of chemical process plants with storage units, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 227 (3), 279-289
REFEREED SCIENTIFIC CONFERENCE PAPERS
Fink, O., E. Zio, U. Weidmann (2013): Extreme learning machines for predicting operation disruption events in railway systems. In Proceedings of the European Safety and Reliability Conference (ESREL), Amsterdam, Netherlands, September 2013
Fink, O., E. Zio, U. Weidmann (2013): Anticipating railway operation disruption events based on the analysis of discrete-event diagnostic data. Chemical Engineering Transactions, 33; Proceedings of the Prognostics and Health Management Conference (PHM), Milan, Italy, September 2013.
Fink, O., U. Weidmann (2013): Deep belief networks for predicting potential railway operations disruptions caused by critical component failures. In Proceedings of the Advances in Risk and Reliability Technology Symposium, Loughborough, UK, Mai 2013.
Fink, O., U. Weidmann (2013): Predicting potential railway operations disruptions caused by critical component failure using echo state neural networks and automatically collected diagnostic data. 92nd Annual Meeting of the Transportation Research Board, Washington, D.C., USA, January 2013.
Fink, O. (2012): Availability analysis of chemical process plants with storage units applying semi-Markov processes with semi-regenerative states. In Proceedings of the European Safety and Reliability Conference (ESREL) Helsinki, Finland, June 2012.
Fink, O., Weidmann, U. (2011) Scope and potential of applying artificial neural networks in reliability prediction with a focus on railway rolling stock. In Proceedings of European Safety and Reliability Conference (ESREL) Troyes, France, September 2011.
Fink, O., Weidmann, U. (2011): Review of artificial neural network applications in reliability prediction with a focus on design phase and railway rolling stock applications, In Proceedings of the 17th ISSAT international Conference Reliability & Quality in Design, Vancouver, Canada, August 2011
Fink, O., U. Weidmann, D. Hofmann, A. Krolo (2011): Potential of applying artificial neural networks in analysis and forecasting of reliability (in German). In Proceedings of the conference for technical reliability, Stuttgart, Germany, Mai 2011
O. Fink (2014) Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems, Monograph series 165, IVT, ETH Zurich, Zurich
Carrasco N., O. Fink and U. Weidmann (2012) Operational stability and reliability of urban bus routes in Zurich, Switzerland, Monograph series 156, IVT, ETH Zurich, Zurich