Olga Fink
Olga Fink
Chair of Intelligent Maintenance Systems, ETH Zürich
Verified email at ethz.ch - Homepage
Title
Cited by
Cited by
Year
Predicting component reliability and level of degradation with complex-valued neural networks
O Fink, E Zio, U Weidmann
Reliability Engineering & System Safety 121, 198-206, 2014
782014
Two machine learning approaches for short-term wind speed time-series prediction
R Ak, O Fink, E Zio
IEEE transactions on neural networks and learning systems 27 (8), 1734-1747, 2016
742016
Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction
YQ Chen, O Fink, G Sansavini
IEEE Transactions on Industrial Electronics 65 (1), 561-569, 2018
432018
Novelty detection by multivariate kernel density estimation and growing neural gas algorithm
O Fink, E Zio, U Weidmann
Mechanical Systems and Signal Processing 50, 427-436, 2015
192015
Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines
Y Hu, T Palmé, O Fink
Engineering Applications of Artificial Intelligence 57, 105-117, 2017
162017
Predicting time series of railway speed restrictions with time-dependent machine learning techniques
O Fink, E Zio, U Weidmann
Expert Systems with Applications 40 (15), 6033-6040, 2013
162013
Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures
O Fink, E Zio, U Weidmann
IEEE Transactions on Reliability 64 (3), 861-868, 2015
152015
Domain adaptive transfer learning for fault diagnosis
Q Wang, G Michau, O Fink
2019 Prognostics and System Health Management Conference (PHM-Paris), 279-285, 2019
142019
Deep feature learning network for fault detection and isolation
G Michau, P Thomas, O Fink
PHM 2017, St. Petersburg, USA, 2-5 October 2017, 108-118, 2017
142017
A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data
O Fink, E Zio, U Weidmann
IEEE Transactions on Reliability 64 (3), 1049-1056, 2015
142015
Feature learning for fault detection in high-dimensional condition-monitoring signals
G Michau, Y Hu, T Palmé, O Fink
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2019
122019
Fleet PHM for critical systems: bi-level deep learning approach for fault detection
G Michau, T Palmé, O Fink
Proceedings of the European Conference of the PHM Society 4, 2018
112018
Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines
Y Hu, T Palmé, O Fink
PHM Society, 2016
102016
Quantifying the reliability of fault classifiers
O Fink, E Zio, U Weidmann
Information Sciences 266, 65-74, 2014
92014
Extreme learning machines for predicting operation disruption events in railway systems
O Fink, E Zio, U Weidmann
Proceedings of the European Safety and Reliability Conference, 1-8, 2013
92013
Assessment of maintenance strategies for railway vehicles using Petri-nets
D Eisenberger, O Fink
Transportation Research Procedia 27, 205-214, 2017
72017
Unsupervised Fault Detection in Varying Operating Conditions
G Michau, O Fink
2019 IEEE International Conference on Prognostics and Health Management …, 2019
42019
Online sequential extreme learning machines for fault detection
Y Hu, O Fink, T Palmé
2016 IEEE International Conference on Prognostics and Health Management …, 2016
42016
Development and Application of Deep Belief Networks for Predicting Railway Operation Disruptions
O FINK, E ZIO, U WEIDMANN
International Journal of Performability Engineering 11 (2), 121, 2015
42015
Semi-Markov processes with semi-regenerative states for the availability analysis of chemical process plants with storage units
O Fink, E Zio
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2013
42013
The system can't perform the operation now. Try again later.
Articles 1–20