Прати
Olga Fink
Olga Fink
Laboratory of Intelligent Maintenance and Operations Systems, EPFL
Верификована је имејл адреса на epfl.ch
Наслов
Навело
Навело
Година
Potential, challenges and future directions for deep learning in prognostics and health management applications
O Fink, Q Wang, M Svensén, P Dersin, WJ Lee, M Ducoffe
Engineering Applications of Artificial Intelligence 92, 103678, 2020
5092020
Continual test-time domain adaptation
Q Wang, O Fink, L Van Gool, D Dai
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
5002022
Fusing physics-based and deep learning models for prognostics
MA Chao, C Kulkarni, K Goebel, O Fink
Reliability Engineering & System Safety 217, 107961, 2022
3382022
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
2622018
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ...
Structural and Multidisciplinary Optimization 65 (12), 354, 2022
2522022
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
M Arias Chao, C Kulkarni, K Goebel, O Fink
Data 6 (1), 5, 2021
2442021
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
2022016
Domain adaptive semantic segmentation with self-supervised depth estimation
Q Wang, D Dai, L Hoyer, L Van Gool, O Fink
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
1592021
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
1562019
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
1492014
Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer
G Michau, O Fink
Knowledge-Based Systems 216, 106816, 2021
1212021
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ...
Structural and multidisciplinary optimization 66 (1), 1, 2023
1182023
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
V Nemani, L Biggio, X Huan, Z Hu, O Fink, A Tran, Y Wang, X Zhang, ...
Mechanical Systems and Signal Processing 205, 110796, 2023
1112023
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms
GR Garcia, G Michau, M Ducoffe, JS Gupta, O Fink
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2022
882022
Integrating expert knowledge with domain adaptation for unsupervised fault diagnosis
Q Wang, C Taal, O Fink
IEEE Transactions on Instrumentation and Measurement 71, 1-12, 2021
842021
Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series
G Michau, G Frusque, O Fink
Proceedings of the National Academy of Sciences 119 (8), e2106598119, 2022
762022
Off-policy reinforcement learning for efficient and effective gan architecture search
Y Tian, Q Wang, Z Huang, W Li, D Dai, M Yang, J Wang, O Fink
European Conference on Computer Vision, 175-192, 2020
732020
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
732019
Missing-class-robust domain adaptation by unilateral alignment
Q Wang, G Michau, O Fink
IEEE Transactions on Industrial Electronics 68 (1), 663-671, 2020
682020
Real-time model calibration with deep reinforcement learning
Y Tian, MA Chao, C Kulkarni, K Goebel, O Fink
Mechanical Systems and Signal Processing 165, 108284, 2022
642022
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