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Olga Fink

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Publications associées (83)

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Sayan Ghosh, Chao Hu, Olga Fink

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively m ...

SPRINGER2023

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Learning Physics-Consistent Particle Interactions

David Simon Kammer, Zhichao Han, Olga Fink

Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hi ...

Oxford University Press2023

Complex-Valued-Autoencoder for Structural Health Monitoring with Frequency Modulated Continuous Wave Radar

Olga Fink, Gaëtan Michel Frusque

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequence ...

Research Publishing2023

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

Olga Fink, Gaëtan Michel Frusque

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learnin ...

2023

Controlled generation of unseen faults for Partial and Open-Partial domain adaptation

Olga Fink

New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome suc ...

2023

Graph neural networks for dynamic modeling of roller bearings

Olga Fink, Vinay Sharma

Machine learning has paved the way for the real-time monitoring of complex infrastructure and industrial systems. However, purely data-driven methods have not been able to learn the underlying dynamics and generalize them to operating conditions that have ...

PHM Society2023

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

Olga Fink, Mina Montazeri

This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its reve ...

2023

Fusing physics-based and deep learning models for prognostics

Olga Fink

Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited ...

2023

Real-time model calibration with deep reinforcement learning

Olga Fink

The real-time, and accurate inference of model parameters is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, f ...

2022

Learnable Wavelet Packet Transform for Data-Adapted Spectrograms

Olga Fink, Gaëtan Michel Frusque

Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically contain time dependencies ranging over different time scales and different type ...

2022

Maintenance scheduling of manufacturing systems based on optimal price of the network

Olga Fink

Goods can exhibit positive externalities impacting decisions of customers in social networks. Suppliers can integrate these externalities in their pricing strategies to increase their revenue. Besides optimizing the prize, suppliers also have to consider t ...

2022

A prescriptive Dirichlet power allocation policy with deep reinforcement learning

Olga Fink

Prescribing optimal operation based on the condition of the system, and thereby potentially prolonging its remaining useful lifetime, has tremendous potential in terms of actively managing the availability, maintenance, and costs of complex systems. Reinfo ...

2022

DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning

Ismail Nejjar, Olga Fink, Gaëtan Michel Frusque

Detecting anomalies in sound data has recently received significant attention due to the increasing number of implementations of sound condition monitoring solutions for critical assets. In this context, changing operating conditions impose significant dom ...

2022

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Peter Molnar, Silvan Ragettli, Olga Fink, Yi Zhang

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their a ...

ELSEVIER2022

Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study

Olga Fink, Gaëtan Michel Frusque

Circuit breakers (CBs) play an important role in modern society because they make the power transmission and distribution systems reliable and resilient. Therefore, it is important to maintain their reliability and to monitor their operation. A key to ensu ...

2022

Artificial intelligence across company borders

Olga Fink

Enabling effective cross-company AI without data disclosure. ...

2022

Multi-agent maintenance scheduling of generation unit in electricity market using safe deep reinforcement learning algorithm

Olga Fink

Improving maintenance scheduling of generation units in an electricity market would increase the safety and reliability of the system. This problem can be modeled as a multi-agent bi-level decision-making problem associated with some safety constraints. In ...

2022

Acceleration-Guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring

Olga Fink, Gaëtan Michel Frusque

Acoustic monitoring has recently shown great potential in the diagnosis of infrastructure conditions. However, due to the severe noise interference in acoustic signals, meaningful features tend to be difficult to infer. It creates a considerable obstacle t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

A comprehensive review of digital twin - part 1: modeling and twinning enabling technologies

Sayan Ghosh, Chao Hu, Olga Fink

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively mo ...

SPRINGER2022

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