Olga Fink

Publications associées (83)

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Digital Twin Generalization with Meta and Geometric Deep Learning

Olga Fink, Raffael Pascal Theiler

Deep digital twins (DDTs) are deep neural networks that encode the behavior of complex physical systems. DDTs are excellent system representations due to their ability to continuously adapt to operational changes and their capability to capture complex rel ...

Prognostics and Health Management Society2024

Semisupervised Health Index Monitoring With Feature Generation and Fusion

Olga Fink, Ismail Nejjar, Gaëtan Michel Frusque

—The health index (HI) is crucial for evaluating system health, important for tasks, such as anomaly detection and remaining useful life prediction of safety-critical systems. Real-time, meticulous monitoring of system conditions is essential, especially i ...

2024

Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation

Lukas Kühn, Olga Fink, Keivan Faghih Niresi, Gaëtan Michel Frusque

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across var ...

IEEE2024

From classification to segmentation with explainable AI: A study on crack detection and growth monitoring

Olga Fink, Florent Evariste Forest

Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically ...

2024

IM-Context: In-Context Learning for Imbalanced Regression Tasks

Olga Fink, Ismail Nejjar

Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gradient-based weight updates, which tend to overfit in underrepresented regio ...

arXiv2024

Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks

Olga Fink, Mengjie Zhao

Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load monitori ...

PHM Society2024

GEMTELLIGENCE: Accelerating gemstone classification with deep learning

Olga Fink

The value of luxury goods, particularly investment-grade gemstones, is influenced by their origin and authenticity, often resulting in differences worth millions of dollars. Traditional methods for determining gemstone origin and detecting treatments invol ...

2024

NNG-Mix: Improving Semi-Supervised Anomaly Detection with Pseudo-Anomaly Generation

Olga Fink, Gaëtan Michel Frusque

Anomaly detection (AD) is essential in identifying rare and often critical events in complex systems, finding applications in fields such as network intrusion detection, financial fraud detection, and fault detection in infrastructure and industrial system ...

2024

Deep Koopman Operator-based degradation modelling

Olga Fink, Sergei Garmaev

Developing reliable health indicators for industrial assets is essential for accurate condition monitoring, fault detection, and predicting the remaining useful lifetime. However, constructing such indicators is challenging, especially given the increasing ...

Elsevier BV2024

Collective relational inference for learning heterogeneous interactions

Olga Fink

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying con ...

Nature Portfolio2024

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