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Olga Fink

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Publications associées (83)

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

Ismail Nejjar, Olga Fink, 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, Gaëtan Michel Frusque, Keivan Faghih Niresi

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

Florent Evariste Forest, Olga Fink

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

Ismail Nejjar, Olga Fink

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

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Hao Lu, Chao Hu, Olga Fink

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity

Olga Fink, Raffael Pascal Theiler

Pumped-storage hydropower plants (PSH) actively participate in grid power-frequency control and therefore often operate under dynamic conditions, which results in rapidly varying system states. Predicting these dynamically changing states is essential for ...

PMH Society2024

Calibrated Adaptive Teacher for Domain-Adaptive Intelligent Fault Diagnosis

Florent Evariste Forest, Olga Fink

Intelligent fault diagnosis (IFD) based on deep learning can achieve high accuracy from raw condition monitoring signals. However, models usually perform well on the training distribution only, and experience severe performance drops when applied to a diff ...

MDPI2024

Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions

Olga Fink, Han Sun

Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition ...

PHM Society2024

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Ismail Nejjar, Olga Fink, Mengjie Zhao

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...

2024

Machine condition monitoring, fault diagnosis/prognosis, and maintenance

Olga Fink

SAGE PUBLICATIONS LTD2024

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Olga Fink

Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault d ...

Ieee-Inst Electrical Electronics Engineers Inc2024

DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems

Olga Fink, Mengjie Zhao

In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying conditions. These complex dynamics make fault detection (FD) particularly c ...

2024

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

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 ...

2024

Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection

Olga Fink, Giovanni Floreale

In the context of automatic visual inspection of infrastructures by drones, Deep Learning (DL) models are used to automatically process images for fault diagnostics. While explainable Artificial Intelligence (AI) algorithms can provide explanations to asse ...

Research Publishing2023

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