Publications de Florent Evariste Forest

Interpretable prognostics with concept bottleneck models

Deep learning approaches have recently been extensively explored for the prognostics of industrial assets. However, they still suffer from a lack of interpretability, which hinders their adoption in safety-critical applications. To improve their trustworth ...

2025

ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades

Mariam Ahmed Mahmoud Hegazy Hassan, Florent Evariste Forest, Olga Fink

Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, i ...

2025

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

Florent Evariste Forest, Olga Fink, Gaëtan Michel Frusque

Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfun ...

2025

Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-training Strategies and Performance Insights

Florent Evariste Forest, Olga Fink

This paper focuses on source-free domain adaptation for object detection in computer vision. This task is challenging and of great practical interest, due to the cost of obtaining annotated data sets for every new domain. Recent research has proposed vario ...

Springer Science and Business Media Deutschland GmbH2025

Exploiting semantic scene reconstruction for estimating building envelope characteristics

Florent Evariste Forest, Olga Fink

Precise assessment of geometric building envelope characteristics is essential for parametric simulation analysis and informed retrofitting decisions. Previous methods for estimating building characteristics, such as window-to-wall ratio and building footp ...

Elsevier BV2025

Assessing Aircraft Engine Wear through Simulation Techniques

Florent Evariste Forest

In the field of aeronautical engineering, understanding and simulating aircraft engine performance is critical, especially for improving operational safety, efficiency, and sustainability. At Safran Aircraft Engines, we were able to demonstrate the effecti ...

Prognostics and Health Management Society2024

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Florent Evariste Forest, Yunhong Che

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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

One-Pass Generation of Multivariate Time Series through Conditional Multivariate Modeling

Florent Evariste Forest

In recent years, exploring deep generative models for generating time series has garnered significant interest within the research community. These models have found wide-ranging applications in areas such as data augmentation, scenario simulation, and the ...

Institute of Electrical and Electronics Engineers Inc.2024

Knowledge-based and Expert Systems in Prognostics and Health Management: a Survey

Florent Evariste Forest

Prognostics and Health Management (PHM) has become increasingly popular in recent years, and data-driven methods and artificial intelligence have emerged as dominant tools within the PHM field. This trend is mainly due to the increasing use of sensors and ...

2024

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

Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations

Devis Tuia, Hugo Laurent Pascal Porta, Florent Evariste Forest, Olga Fink

Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

Research Publishing2023

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

Florent Evariste Forest, Olga Fink

Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various ap ...

2023

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Florent Evariste Forest, Yunhong Che

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide t ...

Elsevier Sci Ltd2023