Source Themes

Integrating imprecise data in generative models using interval-valued Variational Autoencoders

VAE for interval-values data.

Luciano Sánchez, Nahuel Costa, PhD, Inés Couso, Olivier Strauss

Few-shot generative compression approach for system health monitoring

This work proposes a compression-based approach for problems with limited labeled data.

Nahuel Costa, PhD, Luciano Sánchez

Few-shot generative compression approach for system health monitoring

ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection

This work proposes a novel Deep Learning model based on a Transformer encoder to accurately estimate degradation modes and early detect knee points.

Nahuel Costa, PhD, David Anseán, Matthieu Dubarry, Luciano Sánchez

ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection

Learning remaining useful life with incomplete health information: A case study on battery deterioration assessment

This work proposes a method for developing equipment lifespan estimators that combine incomplete physical and numerical information.

Luciano Sánchez, Nahuel Costa, PhD, Jose Otero, David Anseán, Inés Couso

Physics-informed learning under epistemic uncertainty with an application to system health modeling

This work proposes a methodology for developing deterioration models to estimate the remaining lifetime of a system using physics-informed learning (PIL).

Luciano Sánchez, Nahuel Costa, PhD, Jose Otero, Inés Couso

Simplified models of remaining useful life based on stochastic orderings

This work proposes a method for designing simple models of the remaining lifetime of a system.

Luciano Sánchez, Nahuel Costa, PhD, Inés Couso

Simplified models of remaining useful life based on stochastic orderings

Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance

This work proposes a new methodology for opportunistic diagnosis using machine learning algorithms trained directly on photovoltaic battery charging data.

Matthieu Dubarry, Fahim Yasir, Nahuel Costa, PhD, Dax Matthews

Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

This work proposes a new methodology for opportunistic diagnosis using machine learning algorithms trained directly on photovoltaic battery charging data.

Matthieu Dubarry, Nahuel Costa, PhD, Dax Matthews

Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

Li-ion battery degradation modes diagnosis via Convolutional Neural Networks

This work proposes a new representation of battery data as images, in order to leverage the use of well-established Convolutional Neural Networks for battery degradation diagnosis.

Nahuel Costa, PhD, Luciano Sánchez, David Anseán, Matthieu Dubarry

Li-ion battery degradation modes diagnosis via Convolutional Neural Networks

Variational encoding approach for interpretable assessment of remaining useful life estimation

This work proposes a novel approach based on variational encoding to evaluate aircraft engine monitoring data.

Nahuel Costa, PhD, Luciano Sánchez

Variational encoding approach for interpretable assessment of remaining useful life estimation