Work

Performance monitoring and wear comprehension through Neural Network

Year: 2025

Type: conference-paper

Abstract: In this paper, we present a novel approach to modeling the wear of complex dynamic systems, exemplified by aircraft engines, through the construction of a structured latent space.Unlike traditional methods, our model does not rely on explicit wear data but instead leverages supervised training to minimize the error on observable system parameters.Beyond wear forecasting, this work offers a foundation for unsupervised diagnosis, risk prevention, and the quantification of repair impacts.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-110

Authors: Thomas Binet, Hanane Azzag, Mustapha Lebbah, Jérôme Lacaille

Institutions: Centre National de la Recherche Scientifique, Université Sorbonne Paris Nord, Laboratoire d'Informatique de Paris-Nord, Safran Aircraft Engines (France), Université Paris-Saclay, Données et algorithmes pour une ville intelligente et durable

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480461

FWCI:

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