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pro vyhledávání: '"Ramasso, Emmanuel"'
This paper investigates the use of deep transfer learning based on convolutional neural networks (CNNs) to monitor the condition of bolted joints using acoustic emissions. Bolted structures are critical components in many mechanical systems, and the
Externí odkaz:
http://arxiv.org/abs/2405.20887
Autor:
Ramasso, Emmanuel, Nkogo, Martin Mbarga, Chandarana, Neha, Bourbon, Gilles, Moal, Patrice Le, Lefebvre, Quentin, Personeni, Martial, Soutis, Constantinos, Gresil, Matthieu
Structural Health Monitoring (SHM) relies on non-destructive techniques such as Acoustic Emission (AE) which provide a large amount of data over the life of the systems. The analysis of these data is often based on clustering in order to get insights
Externí odkaz:
http://arxiv.org/abs/2312.13416
Publikováno v:
MDPI Data 2022, 7(3), 31
The data set presented in this work, called ORION-AE, is made of raw AE data streams collected by three different AE sensors and a laser vibrometer during five campaigns of measurements by varying the tightening conditions of two bolted plates submit
Externí odkaz:
http://arxiv.org/abs/2111.06322
Publikováno v:
Mechanical Systems and Signal Processing, Vol. 181, 109504, 2022
The interpretation of unlabeled acoustic emission (AE) data classically relies on general-purpose clustering methods. While several external criteria have been used in the past to select the hyperparameters of those algorithms, few studies have paid
Externí odkaz:
http://arxiv.org/abs/2108.11211
Publikováno v:
European Conference of the PHM Society 2016, selected for extended version in IJPHM
[This paper was initially published in PHME conference in 2016, selected for further publication in International Journal of Prognostics and Health Management.] This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM) for fault d
Externí odkaz:
http://arxiv.org/abs/2105.00211
Publikováno v:
In Mechanical Systems and Signal Processing 1 December 2022 181
Autor:
Seychal, Guillem, Ramasso, Emmanuel, Le Moal, Patrice, Bourbon, Gilles, Gabrion, Xavier, Placet, Vincent
Publikováno v:
In Composites Part B 1 May 2022 236
Autor:
Ramasso, Emmanuel
Cette thèse porte sur la problématique de reconnaissance automatique de systèmes dynamiques. Une méthodologie basée sur des modèles de séquences d'états est employée : les états permettent de décrire le système à un instant particulier t
Externí odkaz:
http://tel.archives-ouvertes.fr/tel-00260770
http://tel.archives-ouvertes.fr/docs/00/26/07/70/PDF/these_ramasso.pdf
http://tel.archives-ouvertes.fr/docs/00/26/07/70/PDF/these_ramasso.pdf
Autor:
Butaud, Pauline, Le Moal, Patrice, Bourbon, Gilles, Placet, Vincent, Ramasso, Emmanuel, Verdin, Benoit, Joseph, Eric
Publikováno v:
In Sensors and Actuators: A. Physical 1 October 2020 313
Autor:
Ramasso, Emmanuel, Butaud, Pauline, Jeannin, Thomas, Sarasini, Fabrizio, Placet, Vincent, Godin, Nathalie, Tirillò, Jacopo, Gabrion, Xavier
Publikováno v:
In Engineering Applications of Artificial Intelligence April 2020 90