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Team

Signal automatique pour la surveillance, le diagnostic et la biomécanique
Team manager : Franck QUAINE, Pierre GRANJON

SAIGA team (Signal and Automatics for monitoring, diagnosis, and the Biomechanics) conducts research on the analysis and modelling of complex systems. The work focuses on understanding, monitoring, diagnosis and safety of industrial, natural and living biomechanical systems.

Key words: modeling, time-frequency, multi-sensor, pattern recognition, separation of sources, biomechanical models, EMG, human movement, surveillance, safety, diagnosis, prognosis

 

vignette slides SaigaTeam presentation :
Français (pdf, 119 ko)
English (pdf, 100 ko)

 

 

TOPICS of the team

Topic 1: models of signal processing for surveillance of systems

Topic 2: models for decision in surveillance and safety

Topic 3: models and signals for Biomechanics

Complex systems studied by SAIGA are the mechatronic systems, production systems, transportation or communication, and living systems. These systems are sometimes distributed. In some cases, methodologies must be autonomous and integrated, and therefore respect the constraints of embedded systems. The 3 topics of the team reflect its composition and reinforce its disciplinary coherence around signal, automatic and biomechanics. This composition is original and relies on the presence of skills in these disciplines within the team, which explains the originality of our results.

Preferred applications of SAIGA team are energy and health, fields that federate the core activity of every one on each topic.





Last publications of team

Estimating the Rotational Synchronous Component from Instantaneous Angular Speed Signals in Variable Speed Conditions

Guillaume Bruand, Florent Chatelain, Pierre Granjon, Nadine Martin, Christophe Duret, et al.. Estimating the Rotational Synchronous Component from Instantaneous Angular Speed Signals in Variable Speed Conditions. International Conference on CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS, Jun 2018, Santander, Spain. 2018. 〈hal-01761532〉

Automatic Data-Driven Spectral Analysis Based on a Multi-Estimator Approach

Nadine Martin, Corinne Mailhes. Automatic Data-Driven Spectral Analysis Based on a Multi-Estimator Approach. Signal Processing, Elsevier, 2018, 〈10.1016/j.sigpro.2017.12.024〉. 〈hal-01677964〉

Caractérisation et auralisation de la contribution du rail dans le bruit ferroviaire

Abbès Kacem, Julien Maillard, Baldrik Faure, Nadine Martin. Caractérisation et auralisation de la contribution du rail dans le bruit ferroviaire. 14ème Congrès Français d'Acoustique, Apr 2018, Le Havre, France. 2018. 〈hal-01761490〉


Voir toutes les publications de l'équipe dans HAL
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