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Brigitte Chebel-Morello
Researcher at Franche Comté Électronique Mécanique Thermique et Optique Sciences et Technologies
Publications - 23
Citations - 2068
Brigitte Chebel-Morello is an academic researcher from Franche Comté Électronique Mécanique Thermique et Optique Sciences et Technologies. The author has contributed to research in topics: Prognostics & Ontology (information science). The author has an hindex of 12, co-authored 23 publications receiving 1551 citations. Previous affiliations of Brigitte Chebel-Morello include ASM International & University of Franche-Comté.
Papers
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Journal ArticleDOI
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
Proceedings Article
PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Patrick Nectoux,Rafael Gouriveau,Kamal Medjaher,Emmanuel Ramasso,Brigitte Chebel-Morello,Noureddine Zerhouni,Christophe Varnier +6 more
TL;DR: In this paper, the authors present an experimental platform called PRONOSTIA, which enables testing, verifying and validating methods related to bearing health assessment, diagnostic and prognostic, which are performed under constant and/or variable operating conditions.
Journal ArticleDOI
Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network
Jaouher Ben Ali,Jaouher Ben Ali,Brigitte Chebel-Morello,Lotfi Saidi,Simon Malinowski,Farhat Fnaiech +5 more
TL;DR: In this article, a combination of simplified fuzzy adaptive Resonance theory map (SFAM) neural network and Weibull distribution (WD) is explored to predict the remaining useful life (RUL) of rolling element bearings.
Journal ArticleDOI
Direct Remaining Useful Life Estimation Based on Support Vector Regression
Racha Khelif,Brigitte Chebel-Morello,Simon Malinowski,Emna Laajili,Farhat Fnaiech,Noureddine Zerhouni +5 more
TL;DR: Experimental results show that the performance of the proposed method is competitive with other existing approaches and has a positive impact on the accuracy of the prediction while reducing the computational time compared to existing indirect RUL prediction methods.
Proceedings ArticleDOI
RUL prediction based on a new similarity-instance based approach
TL;DR: This paper proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter, and makes use of a new similarity measure between HIs.