N
Noureddine Zerhouni
Researcher at Centre national de la recherche scientifique
Publications - 301
Citations - 9616
Noureddine Zerhouni is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Prognostics & Computer science. The author has an hindex of 41, co-authored 276 publications receiving 7061 citations. Previous affiliations of Noureddine Zerhouni include Franche Comté Électronique Mécanique Thermique et Optique Sciences et Technologies & ASM International.
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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
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
TL;DR: The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.
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A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models
TL;DR: The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs.
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Health assessment and life prediction of cutting tools based on support vector regression
TL;DR: Results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their RUL, and can be used by the operators to take appropriate maintenance actions.
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.