His main research concerns Prognostics, Reliability engineering, Condition monitoring, Data mining and Artificial intelligence. Noureddine Zerhouni merges Prognostics with Proton exchange membrane fuel cell in his research. His Reliability engineering research incorporates elements of Quality and Process.
His Condition monitoring research is multidisciplinary, relying on both Cutting tool, Artificial neural network, Fault detection and isolation, Dynamic Bayesian network and Predictive maintenance. Noureddine Zerhouni has included themes like Data modeling, Sigmoid function, Algorithm, Feature selection and Hidden Markov model in his Data mining study. In Artificial intelligence, he works on issues like Machine learning, which are connected to Classifier.
His primary scientific interests are in Prognostics, Artificial intelligence, Reliability engineering, Data mining and Machine learning. He integrates many fields, such as Prognostics and Proton exchange membrane fuel cell, in his works. His Artificial intelligence study typically links adjacent topics like Pattern recognition.
In his study, Bearing is inextricably linked to Fault, which falls within the broad field of Reliability engineering. His Data mining research is multidisciplinary, incorporating perspectives in Cluster analysis, Feature extraction, Support vector machine and Component. His studies in Process integrate themes in fields like Systems engineering, Risk analysis and Condition-based maintenance.
Prognostics, Artificial intelligence, Data mining, Process and Reliability engineering are his primary areas of study. He undertakes multidisciplinary investigations into Prognostics and Proton exchange membrane fuel cell in his work. He has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
The concepts of his Data mining study are interwoven with issues in Estimation and Feature. His Process research includes elements of Retargeting, Data management and Systems engineering. His work carried out in the field of Reliability engineering brings together such families of science as Service-level agreement, Quality of service, Server and Service.
Noureddine Zerhouni focuses on Artificial intelligence, Prognostics, Condition monitoring, Deep learning and Mammography. In his research on the topic of Artificial intelligence, Computer-aided diagnosis is strongly related with Machine learning. His study on Prognostics is covered under Data mining.
His Data mining research integrates issues from Reliability and Sensor fusion. His Condition monitoring research is multidisciplinary, relying on both Feature extraction, Support vector machine and Fault detection and isolation. His studies deal with areas such as Artificial neural network, Data science and Pruning as well as Deep learning.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Patrick Nectoux;Rafael Gouriveau;Kamal Medjaher;Emmanuel Ramasso.
ieee international conference on prognostics and health management (2012)
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
Abdenour Soualhi;Kamal Medjaher;Noureddine Zerhouni.
IEEE Transactions on Instrumentation and Measurement (2015)
A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models
D. A. Tobon-Mejia;K. Medjaher;N. Zerhouni;G. Tripot.
IEEE Transactions on Reliability (2012)
Remaining Useful Life Estimation of Critical Components With Application to Bearings
K. Medjaher;D. A. Tobon-Mejia;N. Zerhouni.
IEEE Transactions on Reliability (2012)
Health assessment and life prediction of cutting tools based on support vector regression
T. Benkedjouh;K. Medjaher;N. Zerhouni;S. Rechak.
Journal of Intelligent Manufacturing (2015)
Remaining useful life estimation based on nonlinear feature reduction and support vector regression
Tarak Benkedjouh;Kamal Medjaher;Noureddine Zerhouni;Saïd Rechak.
Engineering Applications of Artificial Intelligence (2013)
Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction
A. Mosallam;K. Medjaher;N. Zerhouni.
Journal of Intelligent Manufacturing (2016)
CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks
Diego Tobon-Mejia;Diego Tobon-Mejia;Kamal Medjaher;Noureddine Zerhouni.
Mechanical Systems and Signal Processing (2012)
Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics.
Kamran Javed;Rafael Gouriveau;Noureddine Zerhouni;Patrick Nectoux.
IEEE Transactions on Industrial Electronics (2015)
Prognostics of PEM fuel cell in a particle filtering framework
Marine Jouin;Rafael Gouriveau;Daniel Hissel;Marie-Cécile Péra.
International Journal of Hydrogen Energy (2014)
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