What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Electrical engineering
Rafael Gouriveau mainly investigates Prognostics, Proton exchange membrane fuel cell, Stack, Reliability engineering and Artificial intelligence.
Rafael Gouriveau frequently studies issues relating to State and Prognostics.
Stack combines with fields such as Time series, Voltage reduction, Neuro-fuzzy, Signal and Adaptive neuro fuzzy inference system in his research.
His Reliability engineering research is multidisciplinary, incorporating elements of Voltage drop and Power loss.
His work on Entropy and Fuzzy logic as part of general Artificial intelligence research is frequently linked to Numerical control and Generalization, thereby connecting diverse disciplines of science.
His study on Feature is often connected to Transformation as part of broader study in Machine learning.
His most cited work include:
- PRONOSTIA : An experimental platform for bearings accelerated degradation tests. (243 citations)
- Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics. (157 citations)
- Prognostics of PEM fuel cell in a particle filtering framework (138 citations)
What are the main themes of his work throughout his whole career to date?
Prognostics, Reliability engineering, Proton exchange membrane fuel cell, Artificial intelligence and Machine learning are his primary areas of study.
His study looks at the relationship between Prognostics and topics such as Condition monitoring, which overlap with Predictive maintenance.
His research investigates the connection between Reliability engineering and topics such as Robustness that intersect with problems in Ensemble forecasting.
His work on Artificial neural network and Extreme learning machine as part of general Artificial intelligence research is often related to Generalization and Initialization, thus linking different fields of science.
His work investigates the relationship between Artificial neural network and topics such as Algorithm that intersect with problems in Wavelet.
He interconnects Neuro-fuzzy, Adaptive neuro fuzzy inference system and Fuzzy logic in the investigation of issues within Machine learning.
He most often published in these fields:
- Prognostics (58.97%)
- Reliability engineering (33.33%)
- Proton exchange membrane fuel cell (30.77%)
What were the highlights of his more recent work (between 2014-2019)?
- Prognostics (58.97%)
- Proton exchange membrane fuel cell (30.77%)
- Reliability engineering (33.33%)
In recent papers he was focusing on the following fields of study:
His main research concerns Prognostics, Proton exchange membrane fuel cell, Reliability engineering, Stack and Data modeling.
His research in Prognostics intersects with topics in Extreme learning machine and Data-driven, Artificial intelligence.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Operations research.
The study incorporates disciplines such as Entropy, Data mining and Condition monitoring in addition to Machine learning.
His research integrates issues of Duration, Robustness and Forensic engineering in his study of Reliability engineering.
His research investigates the connection with Renewable energy and areas like Biochemical engineering which intersect with concerns in Battery.
Between 2014 and 2019, his most popular works were:
- Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics. (157 citations)
- Particle filter-based prognostics: Review, discussion and perspectives (125 citations)
- Degradations analysis and aging modeling for health assessment and prognostics of PEMFC (89 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Electrical engineering
Rafael Gouriveau mostly deals with Prognostics, Proton exchange membrane fuel cell, Reliability engineering, Stack and Artificial intelligence.
His work deals with themes such as Extreme learning machine, Data-driven and Systems engineering, which intersect with Prognostics.
The various areas that Rafael Gouriveau examines in his Extreme learning machine study include Data mining, Cluster analysis, Fuzzy logic, Fuzzy clustering and Entropy.
His research brings together the fields of Power loss and Reliability engineering.
The study incorporates disciplines such as Data modeling, Machine learning and Condition monitoring in addition to Artificial intelligence.
His research in Machine learning intersects with topics in Discrete wavelet transform and Feature extraction.
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