George Vachtsevanos mainly focuses on Fault, Artificial intelligence, Control theory, Particle filter and Electroencephalography. George Vachtsevanos specializes in Fault, namely Fault detection and isolation. The various areas that George Vachtsevanos examines in his Artificial intelligence study include Machine learning and Pattern recognition.
His studies deal with areas such as Algorithm, Fault indicator, Probability density function and Condition monitoring as well as Particle filter. In Electroencephalography, George Vachtsevanos works on issues like Epilepsy, which are connected to Audiology and Energy variation. His research in Artificial neural network intersects with topics in Reliability engineering and Condition-based maintenance.
His primary areas of study are Artificial intelligence, Fault, Control engineering, Control theory and Fuzzy logic. The Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition. His Pattern recognition study incorporates themes from Feature and Electroencephalography.
His Fault research integrates issues from Particle filter, Reliability engineering, Component and Condition monitoring. His Particle filter research is multidisciplinary, relying on both Algorithm, Fault indicator, Probability density function and Anomaly detection. His Control engineering research is multidisciplinary, incorporating elements of Control system, Intelligent control, Robustness and Optimal control.
George Vachtsevanos mainly investigates Prognostics, Control engineering, Reliability engineering, Corrosion and Systems engineering. His Control engineering research is multidisciplinary, incorporating elements of Mechanism, Anomaly detection, Fault detection and isolation, Feedback control and Simulation. While the research belongs to areas of Anomaly detection, he spends his time largely on the problem of Stuck-at fault, intersecting his research to questions surrounding Particle filter and Algorithm.
His work in the fields of Reliability engineering, such as Fault tolerance, overlaps with other areas such as Warranty. His research investigates the connection between Fault tolerance and topics such as Aerospace that intersect with problems in Fault. His study in the fields of Fault management under the domain of Fault overlaps with other disciplines such as Ask price.
George Vachtsevanos focuses on Prognostics, Reliability engineering, Automotive engineering, Electric motor and Anomaly detection. His Prognostics research is multidisciplinary, relying on both Control system, Flight control surfaces, Actuator and Fault detection and isolation. His Fault detection and isolation study integrates concerns from other disciplines, such as Transformer, Equivalent circuit, Failure mode and effects analysis, Electric power and Particle filter.
George Vachtsevanos integrates several fields in his works, including Reliability engineering and Warranty. His Automotive engineering research incorporates elements of Airframe, Autopilot and Flight simulator. His Electric motor research is multidisciplinary, incorporating perspectives in Electrical engineering technology, Simulation, Control engineering and Mature technology.
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.
Intelligent Fault Diagnosis and Prognosis for Engineering Systems
George Vachtsevanos;Frank Lewis;Michael Roemer;Andrew Hess.
Handbook of Unmanned Aerial Vehicles
Kimon P. Valavanis;George J. Vachtsevanos.
Epileptic seizures may begin hours in advance of clinical onset: a report of five patients.
Brian Litt;Rosana Esteller;Rosana Esteller;Javier Echauz;Javier Echauz;Maryann D'Alessandro.
A particle-filtering approach for on-line fault diagnosis and failure prognosis
Marcos E. Orchard;George J. Vachtsevanos.
Transactions of the Institute of Measurement and Control (2009)
A comparison of waveform fractal dimension algorithms
R. Esteller;G. Vachtsevanos;J. Echauz;B. Litt.
IEEE Transactions on Circuits and Systems I-regular Papers (2001)
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
Andrew B. Gardner;Abba M. Krieger;George Vachtsevanos;Brian Litt.
Journal of Machine Learning Research (2006)
Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients
M. D'Alessandro;R. Esteller;G. Vachtsevanos;A. Hinson.
IEEE Transactions on Biomedical Engineering (2003)
Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering
Chaochao Chen;Bin Zhang;G. Vachtsevanos;M. Orchard.
IEEE Transactions on Industrial Electronics (2011)
Fault prognosis using dynamic wavelet neural networks
G. Vachtsevanos;P. Wang.
Fault prognostics using dynamic wavelet neural networks
Peng Wang;George Vachtsevanos.
Ai Edam Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2001)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: