H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 48 Citations 10,116 274 World Ranking 1711 National Ranking 718

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Electrical engineering
  • Machine learning

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 most cited work include:

  • Intelligent Fault Diagnosis and Prognosis for Engineering Systems (689 citations)
  • Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. (496 citations)
  • A comparison of waveform fractal dimension algorithms (357 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (21.83%)
  • Fault (18.88%)
  • Control engineering (17.40%)

What were the highlights of his more recent work (between 2012-2021)?

  • Prognostics (9.14%)
  • Control engineering (17.40%)
  • Reliability engineering (10.62%)

In recent papers he was focusing on the following fields of study:

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.

Between 2012 and 2021, his most popular works were:

  • Handbook of Unmanned Aerial Vehicles (237 citations)
  • Online Condition Monitoring of Power MOSFET Gate Oxide Degradation Based on Miller Platform Voltage (49 citations)
  • UAV Applications: Introduction (27 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Electrical engineering
  • Machine learning

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.

Top Publications

Intelligent Fault Diagnosis and Prognosis for Engineering Systems

George Vachtsevanos;Frank Lewis;Michael Roemer;Andrew Hess.
(2006)

1243 Citations

Handbook of Unmanned Aerial Vehicles

Kimon P. Valavanis;George J. Vachtsevanos.
(2014)

711 Citations

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.
Neuron (2001)

659 Citations

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)

501 Citations

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)

442 Citations

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)

340 Citations

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)

316 Citations

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)

259 Citations

Fault prognostics using dynamic wavelet neural networks

Peng Wang;George Vachtsevanos.
Ai Edam Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2001)

203 Citations

Fault prognosis using dynamic wavelet neural networks

G. Vachtsevanos;P. Wang.
autotestcon (2001)

203 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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