What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Electrical engineering
- Software engineering
His scientific interests lie mostly in Multi-agent system, Condition monitoring, Control engineering, Intelligent decision support system and Decision support system.
His Multi-agent system study integrates concerns from other disciplines, such as Power engineering, Systems engineering, SCADA, Embedded system and Systems architecture.
His Systems engineering research also works with subjects such as
- Electrical engineering technology that connect with fields like Electric power industry, Power system simulator for engineering and Systems design,
- Requirements engineering and related System of systems.
His Condition monitoring research includes elements of Transformer, Data mining, Partial discharge, Maintenance engineering and Electronic engineering.
His Intelligent decision support system study combines topics in areas such as Intelligent agent, Information engineering and Electric power system.
He has included themes like Anomaly detection and Knowledge-based systems in his Decision support system study.
His most cited work include:
- Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges (872 citations)
- Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems (429 citations)
- Online wind turbine fault detection through automated SCADA data analysis (289 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Condition monitoring, Reliability engineering, Multi-agent system, Intelligent decision support system and Decision support system.
His Condition monitoring research is multidisciplinary, relying on both Partial discharge, Anomaly detection, Data mining and Transformer.
His Reliability engineering study also includes fields such as
- Fault and related Power-system protection and Data analysis,
- Circuit breaker which intersects with area such as Maintenance engineering.
Stephen McArthur combines subjects such as Distributed computing, Intelligent agent, Power engineering, Embedded system and Software engineering with his study of Multi-agent system.
Stephen McArthur interconnects Electric power system, Systems engineering, Control engineering, Smart grid and Case-based reasoning in the investigation of issues within Intelligent decision support system.
As a part of the same scientific study, he usually deals with the Decision support system, concentrating on Knowledge-based systems and frequently concerns with Knowledge base.
He most often published in these fields:
- Condition monitoring (42.01%)
- Reliability engineering (27.85%)
- Multi-agent system (24.20%)
What were the highlights of his more recent work (between 2017-2021)?
- Condition monitoring (42.01%)
- Reliability engineering (27.85%)
- Data mining (9.59%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Condition monitoring, Reliability engineering, Data mining, Fault and Probabilistic logic.
Stephen McArthur performs multidisciplinary study in Condition monitoring and TRACE in his work.
The study incorporates disciplines such as Expert system and Degradation in addition to Reliability engineering.
His is doing research in Anomaly detection, Identification and Decision support system, both of which are found in Data mining.
His Identification study combines topics from a wide range of disciplines, such as Intelligent decision support system, CANDU reactor and Similarity.
His work carried out in the field of Decomposition brings together such families of science as Distributed generation, Multi-agent system, Voltage regulation and Subnetwork.
Between 2017 and 2021, his most popular works were:
- Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques (28 citations)
- Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing (22 citations)
- Adaptive Power Transformer Lifetime Predictions Through Machine Learning and Uncertainty Modeling in Nuclear Power Plants (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Electrical engineering
- Machine learning
His primary areas of study are Transformer, Probabilistic logic, Condition monitoring, Reliability engineering and Data mining.
In Transformer, Stephen McArthur works on issues like Power grid, which are connected to Probabilistic forecasting and Smart grid.
Stephen McArthur performs multidisciplinary studies into Condition monitoring and Automated X-ray inspection in his work.
The various areas that Stephen McArthur examines in his Data mining study include Fault, Supervised learning, Dissolved gas analysis and Process.
His Fault research is multidisciplinary, incorporating elements of Ground truth, Bottleneck, Electric power system and Pattern recognition.
His Prognostics research incorporates themes from Automation, Fault management, Cluster analysis, Decision support system and Data visualization.
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