What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Enrico Zio mostly deals with Reliability engineering, Monte Carlo method, Mathematical optimization, Component and Data mining.
His Reliability engineering research is multidisciplinary, incorporating perspectives in Power transmission, Reliability and Cascading failure.
His Reliability research incorporates themes from Machine learning, Time series, Probabilistic logic, Artificial intelligence and Degradation.
His research in Monte Carlo method intersects with topics in Distributed generation, Sensitivity, Statistical physics, Simulation and Flexibility.
Enrico Zio has included themes like Redundancy, Reliability and Renewable energy in his Mathematical optimization study.
His Data mining research is multidisciplinary, incorporating elements of Uncertainty analysis, Representation, Bayesian probability and Fuzzy logic.
His most cited work include:
- Reliability engineering: Old problems and new challenges (532 citations)
- Artificial intelligence for fault diagnosis of rotating machinery: A review (493 citations)
- Artificial intelligence for fault diagnosis of rotating machinery: A review (493 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Reliability engineering, Monte Carlo method, Reliability, Data mining and Mathematical optimization.
His Reliability engineering study combines topics from a wide range of disciplines, such as Nuclear power plant, Probabilistic logic, Nuclear power and Component.
Enrico Zio interconnects Algorithm, Statistical physics and Simulation in the investigation of issues within Monte Carlo method.
His Data mining research incorporates elements of Support vector machine, Cluster analysis, Fuzzy logic, Artificial intelligence and Machine learning.
Enrico Zio works on Artificial intelligence which deals in particular with Artificial neural network.
His research related to Multi-objective optimization and Genetic algorithm might be considered part of Mathematical optimization.
He most often published in these fields:
- Reliability engineering (36.39%)
- Monte Carlo method (25.22%)
- Reliability (22.16%)
What were the highlights of his more recent work (between 2018-2021)?
- Reliability engineering (36.39%)
- Reliability (22.16%)
- Data mining (21.97%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Reliability engineering, Reliability, Data mining, Risk analysis and Mathematical optimization.
His work carried out in the field of Reliability engineering brings together such families of science as Nuclear power and Boiler.
The Reliability study combines topics in areas such as Turbine blade, Structural engineering, Probabilistic logic, Algorithm and Monte Carlo method.
His work deals with themes such as Risk assessment and Condition monitoring, which intersect with Data mining.
His study explores the link between Risk analysis and topics such as Bayesian network that cross with problems in Scenario analysis and Component.
His studies in Mathematical optimization integrate themes in fields like Redundancy and Electric power.
Between 2018 and 2021, his most popular works were:
- A reinforcement learning framework for optimal operation and maintenance of power grids (51 citations)
- Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (50 citations)
- A resilience perspective on water transport systems: The case of Eastern Star (41 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Reliability, Reliability engineering, Risk analysis, Data mining and Prognostics are his primary areas of study.
Enrico Zio has researched Reliability in several fields, including Probabilistic logic, Moving least squares, Random variable and Phase.
While working on this project, Enrico Zio studies both Reliability engineering and Field data.
His Risk analysis study incorporates themes from Nuclear power plant, SCADA, Nuclear power, Bayesian network and Risk management.
His study on Similarity measure is often connected to Combined use as part of broader study in Data mining.
His Prognostics research integrates issues from Artificial neural network, Particle filter, Boiler and Missing data.
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