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Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T19A004, June 13–17, 2022

Publisher: American Society of Mechanical Engineers

Paper No: GT2022-80666

https://doi.org/10.1115/GT2022-80666

View Paper titled, Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Topics: Gas turbines, Artificial neural networks, Failure, Maintenance, Sensors, Service life (Equipment)

Proceedings Papers

Statistical Rule Extraction for Gas Turbine Trip Prediction

Giovanni Bechini, Enzo Losi, Lucrezia Manservigi, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan, Mauro Venturini

Publisher: American Society of Mechanical Engineers

Paper No: GT2022-82915

https://doi.org/10.1115/GT2022-82915

View Paper titled, Statistical Rule Extraction for Gas Turbine Trip Prediction

Topics: Gas turbines, Artificial intelligence, Computers, Industrial gases, Machine learning, Statistical analysis, Turbines

Proceedings Papers

Analysis of a Multi-Generation Renewable Energy System with Hydrogen-Fueled Gas Turbine

Hilal Bahlawan, Enzo Losi, Lucrezia Manservigi, Mirko Morini, Pier Ruggero Spina, Mauro Venturini

Publisher: American Society of Mechanical Engineers

Paper No: GT2022-81082

https://doi.org/10.1115/GT2022-81082

View Paper titled, Analysis of a Multi-Generation Renewable Energy System with Hydrogen-Fueled Gas Turbine

Topics: Gas turbines, Hydrogen fuels, Renewable energy, Solar energy, Hydrogen, Hydrogen storage, Renewable energy sources, Absorption, Climate change, Cooling

Proceedings Papers

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini

Publisher: American Society of Mechanical Engineers

Paper No: GT2022-80372

https://doi.org/10.1115/GT2022-80372

View Paper titled, Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Topics: Gas turbines, Machine learning, Reliability, Artificial neural networks, Robustness, Support vector machines

Journal Articles

Analysis of a Multi-Generation Renewable Energy System With Hydrogen-Fueled Gas Turbine

Hilal Bahlawan, Enzo Losi, Lucrezia Manservigi, Mirko Morini, Pier Ruggero Spina, Mauro Venturini

Publisher: ASME

J. Eng. Gas Turbines Power. November 2022, 144(11): 111020.

Paper No: GTP-22-1359

https://doi.org/10.1115/1.4055456

Published Online: September 23, 2022

View Article titled, Analysis of a Multi-Generation Renewable Energy System With Hydrogen-Fueled Gas Turbine

Topics: Cooling, Gas turbines, Hydrogen, Hydrogen fuels, Hydrogen storage, Optimization, Renewable energy, Solar energy, Thermal energy, Proton exchange membranes

Journal Articles

Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini, Giuseppe Cota, Fabrizio Riguzzi

Publisher: ASME

J. Eng. Gas Turbines Power. March 2022, 144(3): 031025.

Paper No: GTP-21-1324

https://doi.org/10.1115/1.4053194

Published Online: January 21, 2022

View Article titled, Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models

Topics: Gas turbines, Reliability, Machine learning, Time series, Temperature, Compressors, Image segmentation, Natural, scientific and engineering processes and practices, Sensors, Thermocouples

Proceedings Papers

Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini, Giuseppe Cota, Fabrizio Riguzzi

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T20A005, June 7–11, 2021

Publisher: American Society of Mechanical Engineers

Paper No: GT2021-58916

https://doi.org/10.1115/GT2021-58916

View Paper titled, Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

Topics: Gas turbines, Machine learning, Case studies, Data mining, Image segmentation, Natural, scientific and engineering processes and practices, Profitability, Sensors, Service life (Equipment), Time series

Proceedings Papers

Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini, Giuseppe Cota, Fabrizio Riguzzi

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T20A004, June 7–11, 2021

Publisher: American Society of Mechanical Engineers

Paper No: GT2021-58914

https://doi.org/10.1115/GT2021-58914

View Paper titled, Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip

Topics: Gas turbines, Machine learning, Algorithms, Damage, Fourier transforms, Peak load, Performance, Reliability, Service life (Equipment), Stress

Journal Articles

Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems, and Energy Storage Technologies

Hilal Bahlawan, Agostino Gambarotta, Enzo Losi, Lucrezia Manservigi, Mirko Morini, Pier Ruggero Spina, Mauro Venturini

Publisher: ASME

J. Eng. Gas Turbines Power. June 2021, 143(6): 061013.

Paper No: GTP-20-1542

https://doi.org/10.1115/1.4049652

Published Online: March 16, 2021

View Article titled, Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems, and Energy Storage Technologies

Topics: Combined heat and power, Cooling, Energy storage, Gas turbines, Optimization, Photovoltaic power systems, Renewable energy, Storage, Stress, Thermal energy

Journal Articles

Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini, Giuseppe Cota, Fabrizio Riguzzi

Publisher: ASME

J. Eng. Gas Turbines Power. March 2021, 143(3): 031014.

Paper No: GTP-20-1534

https://doi.org/10.1115/1.4049503

Published Online: February 10, 2021

View Article titled, Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series

Topics: Transients (Dynamics), Time series

Proceedings Papers

Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems and Energy Storage Technologies

Hilal Bahlawan, Agostino Gambarotta, Enzo Losi, Lucrezia Manservigi, MIrko Morini, Pier Ruggero Spina, Mauro Venturini

Proc. ASME. GT2020, Volume 8: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Marine; Microturbines, Turbochargers, and Small Turbomachines, V008T17A009, September 21–25, 2020

Publisher: American Society of Mechanical Engineers

Paper No: GT2020-16331

https://doi.org/10.1115/GT2020-16331

View Paper titled, Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems and Energy Storage Technologies

Topics: Energy storage, Industrial gases, Renewable energy, Turbines, Fossil fuels, Energy consumption, Thermal energy storage, Boilers, Combined heat and power, Compression

Proceedings Papers

Detection of Unit of Measure Inconsistency by Means of a Machine Learning Model

Lucrezia Manservigi, Daniel Murray, Javier Artal de la Iglesia, Giuseppe Fabio Ceschini, Giovanni Bechini, Enzo Losi, Mauro Venturini

Proc. ASME. GT2020, Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine, V009T21A017, September 21–25, 2020

Publisher: American Society of Mechanical Engineers

Paper No: GT2020-16094

https://doi.org/10.1115/GT2020-16094

View Paper titled, Detection of Unit of Measure Inconsistency by Means of a Machine Learning Model

Topics: Machine learning, Sensors, Decision making, Energy / power systems, Engines, Errors, Gas turbines, Maintenance, Reliability, Robustness

Journal citation

NARROW

Methodology to Monitor Early Warnings Before Gas Turbine Trip

Concurrent Design and Operation Optimization of a Multi-Generation Energy System by Using a SMO-MILP Hybrid Algorithm

Methodology to Monitor Early Warnings Before Gas Turbine Trip

Prediction of Gas Turbine Trip by Combining Gas Path Measurements and Vibration Signals

Application of Transfer Learning for the Prediction of Gas Turbine Trip

Statistical Rule Extraction for Gas Turbine Trip Prediction

Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Statistical Rule Extraction for Gas Turbine Trip Prediction

Analysis of a Multi-Generation Renewable Energy System with Hydrogen-Fueled Gas Turbine

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Analysis of a Multi-Generation Renewable Energy System With Hydrogen-Fueled Gas Turbine

Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models

Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip

Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems, and Energy Storage Technologies

Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series

Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems and Energy Storage Technologies

Detection of Unit of Measure Inconsistency by Means of a Machine Learning Model

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