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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

Paper No: GT2022-80666

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

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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

Paper No: GT2022-82915

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

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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

Paper No: GT2022-81082

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

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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

Paper No: GT2022-80372

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

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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

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

Paper No: GTP-22-1359

DOI: https://doi.org/10.1115/1.4055456

Published Online: September 23, 2022

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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

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

Paper No: GTP-21-1324

DOI: https://doi.org/10.1115/1.4053194

Published Online: January 21, 2022

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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

Paper No: GT2021-58916

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

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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

Paper No: GT2021-58914

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

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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

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

Paper No: GTP-20-1542

DOI: https://doi.org/10.1115/1.4049652

Published Online: March 16, 2021

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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

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

Paper No: GTP-20-1534

DOI: https://doi.org/10.1115/1.4049503

Published Online: February 10, 2021

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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

Paper No: GT2020-16331

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

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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

Paper No: GT2020-16094

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

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

Proceedings Papers

Autoregressive Bayesian Hierarchical Model to Predict Gas Turbine Degradation

Enzo Losi, Mauro Venturini, Lucrezia Manservigi

Proc. ASME. GT2020, Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine, V009T21A018, September 21–25, 2020

Paper No: GT2020-16330

DOI: https://doi.org/10.1115/GT2020-16330

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Topics: Gas turbines, Maintenance, Breakdown (Electricity), Errors

Proceedings Papers

Structured Methodology for Clustering Gas Turbine Transients by Means of Multi-Variate Time Series

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

Proc. ASME. GT2020, Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine, V009T21A005, September 21–25, 2020

Paper No: GT2020-14751

DOI: https://doi.org/10.1115/GT2020-14751

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Topics: Gas turbines, Time series, Transients (Dynamics), Algorithms, Case studies, Damage, Data mining, Databases, Engines, Reliability

Journal Articles

Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

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

J. Eng. Gas Turbines Power. February 2020, 142(2): 021009.

Paper No: GTP-19-1494

DOI: https://doi.org/10.1115/1.4045711

Published Online: January 10, 2020

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Topics: Flaw detection, Sensors, Signals

Proceedings Papers

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

Enzo Losi, Mauro Venturini, Lucrezia Manservigi

Proc. ASME. GT2019, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A003, June 17–21, 2019

Paper No: GT2019-90054

DOI: https://doi.org/10.1115/GT2019-90054

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Topics: Gas turbines, Errors, Reliability, Life cycle costing

Proceedings Papers

A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment

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

Proc. ASME. GT2019, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A004, June 17–21, 2019

Paper No: GT2019-90055

DOI: https://doi.org/10.1115/GT2019-90055

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Topics: Flaw detection, Sensors, Gas turbines, Machinery, Signals

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Journal citation

NARROW

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

Autoregressive Bayesian Hierarchical Model to Predict Gas Turbine Degradation

Structured Methodology for Clustering Gas Turbine Transients by Means of Multi-Variate Time Series

Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment

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