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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

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

Paper No: GT2021-58916

DOI: 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

Paper No: GT2021-58914

DOI: 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

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

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

Topics: Transients (Dynamics), Time series

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

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 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

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

Topics: Flaw detection, Sensors, Signals

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

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

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

Proceedings Papers

Validation of an Advanced Diagnostic Methodology for the Identification and Classification of Gas Turbine Sensor Faults by Means of Field Data

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, V009T27A005, June 17–21, 2019

Paper No: GT2019-90056

DOI: https://doi.org/10.1115/GT2019-90056

View Paper titled, Validation of an Advanced Diagnostic Methodology for the Identification and Classification of Gas Turbine Sensor Faults by Means of Field Data

Topics: Gas turbines, Sensors, Sensitivity analysis, Flaw detection, Signals

Proceedings Papers

Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

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

Proc. ASME. GT2019, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A006, June 17–21, 2019

Paper No: GT2019-90057

DOI: https://doi.org/10.1115/GT2019-90057

View Paper titled, Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

Topics: Gas turbines, Time series, Sensors

Journal Articles

Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

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

J. Eng. Gas Turbines Power. November 2019, 141(11): 111019.

Paper No: GTP-19-1379

DOI: https://doi.org/10.1115/1.4044781

Published Online: October 18, 2019

View Article titled, Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

Topics: Sensors, Time series, Gas turbines

Journal Articles

Capability of the Bayesian Forecasting Method to Predict Field Time Series

Nicolò Gatta, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini

J. Eng. Gas Turbines Power. December 2018, 140(12): 121013.

Paper No: GTP-18-1265

DOI: https://doi.org/10.1115/1.4040736

Published Online: October 29, 2018

View Article titled, Capability of the Bayesian Forecasting Method to Predict Field Time Series

Topics: Errors, Gas turbines, Sensors, Steady state, Temperature, Time series, Algorithms

Proceedings Papers

Capability of the Bayesian Forecasting Method to Predict Field Time Series

Nicolò Gatta, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini

Proc. ASME. GT2018, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A003, June 11–15, 2018

Paper No: GT2018-75006

DOI: https://doi.org/10.1115/GT2018-75006

View Paper titled, Capability of the Bayesian Forecasting Method to Predict Field Time Series

Topics: Time series, Errors, Algorithms, Gas turbines, Sensors, Simulation

Proceedings Papers

Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

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

Proc. ASME. GT2018, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A004, June 11–15, 2018

Paper No: GT2018-75007

DOI: https://doi.org/10.1115/GT2018-75007

View Paper titled, Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

Topics: Gas turbines, Sensors, Algorithms, Filtration, Pressure, Temperature, Vibration

Journal Articles

Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

Giuseppe Fabio Ceschini, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, Alin Murarasu

J. Eng. Gas Turbines Power. May 2018, 140(5): 052401.

Paper No: GTP-17-1361

DOI: https://doi.org/10.1115/1.4038155

Published Online: November 21, 2017

View Article titled, Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

Topics: Gas turbines, Robustness, Time series, Contamination, Temperature, Sensors, Reliability, Noise (Sound)

Journal Articles

A Comprehensive Approach for Detection, Classification, and Integrated Diagnostics of Gas Turbine Sensors

Giuseppe Fabio Ceschini, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, Alin Murarasu

J. Eng. Gas Turbines Power. March 2018, 140(3): 032402.

Paper No: GTP-17-1360

DOI: https://doi.org/10.1115/1.4037964

Published Online: October 25, 2017

View Article titled, A Comprehensive Approach for Detection, Classification, and Integrated Diagnostics of Gas Turbine Sensors

Topics: Sensors, Temperature, Filters, Gas turbines, Algorithms

Journal Articles

Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Giuseppe Fabio Ceschini, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, Alin Murarasu

J. Eng. Gas Turbines Power. March 2018, 140(3): 032401.

Paper No: GTP-17-1359

DOI: https://doi.org/10.1115/1.4037963

Published Online: October 25, 2017

View Article titled, Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Topics: Gas turbines, Sensors, Time series, Transients (Dynamics), Steady state

Proceedings Papers

Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Giuseppe Fabio Ceschini, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, Alin Murarasu

Proc. ASME. GT2017, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A009, June 26–30, 2017

Paper No: GT2017-63409

DOI: https://doi.org/10.1115/GT2017-63409

View Paper titled, Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Topics: Gas turbines, Optimization, Time series, Sensors, Transients (Dynamics)

Proceedings Papers

Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

Giuseppe Fabio Ceschini, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, Alin Murarasu

Proc. ASME. GT2017, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A010, June 26–30, 2017

Paper No: GT2017-63410

DOI: https://doi.org/10.1115/GT2017-63410

View Paper titled, Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

Topics: Gas turbines, Time series, Weight (Mass), Robustness, Reliability, Sensors, Transients (Dynamics)

Proceedings Papers

A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS)

Giuseppe Fabio Ceschini, Nicolò Gatta, Mauro Venturini, Thomas Hubauer, Alin Murarasu

Proc. ASME. GT2017, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A011, June 26–30, 2017

Paper No: GT2017-63411

DOI: https://doi.org/10.1115/GT2017-63411

View Paper titled, A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS)

Topics: Gas turbines, Sensors, Algorithms, Time series, Statistical analysis, Filtration, Physics, Temperature

Proceedings Papers

Gas Turbines Axial Compressor NDE by Eddy Current Probes to Detect Corrosion Pitting

Giuseppe Fabio Ceschini, Federico Iozzelli

Proc. ASME. GT2009, Volume 5: Microturbines and Small Turbomachinery; Oil and Gas Applications, 465-470, June 8–12, 2009

Paper No: GT2009-60028

DOI: https://doi.org/10.1115/GT2009-60028

View Paper titled, Gas Turbines Axial Compressor NDE by Eddy Current Probes to Detect Corrosion Pitting

Topics: Compressors, Corrosion, Eddies (Fluid dynamics), Eddy currents (Electricity), Gas turbines, Nondestructive evaluation, Probes, Blades, Failure mechanisms, Filtration

Journal citation

NARROW

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

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

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

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

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

Validation of an Advanced Diagnostic Methodology for the Identification and Classification of Gas Turbine Sensor Faults by Means of Field Data

Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

Anomaly Detection in Gas Turbine Time Series by Means of Bayesian Hierarchical Models

Capability of the Bayesian Forecasting Method to Predict Field Time Series

Capability of the Bayesian Forecasting Method to Predict Field Time Series

Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

A Comprehensive Approach for Detection, Classification, and Integrated Diagnostics of Gas Turbine Sensors

Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series

Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation

A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS)

Gas Turbines Axial Compressor NDE by Eddy Current Probes to Detect Corrosion Pitting

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