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Giovanni Bechini
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Proceedings Papers

Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2024, Volume 9: Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications; Steam Turbine, V009T19A007, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122163
https://doi.org/10.1115/GT2024-122163
View Paper titled, Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults
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Topics: Artificial neural networks, Gas turbines
Journal Articles

Methodology to Monitor Early Warnings Before Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. May 2024, 146(5): 051005.
Paper No: GTP-23-1524
https://doi.org/10.1115/1.4063720
Published Online: December 8, 2023
View Article titled, Methodology to Monitor Early Warnings Before Gas Turbine Trip
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Topics: Gas turbines, Reliability, Probability, Testing, Artificial neural networks
Proceedings Papers

Methodology to Monitor Early Warnings Before Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2023, Volume 9: Microturbines, Turbochargers, and Small Turbomachines; Oil and Gas Applications, V009T19A004, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101701
https://doi.org/10.1115/GT2023-101701
View Paper titled, Methodology to Monitor Early Warnings Before Gas Turbine Trip
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Topics: Gas turbines, Reliability, Maintenance
Proceedings Papers

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

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2023, Volume 9: Microturbines, Turbochargers, and Small Turbomachines; Oil and Gas Applications, V009T19A005, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101703
https://doi.org/10.1115/GT2023-101703
View Paper titled, Prediction of Gas Turbine Trip by Combining Gas Path Measurements and Vibration Signals
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Topics: Gas turbines, Signals, Vibration, Reliability, Artificial neural networks, Sensors, Testing
Proceedings Papers

Application of Transfer Learning for the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2023, Volume 9: Microturbines, Turbochargers, and Small Turbomachines; Oil and Gas Applications, V009T19A006, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101704
https://doi.org/10.1115/GT2023-101704
View Paper titled, Application of Transfer Learning for the Prediction of Gas Turbine Trip
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Topics: Gas turbines, Artificial neural networks, Maintenance
Journal Articles

Statistical Rule Extraction for Gas Turbine Trip Prediction

Giovanni Bechini, Enzo Losi, Lucrezia Manservigi, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan, Mauro Venturini
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. May 2023, 145(5): 051017.
Paper No: GTP-22-1408
https://doi.org/10.1115/1.4056287
Published Online: January 10, 2023
View Article titled, Statistical Rule Extraction for Gas Turbine Trip Prediction
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Topics: Gas turbines, Statistical analysis, Turbines, Machine learning, Time series
Journal Articles

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

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2023, 145(3): 031023.
Paper No: GTP-22-1318
https://doi.org/10.1115/1.4055904
Published Online: December 8, 2022
View Article titled, Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data
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Topics: Artificial neural networks, Gas turbines, Probability, Reliability
Journal Articles

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. February 2023, 145(2): 021009.
Paper No: GTP-22-1319
https://doi.org/10.1115/1.4055905
Published Online: November 28, 2022
View Article titled, Ensemble Learning Approach to the Prediction of Gas Turbine Trip
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Topics: Gas turbines, Machine learning, Reliability, Support vector machines, Artificial neural networks, Testing, Algorithms, Probability
Proceedings Papers

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

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
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
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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
Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T19A012, June 13–17, 2022
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
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Topics: Gas turbines, Artificial intelligence, Computers, Industrial gases, Machine learning, Statistical analysis, Turbines
Proceedings Papers

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T19A002, June 13–17, 2022
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
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Topics: Gas turbines, Machine learning, Reliability, Artificial neural networks, Robustness, Support vector machines
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
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
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
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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
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
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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
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
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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
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
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
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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
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
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Topics: Machine learning, Sensors, Decision making, Energy / power systems, Engines, Errors, Gas turbines, Maintenance, Reliability, Robustness
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
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14751
https://doi.org/10.1115/GT2020-14751
View Paper titled, Structured Methodology for Clustering Gas Turbine Transients by Means of Multi-Variate Time Series
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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
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. February 2020, 142(2): 021009.
Paper No: GTP-19-1494
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
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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
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-90055
https://doi.org/10.1115/GT2019-90055
View Paper titled, A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment
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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
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-90056
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
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Topics: Gas turbines, Sensors, Sensitivity analysis, Flaw detection, Signals