Search Results | ASME Digital Collection

1-8 of 8

Sort by

Sort Order Select

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

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

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

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

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

Journal citation

NARROW

Capability of the Bayesian Forecasting Method to Predict Field Time Series

Capability of the Bayesian Forecasting Method to Predict Field Time Series

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

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

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

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)

Journals

Conference Proceedings

eBooks

Resources

Opportunities

Journals

Conference Proceedings

eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only