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Proceedings Papers

Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks

Proc. ASME. GT2015, Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy; Honors and Awards, V006T05A030, June 15–19, 2015

Paper No: GT2015-44101

DOI: https://doi.org/10.1115/GT2015-44101

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Topics: Artificial neural networks, Gas turbines, Engines, Errors, Performance, Temperature, Uncertainty

Journal Articles

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

Z. N. Sadough Vanini, N. Meskin, K. Khorasani

J. Eng. Gas Turbines Power. September 2014, 136(9): 091603.

Paper No: GTP-13-1429

DOI: https://doi.org/10.1115/1.4027215

Published Online: May 5, 2014

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Topics: Artificial neural networks, Sensors, Fault diagnosis, Flaw detection, Jet engines

Proceedings Papers

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

E. Naderi, N. Meskin, K. Khorasani

Proc. ASME. GT2011, Volume 1: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Wind Turbine Technology, 63-75, June 6–10, 2011

Paper No: GT2011-45143

DOI: https://doi.org/10.1115/GT2011-45143

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Topics: Fault diagnosis, Jet engines, Engines, Kalman filters, Probability, Actuators, Algorithms, Compressors, Flaw detection, Simulation results

Journal Articles

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

E. Naderi, N. Meskin, K. Khorasani

J. Eng. Gas Turbines Power. January 2012, 134(1): 011602.

DOI: https://doi.org/10.1115/1.4004152

Published Online: November 4, 2011

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Topics: Algorithms, Compressors, Fault diagnosis, Filters, Flaw detection, Jet engines, Kalman filters, Probability, Turbines, Flow (Dynamics)

Proceedings Papers

Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

N. Meskin, E. Naderi, K. Khorasani

Proc. ASME. GT2010, Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine, 319-329, June 14–18, 2010

Paper No: GT2010-23442

DOI: https://doi.org/10.1115/GT2010-23442

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Topics: Fault diagnosis, Jet engines, Engines, Probability, Actuators, Algorithms, Compressors, Failure, Flaw detection, Noise (Sound)

Journal citation

NARROW

Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

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Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach

Journals

Conference Proceedings

eBooks

Resources

Opportunities

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