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Proceedings Papers

Implementation of Artificial Intelligence for Aircraft Engine Health Monitoring and Prognostics

Aditya Aditya, Theoklis Nikolaidis, Arias Chao Manuel, Simone Togni
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A030, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127081
https://doi.org/10.1115/GT2024-127081
View Paper titled, Implementation of Artificial Intelligence for Aircraft Engine Health Monitoring and Prognostics
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Topics: Aircraft engines, Artificial intelligence, Condition monitoring, Deep learning, Engines, Convolutional neural networks, Cycles, Flight, Service life (Equipment), Aircraft
Proceedings Papers

Novel Applications of Data Analytics in Gas Turbine Operation for Distributed Power Generation

Deepak Thirumurthy, Mark Scudamore
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A026, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125463
https://doi.org/10.1115/GT2024-125463
View Paper titled, Novel Applications of Data Analytics in Gas Turbine Operation for Distributed Power Generation
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Topics: Distributed power generation, Gas turbines, Maintenance, Vibration, Condition monitoring, Decision making, Innovation, Life cycle costing, Optimization, Power stations
Proceedings Papers

Dry Gas Seal Condition Monitoring With Embedded Acoustic Emissions Sensors

Christopher Palmer, Colleen Fritz, Chris Kotar
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A019, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124365
https://doi.org/10.1115/GT2024-124365
View Paper titled, Dry Gas Seal Condition Monitoring With Embedded Acoustic Emissions Sensors
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Topics: Acoustic emissions, Condition monitoring, Sensors
Proceedings Papers

Prediction of a Crack Propagation in a Steam Turbine Rotor

Steven Chatterton, Paolo Pennacchi, Andrea Vania, Ludovico Dassi
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT26A016, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127943
https://doi.org/10.1115/GT2024-127943
View Paper titled, Prediction of a Crack Propagation in a Steam Turbine Rotor
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Topics: Crack propagation, Fatigue, Fracture (Materials), Machinery, Rotors, Steam turbines, Vibration, Condition monitoring, Damage, Inspection
Proceedings Papers

SCADA-Based Long-Term Wind Turbine Performance Degradation Analysis Based on Deep Learning Techniques

Dandan Peng, Ludovico Terzi, Wim Desmet, Konstantinos Gryllias
Proc. ASME. GT2024, Volume 13: Heat Transfer: General Interest / Additive Manufacturing Impacts on Heat Transfer; Wind Energy, V013T37A017, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128709
https://doi.org/10.1115/GT2024-128709
View Paper titled, SCADA-Based Long-Term Wind Turbine Performance Degradation Analysis Based on Deep Learning Techniques
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Topics: Condition monitoring, Convolutional neural networks, Deep learning, Wind turbines
Proceedings Papers

Predicting Premature Failures in Small Wind Turbines With Recurrence Plots

Juan C. Jauregui, Ignacio Torres-Contreras
Proc. ASME. GT2023, Volume 14: Wind Energy, V014T37A006, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102631
https://doi.org/10.1115/GT2023-102631
View Paper titled, Predicting Premature Failures in Small Wind Turbines With Recurrence Plots
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Topics: Failure, Wind turbines, Signals, Accelerometers, Algorithms, Condition monitoring, Resolution (Optics), Transients (Dynamics)
Proceedings Papers

Bayesian Information Fusion for Gas Turbines Diagnostics and Prognostics

Valentina Zaccaria, Amare D. Fentaye, Konstantinos Kyprianidis
Proc. ASME. GT2023, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A021, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-103171
https://doi.org/10.1115/GT2023-103171
View Paper titled, Bayesian Information Fusion for Gas Turbines Diagnostics and Prognostics
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Topics: Filters, Gas turbines, Particulate matter, Uncertainty, Bayesian network, Maintenance, Condition monitoring, Engines, Errors, Machinery
Proceedings Papers

Transient Engine Condition Monitoring of Micro Gas Turbines in a Highly Dynamic Setting

Tihomir Varchev, Matthias Weiss, Stephan Staudacher, Christian Koch
Proc. ASME. GT2022, Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine, V002T05A013, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82377
https://doi.org/10.1115/GT2022-82377
View Paper titled, Transient Engine Condition Monitoring of Micro Gas Turbines in a Highly Dynamic Setting
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Topics: Condition monitoring, Engines, Micro gas turbines, Transients (Dynamics), Flight, Artificial neural networks, Flow (Dynamics), Measurement uncertainty, Noise (Sound), Wavelets
Proceedings Papers

Condition Monitoring Insight Using Bayesian Inference and Rotor Dynamics Modelling for Rotating Machinery

Greg Nelson, Iain Palmer
Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A002, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-80976
https://doi.org/10.1115/GT2022-80976
View Paper titled, Condition Monitoring Insight Using Bayesian Inference and Rotor Dynamics Modelling for Rotating Machinery
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Topics: Condition monitoring, Machinery, Modeling, Rotordynamics, Vibration, Bearings, Clearances (Engineering), Compressors, Digital twin, Finite element model
Proceedings Papers

The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

Kathryn Colquhoun, Homam Nikpey Somehsaraei, Ward De Paepe
Proc. ASME. GT2022, Volume 4: Cycle Innovations; Cycle Innovations: Energy Storage, V004T06A021, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82444
https://doi.org/10.1115/GT2022-82444
View Paper titled, The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles
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Topics: Artificial neural networks, Condition monitoring, Cycles, Micro gas turbines
Proceedings Papers

A Proven Engine Optimizer for the Aeroderivative Industry: Challenges and Solutions

Nicolas Demougeot, Franklin van den Hout, Danny Grobbe, Wenping Wang
Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T09A009, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-83026
https://doi.org/10.1115/GT2022-83026
View Paper titled, A Proven Engine Optimizer for the Aeroderivative Industry: Challenges and Solutions
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Topics: Engines, Gas turbines, Emissions, Dynamic models, Combustion systems, Combustion, Control equipment, Dynamics (Mechanics), Combustion chambers, Computer software
Proceedings Papers

Unsupervised Feature Selection of Multi-Sensor SCADA Data in Horizontal Axis Wind Turbine Condition Monitoring

Eric Stefan Miele, Fabrizio Bonacina, Alessandro Corsini, Arianna Peruch, Marco Cannarozzo, Daniele Baldan, Francesco Pennisi
Proc. ASME. GT2022, Volume 11: Wind Energy, V011T38A015, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82462
https://doi.org/10.1115/GT2022-82462
View Paper titled, Unsupervised Feature Selection of Multi-Sensor SCADA Data in Horizontal Axis Wind Turbine Condition Monitoring
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Topics: Condition monitoring, Feature selection, Horizontal axis wind turbines, Sensors
Proceedings Papers

Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators

Kayacan Kestel, Cédric Peeters, Jérôme Antoni, Shawn Sheng, Jan Helsen
Proc. ASME. GT2022, Volume 11: Wind Energy, V011T38A005, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-81294
https://doi.org/10.1115/GT2022-81294
View Paper titled, Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators
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Topics: Bearings, Condition monitoring, Flaw detection, Mechanical drives, Vibration, Wind turbines
Proceedings Papers

Condition Monitoring of Wind Turbines Based on Anomaly Detection Using Deep Support Vector Data Description

Dandan Peng, Chenyu Liu, Wim Desmet, Konstantinos Gryllias
Proc. ASME. GT2022, Volume 11: Wind Energy, V011T38A018, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82624
https://doi.org/10.1115/GT2022-82624
View Paper titled, Condition Monitoring of Wind Turbines Based on Anomaly Detection Using Deep Support Vector Data Description
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Topics: Condition monitoring, Wind turbines
Proceedings Papers

Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data

Junyu Qi, Alexandre Mauricio, Konstantinos Gryllias
Proc. ASME. GT2021, Volume 1: Aircraft Engine; Fans and Blowers; Marine; Wind Energy; Scholar Lecture, V001T40A005, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-60280
https://doi.org/10.1115/GT2021-60280
View Paper titled, Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data
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Topics: Bearings, Condition monitoring, Electromagnetic scattering, Radiation scattering, Scattering (Physics), Vibration, Wind turbines
Proceedings Papers

Possibilities and Limitations of Early Fault Detection in Gas Turbines

Bernhard Persigehl, Thomas Gellermann, Stefan Thumm, Johannes Stoiber
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A034, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-16251
https://doi.org/10.1115/GT2020-16251
View Paper titled, Possibilities and Limitations of Early Fault Detection in Gas Turbines
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Topics: Flaw detection, Gas turbines, Maintenance, Failure, Damage, Condition monitoring
Proceedings Papers

Novel Cyclo-Non-Stationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions

Alexandre Mauricio, Dustin Helm, Markus Timusk, Jerome Antoni, Konstantinos Gryllias
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A018, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15245
https://doi.org/10.1115/GT2020-15245
View Paper titled, Novel Cyclo-Non-Stationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions
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Topics: Machinery, Stress, Signals, Condition monitoring, Failure, Maintenance, Bearings, Damage, Density, Excitation
Proceedings Papers

Real Time Diagnostic Method of Gas Turbines Operating Under Transient Conditions in Hybrid Power Plants

Elias Tsoutsanis, Moussa Hamadache, Roger Dixon
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A013, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14748
https://doi.org/10.1115/GT2020-14748
View Paper titled, Real Time Diagnostic Method of Gas Turbines Operating Under Transient Conditions in Hybrid Power Plants
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Topics: Gas turbines, Hybrid power systems, Transients (Dynamics), Engines, Stress, Wind farms, Condition monitoring, Electric power generation, Maintenance, Renewable energy
Proceedings Papers

Dynamic Gas Turbine Condition Monitoring Scheme With Multi-Part Neural Network

Zhouzheng Li, Kun Feng, Binbin Yan
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A007, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14454
https://doi.org/10.1115/GT2020-14454
View Paper titled, Dynamic Gas Turbine Condition Monitoring Scheme With Multi-Part Neural Network
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Topics: Artificial neural networks, Condition monitoring, Failure, Gas turbines
Proceedings Papers

Cyclostationary Approach for Instabilities Detection and Condition Monitoring of Centrifugal Compressor

Mateusz Stajuda, David Garcia Cava, Grzegorz Liśkiewicz
Proc. ASME. GT2020, Volume 2E: Turbomachinery, V02ET41A032, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15699
https://doi.org/10.1115/GT2020-15699
View Paper titled, Cyclostationary Approach for Instabilities Detection and Condition Monitoring of Centrifugal Compressor
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Topics: Compressors, Condition monitoring