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Proceedings Papers

Fault Detection on Short-Haul or Highly Dynamic Flights Using Transient Flight Segments

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Tihomir Varchev, Jürgen Mathes, Christian Koch, Stephan Staudacher
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A014, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124026
https://doi.org/10.1115/GT2024-124026
View Paper titled, Fault Detection on Short-Haul or Highly Dynamic Flights Using Transient Flight Segments
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Topics: Flaw detection, Flight, Machine learning, Transients (Dynamics)
Proceedings Papers

A Comparative Analysis of Various Machine Learning Approaches for Fault Diagnostics of Hydrogen Fueled Gas Turbines

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Muhammad Baqir Hashmi, Amare Desalegn Fentaye, Mohammad Mansouri, Konstantinos G. Kyprianidis
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A050, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129279
https://doi.org/10.1115/GT2024-129279
View Paper titled, A Comparative Analysis of Various Machine Learning Approaches for Fault Diagnostics of Hydrogen Fueled Gas Turbines
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Topics: Flaw detection, Gas turbines, Hydrogen fuels, Machine learning, Micro gas turbines
Proceedings Papers

Wind Turbine Drivetrain Fault Detection Using Multi-Variate Deep Learning Combined With Signal Processing

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Faras Jamil, Francisco Jara Avila, Konstantinos Vratsinis, Cédric Peeters, Jan Helsen
Proc. ASME. GT2023, Volume 14: Wind Energy, V014T37A003, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101689
https://doi.org/10.1115/GT2023-101689
View Paper titled, Wind Turbine Drivetrain Fault Detection Using Multi-Variate Deep Learning Combined With Signal Processing
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Topics: Flaw detection, Maintenance, Signal processing, Vibration, Wind turbines, Artificial neural networks, Emission spectroscopy, Errors, Fourier analysis, Machine learning
Proceedings Papers

Early Fault Detection Using Deep Learning on Compressed Cyclic Spectral Coherence Maps

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Fabian Perez Sanjines, Cédric Peeters, Timothy Verstraeten, Ivo Vervlimmeren, Ann Nowé, Jan Helsen
Proc. ASME. GT2023, Volume 14: Wind Energy, V014T37A001, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101596
https://doi.org/10.1115/GT2023-101596
View Paper titled, Early Fault Detection Using Deep Learning on Compressed Cyclic Spectral Coherence Maps
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Topics: Flaw detection, Turbines, Vibration, Failure, Maintenance, Signals, Artificial neural networks, Electromagnetic spectrum, Machinery, Renewable energy
Proceedings Papers

Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade

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Giuseppe Bruni, Senthil Krishnababu, Simon Jackson
Proc. ASME. GT2022, Volume 8A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction, V08AT21A019, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82427
https://doi.org/10.1115/GT2022-82427
View Paper titled, Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade
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Topics: Blades, Compressors, Machine learning, Rotors, Design, Artificial neural networks, Computational fluid dynamics, Algorithms, Cycles, Damping
Proceedings Papers

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

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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

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

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Peter Warren, Hessein Ali, Hossein Ebrahimi, Ranajay Ghosh
Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T05A013, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-59801
https://doi.org/10.1115/GT2021-59801
View Paper titled, Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks
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Topics: Artificial neural networks, Flaw detection, Optical quality control, Machine learning, Image processing, Inspection, Manufacturing, Nondestructive evaluation, Turbomachinery
Proceedings Papers

A Novel Vibration-Based Fault Detection Approach of Bolted Engineering Structures Without Reference

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Quankun Li, Zengde Shao, Mingfu Liao
Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT26A002, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-58493
https://doi.org/10.1115/GT2021-58493
View Paper titled, A Novel Vibration-Based Fault Detection Approach of Bolted Engineering Structures Without Reference
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Topics: Bolted joints, Flaw detection, Structures, Vibration
Proceedings Papers

Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network

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Chongyu Wang, Di Zhang, Yonghui Xie
Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A019, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-60247
https://doi.org/10.1115/GT2021-60247
View Paper titled, Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network
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Topics: Artificial neural networks, Computer simulation, Fault diagnosis, Flaw detection, Rotors, Steam turbines
Proceedings Papers

Possibilities and Limitations of Early Fault Detection in Gas Turbines

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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

A Comprehensive Modeling of Centrifugal Compressor Vibrations for Early Fault Detection

Open Access

François Libeyre, Francis Bainier, Pascal Alas
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A022, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15641
https://doi.org/10.1115/GT2020-15641
View Paper titled, A Comprehensive Modeling of Centrifugal Compressor Vibrations for Early Fault Detection
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Topics: Compressors, Flaw detection, Modeling, Vibration
Proceedings Papers

Data Driven Fault Detection of Premixer Centerbody Degradation in a Swirl Combustor

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Raghul Manosh Kumar, Benjamin Peters, Benjamin Emerson, Kamran Paynabar, Nagi Gebraeel, Timothy Lieuwen
Proc. ASME. GT2020, Volume 4A: Combustion, Fuels, and Emissions, V04AT04A007, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14222
https://doi.org/10.1115/GT2020-14222
View Paper titled, Data Driven Fault Detection of Premixer Centerbody Degradation in a Swirl Combustor
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Topics: Combustion chambers, Condition monitoring, Flaw detection, Algorithms, Flames, Gas turbines, Sensors, Sound pressure
Proceedings Papers

Application of Artificial Neural Network Based Gas Path Diagnostics on Gas Pipeline Compressors

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Suleiman M. Suleiman, Yi-Guang Li
Proc. ASME. GT2020, Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine, V009T21A007, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15062
https://doi.org/10.1115/GT2020-15062
View Paper titled, Application of Artificial Neural Network Based Gas Path Diagnostics on Gas Pipeline Compressors
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Topics: Artificial neural networks, Compressors, Pipelines, Compression, Corrosion, Erosion, Errors, Flaw detection, Flow (Dynamics), Instrumentation
Proceedings Papers

Comparative Analysis of Two Gas Turbine Diagnosis Approaches

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Igor Loboda, Juan Luis Pérez-Ruiz, Sergiy Yepifanov, Roman Zelenskyi
Proc. ASME. GT2019, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A025, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91644
https://doi.org/10.1115/GT2019-91644
View Paper titled, Comparative Analysis of Two Gas Turbine Diagnosis Approaches
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Topics: Gas turbines, Physics, Engines, Pattern recognition, Flaw detection, Algorithms, Artificial neural networks, Damage, Errors, Parameter estimation
Proceedings Papers

Extending Engine Gas Path Analysis Using Full Flight Data

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Liang Tang, Allan J. Volponi, Ethan Prihar
Proc. ASME. GT2019, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A004, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-90161
https://doi.org/10.1115/GT2019-90161
View Paper titled, Extending Engine Gas Path Analysis Using Full Flight Data
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Topics: Engines, Flight, Compressors, Flaw detection, Steady state, Algorithms, Delays, Fuel consumption, Maintenance, Sensors
Proceedings Papers

Vibration Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis

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Alexandre Mauricio, Linghao Zhou, David Mba, Konstantinos Gryllias
Proc. ASME. GT2019, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A020, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91150
https://doi.org/10.1115/GT2019-91150
View Paper titled, Vibration Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis
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Topics: Condition monitoring, Vibration, Mechanical drives, Bearings, Maintenance, Accidents, Decision making, Electromagnetic spectrum, Engines, Fatigue
Proceedings Papers

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

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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

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

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Alexandre Mauricio, Shuangwen Sheng, Konstantinos Gryllias
Proc. ASME. GT2019, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T48A008, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91136
https://doi.org/10.1115/GT2019-91136
View Paper titled, Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions
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Topics: Condition monitoring, Wind turbines, Bearings, Machinery, Mechanical drives, Stress, Vibration, Electromagnetic spectrum, Filtration, Internet of things
Proceedings Papers

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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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
Proceedings Papers

Vibration Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis

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Alexandre Mauricio, Junyu Qi, Konstantinos Gryllias
Proc. ASME. GT2018, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T48A017, June 11–15, 2018
Publisher: American Society of Mechanical Engineers
Paper No: GT2018-76993
https://doi.org/10.1115/GT2018-76993
View Paper titled, Vibration Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis
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Topics: Condition monitoring, Vibration, Wind turbines, Bearings, Filtration, Mechanical drives, Electromagnetic spectrum, Machinery, Rolling bearings, Signals