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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

Paper No: GT2022-81294

DOI: https://doi.org/10.1115/GT2022-81294

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Topics: Bearings, Condition monitoring, Flaw detection, Mechanical drives, Vibration, Wind turbines

Proceedings Papers

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

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

Paper No: GT2022-82427

DOI: https://doi.org/10.1115/GT2022-82427

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Topics: Blades, Compressors, Machine learning, Rotors, Design, Artificial neural networks, Computational fluid dynamics, Algorithms, Cycles, Damping

Proceedings Papers

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

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

Paper No: GT2021-59801

DOI: https://doi.org/10.1115/GT2021-59801

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Topics: Artificial neural networks, Flaw detection, Optical quality control, Machine learning, Image processing, Inspection, Manufacturing, Nondestructive evaluation, Turbomachinery

Proceedings Papers

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

Chongyu Wang, Di Zhang, Yonghui Xie

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A019, June 7–11, 2021

Paper No: GT2021-60247

DOI: https://doi.org/10.1115/GT2021-60247

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Topics: Artificial neural networks, Computer simulation, Fault diagnosis, Flaw detection, Rotors, Steam turbines

Proceedings Papers

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

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

Paper No: GT2021-58493

DOI: https://doi.org/10.1115/GT2021-58493

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Topics: Bolted joints, Flaw detection, Structures, Vibration

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

Paper No: GT2020-16251

DOI: https://doi.org/10.1115/GT2020-16251

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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

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

Paper No: GT2020-15641

DOI: https://doi.org/10.1115/GT2020-15641

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Topics: Compressors, Flaw detection, Modeling, Vibration

Proceedings Papers

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

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

Paper No: GT2020-14222

DOI: https://doi.org/10.1115/GT2020-14222

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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

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

Paper No: GT2020-15062

DOI: https://doi.org/10.1115/GT2020-15062

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Topics: Artificial neural networks, Compressors, Pipelines, Compression, Corrosion, Erosion, Errors, Flaw detection, Flow (Dynamics), Instrumentation

Proceedings Papers

Vibration Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis

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

Paper No: GT2019-91150

DOI: https://doi.org/10.1115/GT2019-91150

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Topics: Condition monitoring, Vibration, Mechanical drives, Bearings, Maintenance, Accidents, Decision making, Electromagnetic spectrum, Engines, Fatigue

Proceedings Papers

Comparative Analysis of Two Gas Turbine Diagnosis Approaches

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

Paper No: GT2019-91644

DOI: https://doi.org/10.1115/GT2019-91644

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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

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

Paper No: GT2019-90161

DOI: https://doi.org/10.1115/GT2019-90161

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Topics: Engines, Flight, Compressors, Flaw detection, Steady state, Algorithms, Delays, Fuel consumption, Maintenance, Sensors

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

Paper No: GT2019-90055

DOI: https://doi.org/10.1115/GT2019-90055

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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

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

Paper No: GT2019-91136

DOI: https://doi.org/10.1115/GT2019-91136

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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

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

Paper No: GT2019-90056

DOI: https://doi.org/10.1115/GT2019-90056

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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

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

Paper No: GT2018-76993

DOI: https://doi.org/10.1115/GT2018-76993

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Topics: Condition monitoring, Vibration, Wind turbines, Bearings, Filtration, Mechanical drives, Electromagnetic spectrum, Machinery, Rolling bearings, Signals

Proceedings Papers

A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

Igor Loboda, Juan Luis Pérez-Ruiz, Sergiy Yepifanov

Proc. ASME. GT2018, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A027, June 11–15, 2018

Paper No: GT2018-76887

DOI: https://doi.org/10.1115/GT2018-76887

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Topics: Gas turbines, Algorithms, Flaw detection, Engines, Feature extraction, Aircraft engines, Computer software, Monitoring systems, Pattern recognition, Polynomials

Proceedings Papers

Fleet Monitoring and Diagnostics Framework Based on Digital Twin of Aero-Engines

Valentina Zaccaria, Mikael Stenfelt, Ioanna Aslanidou, Konstantinos G. Kyprianidis

Proc. ASME. GT2018, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A021, June 11–15, 2018

Paper No: GT2018-76414

DOI: https://doi.org/10.1115/GT2018-76414

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Topics: Aircraft, Aircraft engines, Engines, Failure, Flaw detection, Flight, Fuel consumption, Machine learning, Maintenance, Physics

Proceedings Papers

A Two-Layer Multi-Model Gas Path Fault Diagnosis Method

Yunpeng Cao, Yinghui He, Fang Yu, Jianwei Du, Shuying Li, Qingcai Yang, Rui Liu

Proc. ASME. GT2018, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A009, June 11–15, 2018

Paper No: GT2018-75368

DOI: https://doi.org/10.1115/GT2018-75368

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Topics: Fault diagnosis, Flaw detection, Gas turbines, Algorithms, Simulation, Artificial neural networks, Probability, Statistics

Proceedings Papers

Automated Condition Evaluation of Hot-Gas Path Components of Jet Engines Through Exhaust Jet Analysis

Ulrich Hartmann, Joerg R. Seume

Proc. ASME. GT2018, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A011, June 11–15, 2018

Paper No: GT2018-75384

DOI: https://doi.org/10.1115/GT2018-75384

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Topics: Exhaust systems, Jet engines, Density, Algorithms, Support vector machines, Aircraft engines, Blades, Computational fluid dynamics, Engineering simulation, Film cooling

2 3 4 5

NARROW

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

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

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

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

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

Possibilities and Limitations of Early Fault Detection in Gas Turbines

A Comprehensive Modeling of Centrifugal Compressor Vibrations for Early Fault Detection

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

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

Vibration Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis

Comparative Analysis of Two Gas Turbine Diagnosis Approaches

Extending Engine Gas Path Analysis Using Full Flight Data

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

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Validation of an Advanced Diagnostic Methodology for the Identification and Classification of Gas Turbine Sensor Faults by Means of Field Data

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

A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

Fleet Monitoring and Diagnostics Framework Based on Digital Twin of Aero-Engines

A Two-Layer Multi-Model Gas Path Fault Diagnosis Method

Automated Condition Evaluation of Hot-Gas Path Components of Jet Engines Through Exhaust Jet Analysis

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