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Proceedings Papers

Reinforcement Learning-Based Control of a Hydraulic Press With Multiple Actuators

Faried Makansi, Jingkai Huang, Katharina Schmitz
Proc. ASME. FPMC2023, ASME/BATH 2023 Symposium on Fluid Power and Motion Control, V001T01A010, October 16–18, 2023
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2023-110846
https://doi.org/10.1115/FPMC2023-110846
View Paper titled, Reinforcement Learning-Based Control of a Hydraulic Press With Multiple Actuators
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Topics: Actuators, Hydraulic presses, Reinforcement learning, Algorithms, Control equipment, Valves, Design, Hydraulic control, Hydraulics, Machine learning
Proceedings Papers

Detection of Typical Manufacturing Errors in External Gear Machines Using Numerical Simulation and Data Driven Machine Learning

Pasquale Borriello, Ajinkya Pawar, Emma Frosina, Fabrizio Tessicini, Andrea Vacca, Adolfo Senatore
Proc. ASME. FPMC2023, ASME/BATH 2023 Symposium on Fluid Power and Motion Control, V001T01A070, October 16–18, 2023
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2023-113369
https://doi.org/10.1115/FPMC2023-113369
View Paper titled, Detection of Typical Manufacturing Errors in External Gear Machines Using Numerical Simulation and Data Driven Machine Learning
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Topics: Computer simulation, Errors, Gears, Machine learning, Machinery, Manufacturing
Proceedings Papers

The Impact of Electric Drive Structures on Sensorless AI-Based Hydraulic Valve Fault Classification

Viacheslav Zakharov, Abid Abdul Azeez, Xu Han, Tatiana Minav
Proc. ASME. FPMC2023, ASME/BATH 2023 Symposium on Fluid Power and Motion Control, V001T01A074, October 16–18, 2023
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2023-114712
https://doi.org/10.1115/FPMC2023-114712
View Paper titled, The Impact of Electric Drive Structures on Sensorless AI-Based Hydraulic Valve Fault Classification
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Topics: Condition monitoring, Electric propulsion, Hydraulics, Machine learning, Valves
Proceedings Papers

Machine Learning for Fault Diagnosis and Operation Mode Detection in Hydraulic Cylinders

Jose A. Solorio, Jose M. Garcia-Bravo
Proc. ASME. FPMC2023, ASME/BATH 2023 Symposium on Fluid Power and Motion Control, V001T01A059, October 16–18, 2023
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2023-111893
https://doi.org/10.1115/FPMC2023-111893
View Paper titled, Machine Learning for Fault Diagnosis and Operation Mode Detection in Hydraulic Cylinders
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Topics: Artificial intelligence, Fault diagnosis, Hydraulic cylinders, Hydraulic drive systems, Machine learning
Proceedings Papers

Online Monitoring of Pneumatic Actuation System for Energy Efficiency and Dynamic Performance

Vinícius Vigolo, Vladimir Boyko, Jürgen Weber, Antonio Carlos Valdiero, Victor Juliano De Negri
Proc. ASME. FPMC2023, ASME/BATH 2023 Symposium on Fluid Power and Motion Control, V001T01A054, October 16–18, 2023
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2023-111861
https://doi.org/10.1115/FPMC2023-111861
View Paper titled, Online Monitoring of Pneumatic Actuation System for Energy Efficiency and Dynamic Performance
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Topics: Energy efficiency, Machine learning, Robustness
Proceedings Papers

Fault Detection and Diagnosis for a Hydraulic Press by Use of a Mixed Domain Database

Faried Makansi, Katharina Schmitz
Proc. ASME. FPMC2022, BATH/ASME 2022 Symposium on Fluid Power and Motion Control, V001T01A009, September 14–16, 2022
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2022-88646
https://doi.org/10.1115/FPMC2022-88646
View Paper titled, Fault Detection and Diagnosis for a Hydraulic Press by Use of a Mixed Domain Database
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Topics: Databases, Flaw detection, Hydraulic presses, Machine learning, Machinery, Condition monitoring, Data acquisition, Maintenance, Manifolds, Simulation models
Proceedings Papers

Exhaustive Regressor Search (XRS) for Creating Models of Hydraulic Pumps and Motors

Jack Johnson, John Montague, Jose Garcia-Bravo
Proc. ASME. FPMC2021, ASME/BATH 2021 Symposium on Fluid Power and Motion Control, V001T01A059, October 19–21, 2021
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2021-70568
https://doi.org/10.1115/FPMC2021-70568
View Paper titled, Exhaustive Regressor Search (XRS) for Creating Models of Hydraulic Pumps and Motors
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Topics: Flow (Dynamics), Hydraulic pumps, Motors, Pumps, Statistical analysis, Algorithms, Computers, Fluid power systems, Fluids, Machine learning
Proceedings Papers

Machine Learning Prediction of Journal Bearing Pressure Distributions, Considering Elastic Deformation and Cavitation

Nathan Hess, Lizhi Shang
Proc. ASME. FPMC2021, ASME/BATH 2021 Symposium on Fluid Power and Motion Control, V001T01A010, October 19–21, 2021
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2021-68483
https://doi.org/10.1115/FPMC2021-68483
View Paper titled, Machine Learning Prediction of Journal Bearing Pressure Distributions, Considering Elastic Deformation and Cavitation
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Topics: Artificial neural networks, Cavitation, Deformation, Journal bearings, Machine learning, Pressure
Proceedings Papers

Intelligent Machine Operator Identification to Develop Damage-Reducing Operating Strategies for Mobile Machines

Lars Brinkschulte, Marcus Geimer
Proc. ASME. FPMC2019, ASME/BATH 2019 Symposium on Fluid Power and Motion Control, V001T01A017, October 7–9, 2019
Publisher: American Society of Mechanical Engineers
Paper No: FPMC2019-1643
https://doi.org/10.1115/FPMC2019-1643
View Paper titled, Intelligent Machine Operator Identification to Develop Damage-Reducing Operating Strategies for Mobile Machines
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Topics: Damage, Intelligent machines, Machinery, Stress, Simulation, Adaptive control, Control systems, Cycles, Damping, Degrees of freedom