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Machine Learning in Fluid Power - Applications, Trends, and Challenges.

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Title: Machine Learning in Fluid Power - Applications, Trends, and Challenges.
Authors: Brumand-Poor, Faras1 faras.brumand@ifas.rwth-aachen.de, Sharma, Barkha1, Duensing, Yannick1, Logothetis, Panagiotis1, Atali, Emre1, Schmitz, Katharina1
Source: International Journal of Fluid Power. Apr2026, Vol. 27 Issue 2, p359-433. 75p.
Subject Terms: *Machine learning, *Fluid power technology, *Nonlinear systems, *Systems design, *Energy consumption
Abstract: The importance of machine learning (ML) in various engineering disciplines has steadily increased over the past several years, primarily due to the rise in computational power and the development of new, powerful algorithms. ML methods have also found significant applications in fluid power technology, offering substantial benefits such as enhanced system performance, optimized design processes, and improved predictive maintenance. These methods are increasingly used to handle complex, nonlinear systems in fluid power, providing advanced solutions for simulation, system design, control strategies, and condition monitoring. In particular, ML techniques can process vast amounts of data to predict system behavior, identify faults, and optimize energy efficiency, leading to more reliable and efficient fluid power systems. This review aims to introduce and discuss the wide range of ML applications and ongoing research in fluid power, offering engineers a comprehensive overview of the available literature. By doing so, we aim to help engineers identify and select the most suitable and promising ML methods to address their specific tasks and challenges. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: Machine Learning in Fluid Power - Applications, Trends, and Challenges.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Fluid+Power%22">International Journal of Fluid Power</searchLink>. Apr2026, Vol. 27 Issue 2, p359-433. 75p.
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  Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Fluid+power+technology%22">Fluid power technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Systems+design%22">Systems design</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink>
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  Data: The importance of machine learning (ML) in various engineering disciplines has steadily increased over the past several years, primarily due to the rise in computational power and the development of new, powerful algorithms. ML methods have also found significant applications in fluid power technology, offering substantial benefits such as enhanced system performance, optimized design processes, and improved predictive maintenance. These methods are increasingly used to handle complex, nonlinear systems in fluid power, providing advanced solutions for simulation, system design, control strategies, and condition monitoring. In particular, ML techniques can process vast amounts of data to predict system behavior, identify faults, and optimize energy efficiency, leading to more reliable and efficient fluid power systems. This review aims to introduce and discuss the wide range of ML applications and ongoing research in fluid power, offering engineers a comprehensive overview of the available literature. By doing so, we aim to help engineers identify and select the most suitable and promising ML methods to address their specific tasks and challenges. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192950858
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        Value: 10.13052/ijfp1439-9776.2724
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        Text: English
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        StartPage: 359
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      – SubjectFull: Nonlinear systems
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              Text: Apr2026
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