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Water-lubricated journal bearings : marine applications, design, and operational problems and solutions
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ISBN: 0443134588 Year: 2024 Publisher: Cambridge, MA : Elsevier Inc.,

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Incompressibly lubricated rayleigh step journal bearing. : zero-order perturbation solution
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Year: 1968 Publisher: Washington, D.C. : National Aeronautics and Space Administration,

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Load capacity estimation of foil air journal bearings for oil-free turbomachinery applications
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Year: 2000 Publisher: Cleveland, Ohio : National Aeronautics and Space Administration, Glenn Research Center : U.S. Army Research Laboratory,

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Incompressibly lubricated Rayleigh step journal bearing.
Authors: --- --- ---
Year: 1968 Publisher: Washington, D.C. : National Aeronautics and Space Administration,

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Hydrodynamic lubrication
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ISBN: 1281112283 9786611112288 0080534317 0444823662 9780444823663 9780080534312 Year: 1997 Publisher: Amsterdam [Netherlands] New York Elsevier

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Hydrodynamic Lubrication is the culmination of over 20 years close, collaborative work by the five authors and discusses the practical use of the formalization of low pressure lubrication. The work concentrates on the developments to journal and thrust bearings and includes subjects such as: the dynamic behaviour of plain and tilting-pads the thermal aspects the positive and negative effects of non-cyclindricity and shape defects resulting from manufacturing or operation the effects of inertia the appearance of Taylor's vortices and of turbulence and their reper


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Journal bearings in turbomachinery
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ISBN: 0412095904 1475756259 1475756232 Year: 1969 Publisher: London Chapman and Hall


Book
Machine Learning in Tribology
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Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.


Book
Machine Learning in Tribology
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Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

Keywords

Technology: general issues --- History of engineering & technology --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound


Book
Non-Circular Journal Bearings
Author:
ISBN: 3319273310 3319273337 Year: 2016 Publisher: Cham : Springer International Publishing : Imprint: Springer,

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This brief details non-circular journal bearing configurations. The author describes the mathematical and experimental studies that pertain to non-circular journal bearing profiles and how they can be applied to other types of bearing profiles with some modifications. He also examines non-circular journal bearing classifications, the methodology needed to carry out mathematical modeling, and the experimental procedures used to determine oil-film temperature and pressures.

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