Lubrication Regime Classification of Hydrodynamic Journal Bearings by Machine Learning Using Torque Data

Moder, Jakob; Bergmann, Philipp; Grün, Florian

doi:10.3390/lubricants6040108

Cited by 30 publications

(15 citation statements)

References 28 publications

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“…The ANN predictions show high agreement to the experimental data and the authors stated that such ANNs can effectively reduce the number of future experiments. Furthermore, Moder et al [77] showed that supervised ML algorithms can be used to predict the lubrication regime of hydrodynamic radial journal bearings based on given torques. Therefore, the torque time series were first analyzed using Fast Fourier Transformation (FFT) and manually assigned to lubrication regimes.…”

Section: Sliding Bearingsmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

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Section: Sliding Bearingsmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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“…In addition, AI/ML algorithms can be used to predict lubricant film formation and friction behavior in thermo-hydrodynamically (THL) and thermo-elastohydrodynamically lubricated (TEHL) contacts. For example, Moder et al [27] used highspeed data signals of a torque sensor obtained from a journal bearing test-rig to train ML models for predicting lubrication regimes (Figure 5). Main results showed that deep and shallow neural networks performed equally well, reaching high accuracies.…”

Section: Lubrication and Fluid Film Formationmentioning

confidence: 99%

The Use of Artificial Intelligence in Tribology—A Perspective

et al. 2020

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Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

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“…Wear and misalignment identification on journal bearings using ANNs was presented by Saridakis et al [17]. The characterization of the lubrication regimes of journal bearings by machine learning (ML) presented by Moder et al [18]. Otero et al [19] presented the ability of ANNs to predict the friction coefficient, learn by experimental data.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Misaligned Journal Bearings Using Neural Networks

Kokkinidis¹,

Nikolakopoulos²

2022

Tribol. Ind.

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Utilization of smart systems, i.e. software tools that incorporate artificial intelligence (AI), in engineering applications increases. This fact is due to their ability to study the performance of complicated systems, producing results quicker and easier than typical analytical models. This article is focused on the advantages of using Artificial Neural Networks (ANNs) to solve the problem of a misaligned hydrodynamic journal bearing. Firstly, the Reynolds equation is solved using finite difference method (FDM) for different operating and misalignment conditions. The results are used to train four (4) artificial neural networks, one for each design parameter. Afterwards, the networks are tested for several operational characteristics and compared with the results of the finite difference method. The outcome is that the force and the torque can be predicted with maximum error of approximately 5% with less computational cost than the finite difference method.

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Lubrication Regime Classification of Hydrodynamic Journal Bearings by Machine Learning Using Torque Data

Cited by 30 publications

References 28 publications

Current Trends and Applications of Machine Learning in Tribology—A Review

Current Trends and Applications of Machine Learning in Tribology—A Review

The Use of Artificial Intelligence in Tribology—A Perspective

Simulation of Misaligned Journal Bearings Using Neural Networks

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