Machine learning-based prediction of friction torque and friction coefficient in statically loaded radial journal bearings

Baş, Hasan; Karabacak, Yunus Emre

doi:10.1016/j.triboint.2023.108592

Cited by 18 publications

(2 citation statements)

References 38 publications

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“…This results in different lubrication conditions for each bearing pair, leading to dispersity of their service life. If each bearing pair can be designed to have the same service life, the life cycle and application value of the camshaft can be maximized (Allmaier et al, 2012;Hasan and Yunus, 2023). The minimum oil film thickness indicates the level of solid-to-solid contact is widely used as a parameter to determine the lubrication life of bearing pairs (Wu et al, 2020;Wang et al, 2019;Wei et al, 2015;Ji et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Zhao,

Li,

Xie

et al. 2024

ILT

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Purpose This study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine. Design/methodology/approach A lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness. Findings The results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss. Originality/value The optimized performance and corresponding structural parameters are obtained, and the optimization results were verified through multibody dynamics simulation. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0417/

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Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Zhao,

Li,

Xie

et al. 2024

ILT

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“…Other studies focus on areas such as the anomaly detection of force signals [18], the classification of operational states [15,17,[19][20][21], load prediction [22], the estimation of model-based remaining useful life and wear prediction [23], and supervised wear volume estimation [24]. Data-driven regression models have been recently employed to assess the influence of temperature, bearing load, and rotational speed on the variation in friction torque and friction coefficient [25]. Bote-Garcia and Gühmann [26] used the integrated acoustic emission rooted-mean-squared value to estimate the wear state.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Ates,

Höfchen,

Witt

et al. 2023

Sensors

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Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

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Investigating the Effects of Oil Additives on the Tribological Performance of Hydrodynamic Journal Bearings: A Study With Hazelnut Oil in Boundary and Mixed Lubrication Regimes

Baş,

Karabacak

2024

Lubrication Science

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This study investigates the impact of oil additives on the performance of hydrodynamic journal bearings using hazelnut oil. Various additives, including titanium dioxide, hexagonal boron nitride and graphite, are mixed with hazelnut oil in specific concentrations. Experimental tests are conducted in boundary and mixed lubrication regimes using a specialised rig to simulate bearing operating conditions. The friction performance of these oil‐additive combinations is evaluated in terms of bearing load, rotating speed and oil temperature. The findings contribute to understanding hazelnut oil as a potential lubricant and optimising its formulation for specific applications, promoting environmentally friendly lubricants and sustainability.

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Machine learning-based prediction of friction torque and friction coefficient in statically loaded radial journal bearings

Cited by 18 publications

References 38 publications

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Investigating the Effects of Oil Additives on the Tribological Performance of Hydrodynamic Journal Bearings: A Study With Hazelnut Oil in Boundary and Mixed Lubrication Regimes

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