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Lubricants
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New Conceptions in Bearing Lubrication and Temperature Monitoring
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New Conceptions in Bearing Lubrication and Temperature Monitoring

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: 1 December 2024 | Viewed by 1377

Special Issue Editors

Prof. Dr. Ke Yan

E-Mail Website
Guest Editor
Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
Interests: rolling bearing; lubrication technology; simulation modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Shuaijun Ma

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Guest Editor
Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
Interests: bearing modeling; optimization design; state modeling
Dr. Pan Zhang

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Guest Editor
Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China
Interests: condition monitoring; quantum dot; thermoanalysis

Special Issue Information

Dear Colleagues,

Bearings are currently the most widely used mechanical components. In the process of high-speed and heavy load operations, friction and heat generation between rolling elements, cages, and rings rise. At this time, lubrication technology is integral to reducing bearing friction and wear, strengthening bearing heat dissipation, and extending bearing life.

In the last century, integral research on various aspects of bearing thermal analysis and corresponding lubrication technology has been extensive. However, as bearing speeds continue to increase, complex operation conditions pose more challenges to bearing thermal analysis. At the same time, the continuous cross-fusion of materials, sensors, big data, and emerging technologies has enabled the continuous expansion of bearing lubrication technology.

This Special Issue is aimed at the latest developments centered around bearing thermal mechanisms and lubrication technology, as well as the effect of bearing working parameters on lubrication performance and thermal behavior.

Prof. Dr. Ke Yan
Dr. Shuaijun Ma
Dr. Pan Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Lubricants is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bearing lubrication
  • bearing thermal analysis
  • dynamic modelling
  • lubricant flow simulation

Published Papers (3 papers)

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Research

27 pages, 7911 KiB  
Article
Development of a Digital Model for Predicting the Variation in Bearing Preload and Dynamic Characteristics of a Milling Spindle under Thermal Effects
by Tria Mariz Arief, Wei-Zhu Lin, Muhamad Aditya Royandi and Jui-Pin Hung
Lubricants 2024, 12(6), 185; https://doi.org/10.3390/lubricants12060185 - 23 May 2024
Viewed by 321
Abstract
The spindle tool is an important module of the machine tool. Its dynamic characteristics directly affect the machining performance, but it could also be affected by thermal deformation and bearing preload. However, it is difficult to detect the change in the bearing preload [...] Read more.
The spindle tool is an important module of the machine tool. Its dynamic characteristics directly affect the machining performance, but it could also be affected by thermal deformation and bearing preload. However, it is difficult to detect the change in the bearing preload through sensory instruments. Therefore, this study aimed to establish a digital thermal–mechanical model to investigate the thermal-induced effects on the spindle tool system. The technologies involved include the following: Run-in experiments of the milling spindle at different speeds, the establishment of the thermal–mechanical model, identification of the thermal parameters, and prediction of the thermal-induced preload of bearings in the spindle. The speed-dependent thermal parameters were identified from thermal analysis through comparisons with transient temperature history, which were further used to model the thermal effects on the bearing preload and dynamic compliance of the milling spindle under different operating speeds. Current results of thermal–mechanical analysis also indicate that the internal temperature of the bearing can reach 40 °C, and the thermal elongation of the spindle tool is about 27 µm. At the steady state temperature of 15,000 rpm, the bearing preload is reduced by 40%, which yields a decrease in the bearing rigidity by approximately 16%. This, in turn, increases the dynamic compliance of the spindle tool by 22%. Comparisons of the experimental measurements and modeling data show that the variation in bearing preload substantially affects the modal frequency and stiffness of the spindle. These findings demonstrated that the proposed digital spindle model accurately mirrors real spindle characteristics, offering a foundation for monitoring performance changes and refining design, especially in bearing configuration and cooling systems. Full article
(This article belongs to the Special Issue New Conceptions in Bearing Lubrication and Temperature Monitoring)
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13 pages, 5025 KiB  
Article
Test Method for Rapid Prediction of Steady-State Temperature of Outer Rings of Bearings under Grease Lubrication Conditions
by Zhongbing Xia, Fang Yang, Xiqiang Ma, Nan Guo, Xiao Wang, Yunhao Cui and Yuchen Duan
Lubricants 2024, 12(5), 177; https://doi.org/10.3390/lubricants12050177 - 15 May 2024
Viewed by 455
Abstract
Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional [...] Read more.
Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional theoretical calculations and simulation analysis methods such as the large number of calculations, complex models, and large errors. Firstly, a mathematical prediction model is established according to the bearing temperature-rise law; then, a bearing steady-state temperature detection device is designed; and finally, the prediction model parameters are solved according to the experimental results, and experimental verification is carried out. It is shown that the prediction model has high accuracy under different load and speed conditions, and the error between the predicted steady-state temperature and the tested steady-state temperature is less than 0.7 °C. This prediction method reduces the single test time of the same speed to 60 min, which greatly improves the efficiency of the temperature detection test. The steady-state temperature model has important theoretical significance in guiding the study of the limiting speed of bearings. Full article
(This article belongs to the Special Issue New Conceptions in Bearing Lubrication and Temperature Monitoring)
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19 pages, 3432 KiB  
Article
AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings
by Juan Xu, Bin Ma, Weiwei Chen and Chengwei Shan
Lubricants 2024, 12(5), 175; https://doi.org/10.3390/lubricants12050175 - 14 May 2024
Viewed by 369
Abstract
The remaining useful life (RUL) of bearings is vital for the manipulation and maintenance of industrial machines. The existing domain adaptive methods have achieved major achievements in predicting RUL to tackle the problem of data distribution discrepancy between training and testing sets. However, [...] Read more.
The remaining useful life (RUL) of bearings is vital for the manipulation and maintenance of industrial machines. The existing domain adaptive methods have achieved major achievements in predicting RUL to tackle the problem of data distribution discrepancy between training and testing sets. However, they are powerless when the target bearing data are not available or unknown for model training. To address this issue, we propose a single-source domain generalization method for RUL prediction of unknown bearings, termed as the adaptive stage division and parallel reversible instance normalization model. First, we develop the instance normalization of the vibration data from bearings to increase data distribution diversity. Then, we propose an adaptive threshold-based degradation point identification method to divide the healthy and degradation stages of the run-to-failure vibration data. Next, the data from degradation stages are selected as training sets to facilitate the RUL prediction of the model. Finally, we combine instance normalization and instance denormalization of the bearing data into a unified GRU-based RUL prediction network for the purpose of leveraging the distribution bias in instance normalization and improving the generalization performance of the model. We use two public datasets to verify the proposed method. The experimental results demonstrate that, in the IEEE PHM Challenge 2012 dataset experiments, the prediction accuracy of our model with the average RMSE value is 1.44, which is 11% superior to that of the suboptimal comparison model (Transformer model). It proves that our model trained on one-bearing data achieves state-of-the-art performance in terms of prediction accuracy on multiple bearings. Full article
(This article belongs to the Special Issue New Conceptions in Bearing Lubrication and Temperature Monitoring)
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