Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
3.6
Journals
Lubricants
Special Issues
Tribo-Dynamic Behaviors of Bearing–Rotor Systems
share announcement

Tribo-Dynamic Behaviors of Bearing–Rotor Systems

A Special Issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 540

Editors

Dr. Huihui Feng

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Interests: bearing–rotor dynamics; engineering tribology; lubrication theory
Dr. Xun Huang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui Universityof Technology, Ma'anshan 243000, China
Interests: smart bearings; lubrication analysis and structural design; rotor dynamics
Dr. Wan Zhang

E-Mail Website
Guest Editor
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China
Interests: rolling bearings; fault diagnosis

Special Issue Information

Dear Colleagues,

We are delighted to extend an invitation for submissions to a Special Issue on the subject of “Tribo-Dynamic Behaviors of Bearing–Rotor Systems”. Our aim with this Special Issue is to compile a comprehensive collection of the most recent cutting-edge developments and innovations in the field of rotor system reliability and performance.

We are seeking both review articles and original research that provide theoretical explorations, ground-breaking experimental research, and inventive computational approaches. These contributions will help deepen our understanding of the coupled tribological and dynamic mechanisms at play within rotating machinery. We also encourage novel applications that push the existing boundaries of this field, especially those addressing high-speed, heavy-load, and non-linear operating conditions. The scope of this Special Issue includes topics such as tribo-dynamic modeling, system stability and vibration, lubricant effects on rotor performance, and fault diagnosis in the context of various bearing types (hydrodynamic, rolling element, magnetic), as well as smart lubrication techniques and condition monitoring strategies for enhanced reliability.

We are particularly eager to receive submissions from a diverse range of academics, industry researchers, and practitioners who are contributing to this rapidly evolving field. This call for papers represents an exceptional opportunity for your work to reach a broad audience and for you to engage with others in your field.

Dr. Huihui Feng
Dr. Xun Huang
Dr. Wan 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • rotor dynamics
  • tribo-dynamics
  • rotor stability
  • lubrication dynamics
  • non-linear vibrations
  • fluid–film bearings
  • fault diagnosis
  • lubricant properties
  • contact mechanics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

37 pages, 68424 KB  
Article
A Multi-Aspect Geometric Structure Learning and Discrimination Framework for Few-Shot Fault Diagnosis of Rolling Bearings
by Shengyao Wang, Meng Li, Yu Cao, Yuan Ma and Jian Ren
Lubricants 2026, 14(8), 290; https://doi.org/10.3390/lubricants14080290 - 27 Jul 2026
Viewed by 228
Abstract
Identifying rolling bearing faults under few-shot conditions remains difficult because fault samples are scarce, class-space distributions are unstable, and inter-class boundaries may become ambiguous. This paper proposes a fault diagnosis model based on the Multi-Aspect Geometric Structure Learning and Discrimination Framework (MAGS-LDF). First, [...] Read more.
Identifying rolling bearing faults under few-shot conditions remains difficult because fault samples are scarce, class-space distributions are unstable, and inter-class boundaries may become ambiguous. This paper proposes a fault diagnosis model based on the Multi-Aspect Geometric Structure Learning and Discrimination Framework (MAGS-LDF). First, one-dimensional vibration signals are mapped into three two-dimensional representations, namely angle field (AF), band-energy (BE), and time-frequency (TF) images, to describe fault information from temporal correlation, frequency–band energy distribution, and time-frequency response perspectives. Second, a dual-branch feature extraction network is designed to extract fusion features and channel features. For each fault class, channel features are aggregated into channel centers, which are further fused to obtain a public center. Moreover, regular polytope anchor centers aligned with the public-center distribution are introduced to impose geometric constraints, encouraging intra-class compactness and inter-class separation. Finally, channel distances and public distances are jointly used to construct a multi-scale distance discrimination mechanism for few-shot fault classification. Experiments on CWRU and SEU show that MAGS-LDF outperforms the best comparison method by 2.34%, 5.37%, and 5.34% on CWRU and by 3.55%, 5.10%, and 3.46% on SEU under the three-shot, five-shot, and 10-shot settings, respectively. Full article
(This article belongs to the Special Issue Tribo-Dynamic Behaviors of Bearing–Rotor Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop