Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.9
Journals
Lubricants
Special Issues
Advanced Methods for Wear Monitoring
share announcement

Advanced Methods for Wear Monitoring

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 1269

Special Issue Editors

Dr. Shuo Wang

E-Mail Website
Guest Editor
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China
Interests: wear debris analysis; wear mechanism identification; machine condition monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Shao

E-Mail
Guest Editor Assistant
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, China
Interests: condition monitoring; oil

Special Issue Information

Dear Colleagues,

Wear is a fundamental and unavoidable failure mode of tribological systems, involving diverse physical and chemical mechanisms. Accurate analysis of wear surfaces, early damage detection, and continuous monitoring of wear states are essential for understanding wear progression and preventing catastrophic failures in mechanical systems.

This Special Issue invites original research and comprehensive review articles focused on the latest advances in wear monitoring techniques and intelligent diagnostic methodologies. Topics of interest include, but are not limited to:

  • Novel sensor technologies for in situ and real-time wear measurement;
  • Advanced signal processing and feature extraction methods for wear data;
  • Intelligent algorithms for wear state recognition and prognosis (e.g., machine learning and deep learning);
  • Wear mechanism identification through surface and debris analysis;
  • Isotopic tracing and other advanced methods for quantitative wear evaluation;
  • Integration of multi-sensor data for improved wear diagnosis;
  • Case studies and practical applications in industrial tribo-systems.

Both experimental and numerical studies that contribute to the development of reliable wear monitoring systems are welcome. The Special Issue aims to showcase multidisciplinary research that enhances the understanding of wear processes and promotes the implementation of intelligent, accurate, and robust wear monitoring solutions.

Dr. Shuo Wang
Guest Editor

Dr. Tao Shao
Guest Editor Assistant

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-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

  • wear analysis
  • wear mechanism identification
  • intelligent diagnostic algorithms and applications
  • real-time monitoring

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 (2 papers)

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

Research

18 pages, 8612 KB  
Article
Unsupervised Segmentation of Wear Surface Defects in Hydroturbine Bearing Pads Guided by Local Anomaly Scores
by Xiaolong Yang, Jingxuan Han, Gang Wan, Fengdi Zhu, Chuangji Qin, Ning Xu and Shuo Wang
Lubricants 2026, 14(5), 202; https://doi.org/10.3390/lubricants14050202 - 14 May 2026
Viewed by 121
Abstract
Vision-based defect detection on bearing-pad wear surfaces is essential for quantifying damage geometry and assessing condition in hydroturbine units. Compared with 2D color images, depth images can suppress disturbances caused by complex textures, surface color variations, and specular reflections, thereby providing a more [...] Read more.
Vision-based defect detection on bearing-pad wear surfaces is essential for quantifying damage geometry and assessing condition in hydroturbine units. Compared with 2D color images, depth images can suppress disturbances caused by complex textures, surface color variations, and specular reflections, thereby providing a more reliable basis for precise damage localization. Nevertheless, depth-based damage segmentation under a large field of view remains challenging, mainly due to fine-scale texture noise and weak defect saliency; moreover, robust defect probability estimation is often hindered by limited labeled data. To address these challenges, this paper proposes an unsupervised defect segmentation framework for hydroturbine friction components guided by local anomaly score distributions. First, a salient damage detection module is developed based on topography–texture separation, which mitigates the interference of local micro-texture noise on defect segmentation. Then, a normal reference dataset is constructed using defect-free bearing-pad depth images, and an unsupervised network is employed as the core to generate anomaly score representations of potential damage regions for coarse localization. Finally, the obtained anomaly score distribution is used as adaptive weights to fuse depth-based defect cues with morphological processing, enabling self-adaptive refinement of the damage regions. Experiments on real depth images acquired from hydroturbine bearing pads demonstrate that the proposed method achieves accurate defect extraction and reliable geometric quantification. Quantitative evaluations on the testing set yield a mean surface area error of 9.39% ± 4.25% and a volume error of 4.91% ± 2.85%, with best-case errors dropping as low as 3.67% and 1.03%, respectively. Crucially, these results demonstrate that our framework goes beyond mere visual detection; by operating entirely without pixel-level annotations, it offers a highly practical tool for diagnosing specific lubrication failure modes and driving predictive maintenance in actual hydroturbine engineering. Full article
(This article belongs to the Special Issue Advanced Methods for Wear Monitoring)
►▼ Show Figures

Figure 1

20 pages, 4713 KB  
Article
Early-Stage Damage Diagnosis of Rolling Bearings Based on Acoustic Emission Signals Interpreted by Friction Behavior and Machine Learning
by Taketo Nakai, Renguo Lu, Hiroshi Tani, Shinji Koganezawa and Jinqing Wang
Lubricants 2026, 14(2), 95; https://doi.org/10.3390/lubricants14020095 - 20 Feb 2026
Viewed by 815
Abstract
Condition monitoring of rolling bearings is essential for ensuring the reliability of mechanical systems operating under severe or insufficient lubrication conditions. This study proposes a fault diagnosis framework that integrates tribological interpretation of wear phenomena, acoustic emission (AE) signal analysis, and machine learning, [...] Read more.
Condition monitoring of rolling bearings is essential for ensuring the reliability of mechanical systems operating under severe or insufficient lubrication conditions. This study proposes a fault diagnosis framework that integrates tribological interpretation of wear phenomena, acoustic emission (AE) signal analysis, and machine learning, based on bearing life tests conducted under dry conditions as an accelerated wear environment to capture damage progression within a practical experimental time. Unlike conventional studies relying on artificially introduced defects, this work focuses on AE signals obtained from bearings in which damage initiates and progresses through actual wear processes. Life tests were conducted using deep groove ball bearings under two radial load conditions. The temporal evolution of the coefficient of friction, AE signals, and surface damage was analyzed. Although the coefficient of friction was the most sensitive indicator of wear progression, its direct measurement is impractical for in-service applications. Frequency-domain analysis revealed that AE counts per second and band-specific AE energy exhibit early changes consistent with the evolution of the friction coefficient. Using these physically interpretable AE features, a fully connected neural network was developed to classify bearing conditions into normal, early-stage damage, and damage progression. The proposed model achieved an average classification accuracy of approximately 85%, demonstrating the effectiveness of AE-based machine learning for bearing fault diagnosis under real wear progression conditions rather than artificial defect scenarios. Full article
(This article belongs to the Special Issue Advanced Methods for Wear Monitoring)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop