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Lubricants
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Future of Digital Tribology: Prediction of Tribological Performance Using Sensors,...
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Future of Digital Tribology: Prediction of Tribological Performance Using Sensors, Signal Processing and Machine Learning

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 561

Special Issue Editors

Prof. Dr. Ling Wang

E-Mail Website
Guest Editor
Surface Engineering and Tribology Group, School of Engineering Sciences, University of Southampton, Southampton SO17 1BJ, UK
Interests: tribology; digitalization; sensors; data driven; machine learning; artificial intelligence; prediction; remaining useful life
Prof. Dr. Honor Powrie

E-Mail Website
Guest Editor
Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
Interests: data and analytics; lubrication; condition monitoring; machinery management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kun Yang

E-Mail Website
Guest Editor
Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
Interests: tribology; digitalization; sensors; data driven; machine learning; artificial intelligence; prediction; remaining useful life

Special Issue Information

Dear Colleagues,

Over the past two decades, increasing digitalization has transformed global technology and is rapidly impacting every corner of industry and society as a whole. Tribology, the fundamental building block of everything that moves, is transitioning from conventional rub testing and low quantity, empirical models to robust testing, supported by high-throughput sensing and data-driven machine learning for accurate and timely predictions. Digital tribology is key to helping achieve the ambitious drive to carbon net zero by 2050, from increasing machine efficiency to enabling new technologies which positively impact the design, design practice and operation of moving parts. 

This Special Issue focuses on state-of-the-art modelling and the phenomena associated with friction, wear, lubrication and machine condition prediction, as applied to engineered and natural tribological systems. Emphasis will be placed on data-driven models, especially where novel sensors, signal processing and/or machine learning methods are being developed. The Special Issue covers current research and development in digital tribology and will showcase pioneering methods, as well as identify the challenges and opportunities for the future of digital tribology, especially those arising from global societal and technological demands. 

Prof. Dr. Ling Wang
Prof. Dr. Honor Powrie
Prof. Dr. Kun Yang
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

  • tribology
  • digitalization
  • sensors
  • data driven
  • machine learning
  • artificial intelligence
  • prediction
  • remaining useful life

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Published Papers (1 paper)

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Research

22 pages, 11458 KiB  
Article
Convolutional Neural Networks—Long Short-Term Memory—Attention: A Novel Model for Wear State Prediction Based on Oil Monitoring Data
by Ying Du, Hui Wei, Tao Shao, Shishuai Chen, Jianlei Wang, Chunguo Zhou and Yanchao Zhang
Lubricants 2025, 13(7), 306; https://doi.org/10.3390/lubricants13070306 - 15 Jul 2025
Viewed by 140
Abstract
Wear state prediction based on oil monitoring technology enables the early identification of potential wear and failure risks of friction pairs, facilitating optimized equipment maintenance and extended service life. However, the complexity of lubricating oil monitoring data often poses challenges in extracting discriminative [...] Read more.
Wear state prediction based on oil monitoring technology enables the early identification of potential wear and failure risks of friction pairs, facilitating optimized equipment maintenance and extended service life. However, the complexity of lubricating oil monitoring data often poses challenges in extracting discriminative features, limiting the accuracy of wear state prediction. To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. The proposed architecture implements a three-stage computational pipeline. Initially, the CNN performs hierarchical extraction of localized patterns from multi-sensor tribological signals. Subsequently, the self-attention mechanism conducts adaptive recalibration of feature saliency, prioritizing diagnostically critical feature channels. Ultimately, bidirectional LSTM establishes cross-cyclic temporal dependencies, enabling cascaded fully connected layers with Gaussian activation to generate probabilistic wear state estimations. Experimental results demonstrate that the proposed model not only achieves superior predictive accuracy but also exhibits robust stability, offering a reliable solution for condition monitoring and predictive maintenance in industrial applications. Full article
(This article belongs to the Special Issue Future of Digital Tribology: Prediction of Tribological Performance Using Sensors, Signal Processing and Machine Learning)
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