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: 30 May 2025 | Viewed by 109
Special Issue Editors
Interests: tribology; digitalization; sensors; data driven; machine learning; artificial intelligence; prediction; remaining useful life
Interests: data and analytics; lubrication; condition monitoring; machinery management
Special Issues, Collections and Topics in MDPI journals
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
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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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