Digital Twins and Intelligent Systems for Condition-Based Industrial Maintenance
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 93
Special Issue Editors
Interests: predictive modelling; smart maintenance planning; life cycle cost optimisation; digital twins; sustainable manufacturing
Interests: Industry 4.0; smart manufacturing; sustainable manufacturing; life cycle engineering; circular economy
Special Issues, Collections and Topics in MDPI journals
Interests: condition monitoring; prognostics and predictive maintenance; intelligent manufacturing; Industry 4.0; digital twins
Special Issues, Collections and Topics in MDPI journals
Interests: Industry 5.0; digital remanufacturing; smart manufacturing; advanced manufacturing processes
Interests: Industry 4.0; digital manufacturing; digital twins; augmented reality; cyber–physical production system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in Industry 4.0 have paved the way for digital twins and intelligent systems to transform how machines are monitored, maintained, and optimised throughout their operational life. By integrating real-time sensor data, AI-driven diagnostics, and virtual replicas of physical assets, this Special Issue aims to showcase cutting-edge solutions that enable condition-based maintenance (CBM) for enhancing reliability, efficiency, and cost-effectiveness in industrial settings.
We invite original research papers and comprehensive reviews that explore topics including, but not limited to, the following:
- Design and implementation of digital twin frameworks for real-time machine monitoring.
- AI-powered fault detection, diagnostics, and prognostics.
- Integration of IoT and cloud infrastructures within CBM architectures.
- Case studies demonstrating digital twin applications in industrial maintenance.
- Optimisation strategies that bridge virtual and physical systems for intelligent maintenance decision-making.
Submissions may include theoretical models, data-driven techniques, hybrid simulations, real-world implementations, or comparative studies in sectors where machinery and intelligent maintenance systems play a critical role, such as manufacturing, energy production, and automated industrial environments.
This Special Issue offers a platform for researchers and practitioners to share breakthroughs, methodologies, and practical insights in deploying digital twins and intelligent CBM systems. Our goal is to support cross-disciplinary dialogue and accelerate the development of next-generation smart maintenance strategies for industrial machinery.
Dr. Nasser Amaitik
Prof. Dr. Yuchun Xu
Prof. Dr. Jihong Yan
Dr. Muftooh Siddiqi
Dr. Chao Liu
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. Machines 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 2400 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
- digital twins
- condition-based maintenance (CBM)
- predictive maintenance
- machine learning and AI in maintenance
- fault detection and diagnostics
- remaining useful life (RUL) estimation
- life cycle cost modelling
- sustainable maintenance strategies
- intelligent decision support
- prognostics and health management (PHM)
- data-driven maintenance systems
- maintenance planning optimisation
- Industrial Internet of Things (IIoT)
- smart manufacturing
- cyber–physical systems
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