Use of Digital Twin Technology for Intelligent Mechanical Operations and 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 May 2026 | Viewed by 45
Special Issue Editors
Interests: weak supervision fault diagnosis of key components of high-speed trains; tool wear monitoring and condition assessment
Special Issues, Collections and Topics in MDPI journals
Interests: equipment using digital twins; manufacturing services
Interests: industrial large models; digital twins; transfer learning; deep learning; graph neural networks; information fusion; intelligent fault diagnosis and prognosis
Interests: equipment transportation; intelligent operations and maintenance; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Traditional intelligent maintenance methods adopt a strategy of training a model once and using this model indefinitely. However, it is difficult to obtain comprehensive information on operating conditions and status; moreover, it is difficult to adapt to changes in the distribution of monitoring data caused by mechanical equipment over time. Digital twin technology can leverage IoT, sensors, and 3D modeling to create a virtual mirror image of physical entities, synchronizing real-time operational status, environmental parameters, and historical data from the physical world. This enables real-time sensing of equipment status, fault prediction, autonomous decision-making, and optimized maintenance.
The integration of Digital Twins (DTs) into mechanical Operations and Maintenance (O&M) is revolutionizing asset management, predictive maintenance, and operational efficiency. This Special Issue addresses the urgent industry need to harness AI-driven technologies—such as machine learning, computer vision, and Digital Twins—to transition from reactive approaches to intelligent, proactive O&M strategies. We invite authors to submit papers to this Special Issue that explore cutting-edge methodologies to minimize downtime, optimize lifecycle costs, and enhance safety in industrial and infrastructure systems.
Dr. Kai Zhang
Dr. Qing Zheng
Dr. Zihao Lei
Dr. Zhenzhen Jin
Dr. Xiaoxia Yu
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
- operations and maintenance
- artificial intelligence
- deep learning
- fault detection
- fault diagnosis
- remaining-useful-life prediction
- digital twin
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