Digital Technologies Research and Applications

Topical Collection on "Advances in Digital Twins and Intelligent Maintenance for Industrial Equipment in Industry 4.0"

A topical collection of Digital Technologies Research and Applications (DTRA) (E-ISSN: 2754-5687).
Deadline for manuscript submissions: 1 May 2025

Collection Editor: 

Dr. Xiaoli Zhao
School of Mechanical Engineering, Nanjing University of Science and Technology, China
Interests: Artificial Intelligence; Signal Processing; Physical Information Systems

 

Dr. Jipu Li
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University,China
Interests: Industrial Intelligence; Industrial Big Data; Digital Twins; Prognostics and Health Management

 

Dr. Jichao Zhuang
School of Mechanical Engineering, Southeast University, Nanjing, China
Interests: Intelligent Operation and Maintenance; Industrial Health Management;Signal Processing

 

Topical Collection Information:

Dear Colleagues,

Digital twins and intelligent maintenance play a transformative role in the application of industrial equipment within the context of Industry 4.0. By creating a virtual replica of physical assets, digital twins enable real-time monitoring, simulation, and analysis of industrial equipment. This comprehensive visibility allows for enhanced predictive maintenance, minimizing downtime and extending the lifespan of machinery. Intelligent maintenance systems leverage data analytics, machine learning, and IoT technologies to predict equipment failures before they occur, optimize maintenance schedules, and ensure the efficient operation of industrial assets. In an Industry 4.0 environment, where automation and smart manufacturing are key, digital twins and intelligent maintenance systems play a crucial role in achieving seamless integration and coordination of industrial processes. They facilitate real-time decision-making, enable adaptive control strategies, and support the implementation of autonomous maintenance operations. This topic collection aims to explore the latest innovations, research findings, and practical applications of digital twins and intelligent maintenance technologies in the context of Industry 4.0.

We invite submissions that explore a wide range of themes related to perfect sequences in digital technologies, including but not limited to:

  • Development and implementation of digital twins for industrial equipment
  • Intelligent maintenance strategies and predictive maintenance techniques
  • Data analytics and machine learning applications in digital twins and maintenance
  • Real-time monitoring and diagnostics using digital twin technology
  • Case studies and practical applications of digital twins in industry
  • Integration of IoT and sensor networks with digital twins
  • Challenges and future directions in digital twins and intelligent maintenance

We welcome original research articles, reviews, case studies, and theoretical contributions that aid to the understanding and practical implementation of fault diagnosis in industrial equipment.

Dr. Xiaoli Zhao
Dr. Jipu Li
Dr. Jichao Zhuang
Collection Editor

Keywords:

  • Deep learning
  • Transfer learning
  • Artificial Intelligence
  • IoT
  • PHM
  • Industrial equipment
  • Incremental learning
  • Industry 4.0

Manuscript Submission Information:

Please visit the Submissions Guidelines page before submitting a manuscript. Submitted papers should be well formatted and use good English. Manuscripts should be submitted online through the online manuscript submission and editorial system. Additionally, please include a cover letter specifying that the manuscript is intended for the Topical Collection "Advances in Digital Twins and Intelligent Maintenance for Industrial Equipment in Industry 4.0" when submitting it online. Manuscripts can be submitted until the submission deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the Topical Collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract can be sent to the Editorial Office dtra@ukscip.com for announcement on this website.

The Article Processing Charge (APC) for publication in this open access journal is 300 USD. Authors who are unable to cover this cost or those who are invited to submit papers may be eligible for discounts or waivers.

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 double-blind peer-review process.

Published Papers:

This Topical Collection is now open for submission.