Special Issue "Sustainable and Trustworthy Operation and Maintenance of Railway Systems"
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 18334
Interests: industrial AI and eMaintenance
Interests: RAMS; operation and maintenance engineering
Interests: condition monitoring; industrial AI
With the advent of the 4th Industrial revolution and boarding of the digital train by the railway sector, the implementation of artificial intelligence (AI)-based solutions in railway business has become an important part of strategic thinking of senior railway managers. However, developing and implementing AI-based solutions for enhanced analytics in railway contexts is challenging and requires a good domain understanding and insight in a range of disciplines, such as computer science, data science, system engineering, software engineering, control engineering, statistics, and mathematics.
Today, the railway sector is struggling to identify appropriate approaches when developing AI-based solutions, and at the same time avoiding hype-based implementation to ensure effective and efficient use of resources. However, to strengthen the railway industry’s capability to develop AI-based solutions and boost the implementation of AI tools, we believe that there is a need for fundamental and applied research to study, explore, investigate, and develop frameworks, approaches, technologies, and methodologies that enable operational excellence in the railway though improved fact-based decision making using enhanced analytics empowered by digitalization and AI technologies.
This Special Issue of Sustainability is seeking research findings that focus on the utilization of digitalization and AI technologies that enable sustainable asset management, operation, and maintenance of railway systems, including railway infrastructure and rolling stocks.
The issue welcomes submissions that enable enhanced decision making in managing railway assets with special reference to the operation and maintenance of railways through the use of analytics (i.e., descriptive, diagnostics, prognostics, and prescriptive) based on data and/or physical constraints and other digitalization and AI technologies. The scope of the Special Issue includes a wide range of topics related to the operation and maintenance of railway systems, such as asset management, fleet management, RAMS, maintenance analytics, cybersecurity, data-drive models, predictive maintenance, condition monitoring, machine learning, deep-learning, eMaintenance, digitalization, artificial intelligence, etc.
Prof. Dr. Ramin Karim
Prof. Dr. Uday Kumar
Prof. Dr. Diego Galar
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. Sustainability is an international peer-reviewed open access semimonthly 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.
- industrial AI
- asset management
- condition monitoring
- asset management
- fleet management