Maintenance and Reliability Engineering: Latest Advances and Prospects

Dear Colleagues,

Recently, in the face of the fourth industrial revolution, topics related to maintenance engineering and system reliability are gaining particular importance. The ongoing digitalization, process automation and implementation of artificial intelligence in production pose new challenges for engineers, but also unique opportunities. Technologies such as the Internet of Things (IoT), analysis of large data sets (Big Data) or machine learning are revolutionizing the approach to monitoring and predicting failures, which allows for the optimization of maintenance processes and increased system reliability. These changes enable not only faster detection of faults, but also the implementation of preventive strategies that reduce the risk of costly downtime. However, the integration of these advanced technologies is also associated with numerous challenges: the need to adapt new tools, train staff and ensure an appropriate level of data security. In this context, effective maintenance management and ensuring reliability are becoming crucial not only for maintaining production continuity, but also for increasing market competitiveness. This Special Issue will present the latest achievements and perspectives in the field of maintenance and reliability engineering, which are gaining importance in the face of dynamic technological changes.

This Special Issue will present a collection of original research papers that deal with newly emerged maintenance and reliability problems of modern technical, antropo-technical and AI-technical systems in order to report innovative ideas and the most recent interesting applications. In collaboration with Applied Sciences (MDPI), up to 20 high-quality papers will be published in this carefully prepared Special Issue after a strict peer-review process. Submissions dealing with the following topics (but not limited to these) are very much welcomed:

1. The use of artificial intelligence and machine learning in failure prediction;
2. The Internet of Things (IoT) in monitoring and managing the condition of machines;
3. Lifecycle management of machines and devices in the era of Industry 4.0;
4. Data and system security in the context of maintenance in Industry 4.0;
5. Optimization of maintenance processes using advanced data analysis (Big Data); challenges and perspectives for training and technology adaptation in organizations;
6. Implementation of the ISO 55000 standard in asset management and maintenance;
7. The role of reliability in ensuring production continuity in the era of Industry 4.0.

Dr. Przemyslaw Drozyner
Dr. Malgorzata Jasiulewicz-Kaczmarek
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. Applied Sciences 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.

• maintenance engineering
• reliability engineering
• industrial reliability
• Industry 4.0
• smart maintenance
• machine learning in maintenance
• data analytics in reliability
• failure analysis
• maintenance optimization