New Insights in Complex, Technical Systems: Operation and Data-Driven Maintenance

Dear Colleagues,

Welcome to this Special Issue of Electronics, where we present 'New Insights in Complex, Technical Systems: Operation and Data-Driven Maintenance'. In today's technologically advanced era, the synergy between advanced operational strategies and data-driven maintenance has become pivotal. Using the power of data analytics, artificial intelligence, and machine learning, this Special Issue explores innovative approaches to optimize the operation and maintenance of complex technical systems.

Data-driven maintenance, a paradigm that utilizes sophisticated data analytics to enhance the reliability and efficiency of technical systems, stands at the forefront of this exploration. By intelligently processing available datasets, researchers and practitioners can uncover hidden patterns, predict potential failures, and proactively address maintenance needs. This proactive approach not only minimizes downtime but also maximizes the operational potential of diverse technical subsystems. The integration of other Industry 4.0 technologies, such as digital twins or augmented reality, further enhances visualization and simulation capabilities, providing invaluable insights for maintenance professionals.

Topics of interest include (but are not limited to) the following:

• Predictive analytics for power distribution networks;
• Proactive maintenance strategies for renewable energy systems;
• Intelligent fault diagnosis in complex manufacturing processes;
• Smart analytics for water distribution networks;
• Data-driven maintenance of bearing subsystems;
• Real-time monitoring and smart diagnostics of control systems;
• Application of digital twins in predictive maintenance;
• Augmented reality applications for interactive maintenance experiences;
• Machine learning for anomaly detection in technical systems.

This Special Issue serves as a platform for researchers and experts to share their insights, innovations, and practical applications in the realm of data-driven maintenance. We invite contributions that offers fresh perspectives and solutions to the challenges faced in the operation and maintenance of complex technical systems.

We look forward to your valuable contributions, which will enrich our understanding of data-driven maintenance and its impact on complex technical systems.

Dr. Davor Kolar
Prof. Dr. Dragutin Lisjak
Prof. Dr. Michał Pająk
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. Electronics 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.

• artificial intelligence
• machine learning
• complex technical systems