Digital Twins, Machine Learning and Intelligent Algorithms for Smart Manufacturing and Industrial Predictive Maintenance

Dear Colleagues,

Increasing demands on industrial systems and the need for reliable, efficient, and sustainable production drive the need to advance computational methods for monitoring, control, optimization, and maintenance. Digital twins, machine learning, and intelligent algorithms offer approaches for analysing system behaviour, detecting faults, predicting degradation, and supporting informed decision-making throughout the operational lifecycle. This Special Issue aims to gather original research and articles addressing the development, analysis, and application of algorithms for intelligent manufacturing and industrial predictive maintenance. Particular attention will be paid to methods that integrate physical systems, mathematical models, and operational data to enhance equipment reliability, production performance, and maintenance planning. The scope encompasses digital twin modelling and updating; machine learning and deep learning for condition monitoring and fault diagnosis; signal processing and diagnostic feature extraction; multi-sensor data fusion; anomaly detection; remaining useful life prediction; and intelligent optimization and control. Contributions regarding hybrid approaches—combining physics-based knowledge with data-driven methods—are also welcome, as is research addressing uncertainty, interpretability, computational efficiency, and robustness under varying operating conditions. Suitable applications include industrial electric drives, electromechanical systems, rotating machinery, CNC machines, robotic systems, and manufacturing processes. Research utilizing electrical, vibration, acoustic, thermal, imaging, or other operational data is encouraged. Contributions may address individual machines or interconnected manufacturing systems, including algorithms for process optimization, maintenance planning, and decision support. Experimentally validated methods, industrial case studies, comparative algorithm evaluations, and reproducible research using clearly documented datasets and evaluation protocols are particularly relevant. Topics such as generalization across varying operating conditions, limited labelled data, real-time implementation, and integration into existing industrial infrastructure are of special interest. By bridging methodological advancements and practical applications, this Special Issue aims to foster the development of robust and transferable algorithms that enable smarter manufacturing, timely fault detection, and effective predictive maintenance in industrial environments. Keywords: digital twins; machine learning; intelligent algorithms; smart manufacturing; predictive maintenance; condition monitoring; fault diagnosis; multisensor data fusion; industrial electric drives; process optimization, robotics

Dr. Marin Zhilevski
Prof. Dr. Vladimir D. Hristov
Prof. Dr. Massimiliano Caramia
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1800 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.