AI-Based Machine Condition Monitoring and Maintenance
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 2361
Special Issue Editors
Interests: condition-based maintenance; hidden Markov models; deep neural networks; industrial sensors; data-driven models; digital twins
2. Research Centre for Asset Management and Systems Engineering (RCM2+), Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: artificial intelligence; robotics; automation; maintenance; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: data and analytics; lubrication; condition monitoring; machinery management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the transformative role of Artificial Intelligence in machine condition monitoring, diagnostics, prognostics and maintenance decision-making. As industrial systems become increasingly complex and data-rich, AI-driven approaches offer unprecedented capabilities to detect anomalies, predict failures, and optimize maintenance strategies. We invite high-quality contributions that explore advanced algorithms, intelligent sensor networks, virtual sensing technologies, digital twins, and data-driven prognostics approaches for improving asset reliability and operational efficiency.
Topics of interest include (but are not limited to) the following: supervised and unsupervised learning models; machine learning methods; deep neural networks (DNNs); reinforcement learning for autonomous maintenance decision-making; adaptable data-driven models; edge and cloud AI architectures; calibration monitoring and self-calibrating sensors; agent-based AI systems and multi-agent maintenance management; generative AI; virtual sensing; anomaly detection and predictive modelling; multimodal data fusion; and explainable AI for industrial systems. We also welcome studies combining AI with metrology, data quality, and sensor reliability to enhance maintenance precision.
This Special Issue aims to bring together innovative research, practical applications, and industrial case studies that showcase how AI is reshaping the future of maintenance. By integrating intelligent systems with robust data infrastructure, this Issue seeks to highlight new pathways for building resilient, efficient, and autonomous industrial operations.
Dr. Alexandre Martins
Dr. Inácio Fonseca
Prof. Dr. Honor Powrie
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-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.
Keywords
- AI-based condition monitoring
- predictive maintenance
- intelligent sensor systems
- AI-driven metrology
- digital twins
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