Diagnosing Faults with Machine Learning
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 705
Special Issue Editors
Interests: damage detection; gear design; gear manufacturing; gear testing; vibration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In today’s landscape, systems are evolving to become more intricate thanks in part to groundbreaking design concepts and advancements in technologies such as sensing, materials, communication, and overall system integrity. Fault detection and diagnosis play a vital role in fostering a healthy state of awareness, enabling accurate predictions, and helping to prevent potential faults in these systems.
The processes of fault detection and diagnosis in modern systems present certain challenges. These challenges arise from the intricate nature of the subject, which involves careful consideration of performance analysis, sensor placement and communication, and data collection and analysis, as well as the evaluation of benefits and informed decision-making. Additionally, a comprehensive understanding of the operational states of complex systems and their interactions with a range of environmental factors—some of which may be variable or unpredictable—is essential for effective implementation.
Machine learning is a valuable approach for addressing detection and diagnosis challenges in contemporary systems. This advanced tool could complement and enhance traditional model-based methodologies. This Special Issue focuses on the integration of advanced machine learning techniques for effective fault detection and diagnosis in industrial systems. It emphasizes the critical role of fault detection, which can significantly enhance system reliability and efficiency, thereby reducing maintenance costs and downtime. Furthermore, this Special Issue explores predictive maintenance strategies that leverage machine learning to forecast equipment failures, allowing for timely interventions.
Therefore, we welcome contributions on a range of topics, including computational engineering, sensor configuration design solutions, fault detection, fault diagnosis, fault prognosis, and condition-based and predictive maintenance strategies. Original research and review articles related, but not limited, to the following topics are welcomed:
- New design concepts of health monitoring platforms;
- Modeling and analyzing of structures health state;
- Sensing and monitoring advancement toward structures;
- Computation and simulation tools;
- Vibration and its prevention;
- Digital twins;
- Advanced methodologies on machine learning;
- Machine learning-based fault detection;
- Machine learning-based fault diagnosis;
- Machine learning-based fault prognosis.
Dr. Zoltan-Iosif Korka
Dr. Attila Gerőcs
Guest Editors
Manuscript Submission Information
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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. Computation 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.
Keywords
- machine learning
- fault detection
- fault diagnosis
- fault prognosis
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