Diagnosing Faults with Machine Learning

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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• machine learning
• fault detection
• fault diagnosis
• fault prognosis