Data Sciences and Deep Learning Algorithms for Monitoring Systems’ Health

With the development of IoT and “Industry 4.0”, industrial systems become more intelligent and complex, and monitoring systems’ health are very much important and meaningful to guarantee stability, security, and economy. This shift also concerns diverse research areas, e.g. detection of abnormal data, unhealthy status, fault diagnosis, adversarial attacks, robustness analysis, and so on. On the other hand, with the development of sensor systems, a large amount of data becomes easily available today, especially in the information era, bringing challenges to industrial systems’ condition monitoring.

A few methodologies and algorithms related to data mining, big data analysis, and deep learning were developed in this research area. However, there are still exist many challenging problems worth exploring and solving. Therefore, this Research Topic aims at selecting potential contributions to advanced theoretical findings, technologies, algorithms, and industrial applications in the monitoring of industrial systems’ health i.e. condition monitoring. Subtopics of interest include:

• Theory development on monitoring systems’ health.
-Machine learning
-Deep learning
-Data Mining
-Big data analytics
-Graph theory

• Engineering applications related to monitoring systems’ health.
-Data cleaning
-Abnormal data detection
-Anomaly detection
-Condition monitoring
-Fault diagnosis

• Anomaly detection in energy-related industrial systems.

Keywords: Data-driven modeling, data mining, big data, machine learning, deep learning, condition monitoring, anomaly detection, fault diagnosis.

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.