This proceedings volume will document the state-of-the-art research, case studies, and technological advancements in Industrial Artificial Intelligence and intelligent asset management. The content will be relevant to industrial and academic audiences, covering topics such as eMaintenance, Industry 5.0, predictive analytics, intelligent asset management, cybersecurity, and advanced digitalisation solutions. By presenting both academic and practical insights, the volume aims to bridge the gap between theory and application in managing industrial assets. The primary readership includes professionals, researchers, and students interested in the applications of AI for optimizing industrial operations and maintenance.

This proceedings volume will document the state-of-the-art research, case studies, and technological advancements in Industrial Artificial Intelligence and intelligent asset management. The content will be relevant to industrial and academic audiences, covering topics such as eMaintenance, Industry 5.0, predictive analytics, intelligent asset management, cybersecurity, and advanced digitalisation solutions. By presenting both academic and practical insights, the volume aims to bridge the gap between theory and application in managing industrial assets. The primary readership includes professionals, researchers, and students interested in the applications of AI for optimizing industrial operations and maintenance.

This proceedings volume will document the state-of-the-art research, case studies, and technological advancements in Industrial Artificial Intelligence and intelligent asset management. The content will be relevant to industrial and academic audiences, covering topics such as eMaintenance, Industry 5.0, predictive analytics, intelligent asset management, cybersecurity, and advanced digitalisation solutions. By presenting both academic and practical insights, the volume aims to bridge the gap between theory and application in managing industrial assets. The primary readership includes professionals, researchers, and students interested in the applications of AI for optimizing industrial operations and maintenance.