Perception, Control, and Optimization of Intelligent Transportation Systems

Chair:	Co-Chair:
Jiandong Wang  Shandong University of Science and Technology, China	Wenkai Hu China University of Geosciences, Wuhan, China

Key Words: Industrial Alarm System, Alarm & Event Data, Alarm Flood, Early Fault Detection, Predictive Maintenance

Information: Industrial alarm systems are critical for safeguarding operational safety in complex facilities such as power generation and chemical processing. However, high false alarm rates and alarm floods severely degrade their effectiveness, potentially aggravating equipment faults and even leading to catastrophic incidents. In recent years, research on industrial alarm monitoring has gradually evolved from traditional rule-based paradigms toward the exploration and application of cutting-edge technologies, including deep learning, big data analytics, and large foundation models, aiming to build intelligent alarm monitoring frameworks. This special session focuses on frontier advances in this domain, with particular emphasis on alarm system design, alarm flood analysis, alarm root cause analysis, and alarm response decision-making, while also encompassing related topics such as early fault detection and predictive maintenance.

Topics of interest include but are not limited to: 

• Design and performance evaluation of univariate or multivariate alarm systems
• Pattern mining, similarity analysis, prediction, and classification of alarm floods
• Knowledge-based or data-driven causality modelling and root cause analysis
• Deep learning and pretrained models for process monitoring and fault diagnosis
• Real-time process monitoring, anomaly detection, and predictive maintenance

Submission Deadline: June 30, 2026 (the second round)