Dynamic analysis of evolving industrial alarm floods using an adaptive causal directed graph
Publication date
2024-02-19
Document type
Konferenzbeitrag
Author
Kunze, Franz C.
Organisational unit
Conference
2nd Industrial Electronics Society Annual On-Line Conference (ONCON 2023) ; Online ; December 8–10, 2023
Project
Causal Alarm pattern analysis by the Integration of Technical Information from Engineering Documents (CausAlITI)
Publisher
IEEE
Book title
2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON)
Part of the university bibliography
✅
Language
English
Abstract
The increasing complexity of highly automated industrial plants necessitates advanced tools for managing alarm floods and enhancing situational awareness for human operators. While Causal Directed Graphs have proven effective in these roles due to their low computational and informational requirements, their application has largely been confined to static scenarios. This is a limitation given the dynamic nature of many abnormal situations in industrial settings. To address this gap, this contribution introduces a novel extension to Causal Directed Graphs that incorporates real-time actuator states. Our approach is engineered to adapt to changing causal relationships, thereby enabling the identification of not only the root cause but also other causally important alarms in dynamic alarm flood scenarios. The effectiveness of our method is validated on the well-known "Tennessee-Eastman" process using multiple test scenarios from an openly accessible alarm management dataset.
Version
Published version
Access right on openHSU
Metadata only access