Challenges in Learning Causal Models of Alarms in Industrial Plants

The automation and digitization creates a multitude of warnings, messages and alarms for plant operators. These so-called alarm floods have become an increasing issue in the industry regarding safety and plant downtime. A novel approach to reduce the overwhelming information presented to an operator...

Full description

Saved in:
Bibliographic Details
Main Authors: Wunderlich, Paul, Niggemann, Oliver
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Belts
Correlation
Industrial plants
Industries
Production systems
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The automation and digitization creates a multitude of warnings, messages and alarms for plant operators. These so-called alarm floods have become an increasing issue in the industry regarding safety and plant downtime. A novel approach to reduce the overwhelming information presented to an operator during an alarm flood is based on causal models. The task of learning a feasible causal model especially of alarms is difficult. Therefore, we analyse the challenges of learning a causal model based on an industrial plant. Furthermore, we investigate the influence of different disturbances on this process and provide first solutions to address these challenges in the future.
ISSN:2378-363X
DOI:10.1109/INDIN.2018.8471985