Author ORCID Identifier

Document Type

Conference Paper


1.1 MATHEMATICS, Applied mathematics, Probability, Chemical engineering (plants, products)

Publication Details

ESREL 2022 conference

Published online at:

Proceedings of the 32nd European
Safety and Reliability Conference (ESREL 2022)
28th August – 1st September 2022, Dublin, Ireland


Modern industrial plants rely on alarm systems to ensure their safe and effective functioning. Alarms give the operator knowledge about the current state of the industrial plants. Trip alarms indicating a trip event indicate the shutdown of systems. Trip events in power plants can be costly and critical for the running of the operation.This paper demonstrates how trips events based on an alarm log from an offshore gas production can be reliably predicted using a Bayesian network. If a trip event is reliably predicted and the main cause of it is identified, it will allow the operator to prevent it. The Bayesian network model developed to predict trip events is purely data-driven and relies only on historic data from the alarms log from offshore gas production. We describe the method used to build the Bayesian network and the approach used to identify the most key alarm related to the Trip. We then assess the
performance of the Bayesian network on the alarm log of an offshore gas production. The preliminary performance results show significant potential in predicting trips and identifying key alarms. The model is developed to support decision-making of a human operator and increase the performance of the plant.



Collaborative intelligence for safety Critical systems (CISC)

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.