Predicting operators reliability for control room alarm management using knowledge-based Bayesian networks

doi:10.1016/j.ress.2026.112261

Predicting operators reliability for control room alarm management using knowledge-based Bayesian networks

2026-01-23

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DOI：10.1016/j.ress.2026.112261

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• Effective alarm management activities in control rooms are challenged by alarm fatigue, cognitive overload, and complex operator-system interactions under high-stress conditions. • A simple and generalisable approach to Human reliability analysis was adapted to address the scenario for alarm response in process industry so as to incorprorate data driven approaches with simple model of cognition for human performance prediction • A knowledge-based Bayesian Network (KBBN) was developed using established literature and a formaldehyde gas plant simulator experiment to model operator reliability in alarm management across the cognitive phases of perception, planning, and execution. • Explicit modelling of the time available for response and the time used in the different stages of alarm response was incorporated considering how manual and unsupervised discretizations of the time nodes used affect the overall model results.

Keyword:

Alarm management

Human reliability analysis

Bayesian networks

Operator performance

Control room operations

Predicting operators reliability for control room alarm management using knowledge-based Bayesian networks

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