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

Despite comprehensive standards for industrial alarm management and existing human reliability studies on operator behavior, quantitative operator-centered reliability assessment within alarm management activities remains limited. This paper presents a Bayesian network framework that integrates alarm response task decomposition, cognitive modeling, and contextual factors to assess alarm management reliability across perception, planning, and execution phases, capturing both task effectiveness and temporal constraints. The model combines Performance Shaping Factors with phase-specific cognitive mechanisms using an object-oriented Bayesian network implementation in HUGIN Software. The model was built within the context of a simulated experiment to enable future data validation. Model parameters were defined through literature and, when unavailable, through expert assumptions. Value of information and sensitivity analyses reveal that performance is primarily driven by operator experience and task complexity, factors parameterized through established literature. In contrast, support system effects show minimal impact, possibly reflecting the experiment’s limited scope. Failure patterns differ across experience levels: novices most likely fail through timeout, while experienced operators typically fail through incorrect actions. Sensitivity analysis highlighted that the perception phase is most sensitive to parameter changes. This framework demonstrates how established HRA principles can be extended to alarm management contexts, establishing a structured approach for evaluating operator-alarm interaction pending empirical validation.