Managing the unexpected: Operator behavioural data and its value in predicting correct alarm responses
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909737101033472 |
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| author | Amazu, Chidera W. Mietkiewicz, Joseph Abbas, Ammar N. Baldissone, Gabriele Fissore, Davide Demichela, Micaela Madsen, Anders L. Leva, Maria Chiara |
| author_facet | Amazu, Chidera W. Mietkiewicz, Joseph Abbas, Ammar N. Baldissone, Gabriele Fissore, Davide Demichela, Micaela Madsen, Anders L. Leva, Maria Chiara |
| contents | Data from psychophysiological measures can offer new insight into control room operators' behaviour, cognition, and mental workload status. This can be particularly helpful when combined with appraisal of capacity to respond to possible critical plant conditions (i.e. critical alarms response scenarios). However, wearable physiological measurement tools such as eye tracking and EEG caps can be perceived as intrusive and not suitable for usage in daily operations. Therefore, this article examines the potential of using real-time data from process and operator-system interactions during abnormal scenarios that can be recorded and retrieved from the distributed control system's historian or process log, and their capacity to provide insight into operator behavior and predict their response outcomes, without intruding on daily tasks. Data for this study were obtained from a design of experiment using a formaldehyde production plant simulator and four human-in-the-loop experimental support configurations. A comparison between the different configurations in terms of both behaviour and performance is presented in this paper. A step-wise logistic regression and a Bayesian network models were used to achieve this objective. The results identified some predictive metrics and the paper discuss their value as precursor or predictor of overall system performance in alarm response scenarios. Knowledge of relevant and predictive behavioural metrics accessible in real time can better equip decision-makers to predict outcomes and provide timely support measures for operators. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_10917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Managing the unexpected: Operator behavioural data and its value in predicting correct alarm responses Amazu, Chidera W. Mietkiewicz, Joseph Abbas, Ammar N. Baldissone, Gabriele Fissore, Davide Demichela, Micaela Madsen, Anders L. Leva, Maria Chiara Human-Computer Interaction Artificial Intelligence Data from psychophysiological measures can offer new insight into control room operators' behaviour, cognition, and mental workload status. This can be particularly helpful when combined with appraisal of capacity to respond to possible critical plant conditions (i.e. critical alarms response scenarios). However, wearable physiological measurement tools such as eye tracking and EEG caps can be perceived as intrusive and not suitable for usage in daily operations. Therefore, this article examines the potential of using real-time data from process and operator-system interactions during abnormal scenarios that can be recorded and retrieved from the distributed control system's historian or process log, and their capacity to provide insight into operator behavior and predict their response outcomes, without intruding on daily tasks. Data for this study were obtained from a design of experiment using a formaldehyde production plant simulator and four human-in-the-loop experimental support configurations. A comparison between the different configurations in terms of both behaviour and performance is presented in this paper. A step-wise logistic regression and a Bayesian network models were used to achieve this objective. The results identified some predictive metrics and the paper discuss their value as precursor or predictor of overall system performance in alarm response scenarios. Knowledge of relevant and predictive behavioural metrics accessible in real time can better equip decision-makers to predict outcomes and provide timely support measures for operators. |
| title | Managing the unexpected: Operator behavioural data and its value in predicting correct alarm responses |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2508.10917 |