https://orcid.org/0000-0002-9571-9311
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ABSTRACT
This study proposes an adaptive alarm interface, which is supposed to have the good features of both alarm–tile interfaces that are typical in nuclear power plants (NPPs), and alarm–bar interfaces that use changing bars to indicate the current condition of an NPP and its abnormalities. Depending on the situation, the alarm management system will decide whether the alarm–tile or the alarm–bar interface should be presented to operators. To validate the usability of the adaptive alarm interface, it was compared against both the alarm–tile and alarm–bar interfaces through laboratory experiments with 45 participants. The results demonstrated that the adaptive alarm interface not only allowed the understanding of parameter trending, which was a distinguishing feature of the alarm–bar interface, but also improved the alarm detection, which was one advantage of the alarm–tile interface. However, the situation awareness level of the adaptive alarm interface was insufficient compared to the alarm–tile and bar interfaces possibly due to distraction from interface swapping.
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Notes on contributors
Samsoo Seo
Samsoo Seo received his master degree in Management Science and Engineering from Tsinghua University, China in 2017. His research interests include interface design, human error, and alarm system in nuclear power plants.
Xiaojun Wu
Xiaojun Wu is now a lecturer in Tsinghua University, China. Her Ph.D. in Management Science and Engineering was awarded by Tsinghua University in 2016, with a doctorial period partially spent at Ohio State University. Her research interests include interface design, alarm systems in nuclear power plants, and cognitive engineering.
Zhizhong Li
Zhizhong Li is a full professor at the Department of Industrial Engineering, Tsinghua University. He received his Ph.D. degree in Manufacturing Engineering and Automation from Tsinghua University in 1999. His current research areas include interface design, human error, system safety, and other ergonomics issues associated with complex industrial systems.
Hyejin Park
Hyejin Park received her master degree in Management Science and Engineering from Tsinghua University, China in 2018. Her research interests include IoT system, system design, and human computer interaction.