Nuclear Power Plant Alarm Dataset

We introduce a novel dataset specifically designed for the evaluation of “alarm flood classification” (AFC) methods within process plants. The growing complexity of industrial systems and the heightened demands for operational safety and efficiency underscore the critical need for advanced diagnostic tools capable of handling alarm floods—situations where numerous alarms are triggered simultaneously. To bridge the gap identified in existing research regarding the availability of alarm datasets, we have developed a novel publicly available dataset derived from simulated data of a nuclear power plant. This dataset allows for a detailed analysis of alarm dynamics and enables a comprehensive evaluation of AFC methods. We used a systematic methodology for generating alarm data, which involves setting alarm thresholds based on the trade-off between “false alarm rates” (FAR) and “missed alarm rates” (MAR).