A Novel Process Plant Alarm Dataset and Methodology for Alarm Data Generation

This paper introduces a novel alarm dataset specifically designed for the evaluation of alarm analysis methods within process plants. The complexity of industrial systems and the 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 alarm analysis methods. We present 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). The dataset is employed to evaluate three existing “alarm flood classification” (AFC) methods, showcasing the practical implications and benefits of our approach. We demonstrate that AFC methods exhibit varying performances based on the implemented alarm thresholds and the quantity of available alarm data.