Addressing Uncertainty in Online Alarm Flood Classification Using Conformal Prediction

Abstract

Alarm flood management is essential for industrial process plant safety and efficiency. Online “alarm flood classification” (AFC) assigns an observed sequence of alarms to one (of many) alarm flood classes known from the past. Nevertheless, accurately differentiating between alarm flood classes as they evolve over time remains a challenge due to classification uncertainty. Furthermore, AFC usually assigns to just one alarm flood class, preventing the operator from recognizing that multiple classes should be considered given the limited information at hand. This paper proposes a novel approach by integrating “conformal prediction” with “early time series classification” to address these challenges, offering a probabilistic understanding of alarm flood classes to be considered, and enhancing decision-support for operators. Using a method-agnostic framework, our method can be used with existing AFC models, making them adaptable to evolving alarm dynamics. We apply our approach to five AFC methods from literature using a novel and openly accessible synthetic alarm dataset we designed. Our evaluation shows how our method can improve coverage of the existing models, particularly in the early stages of alarm floods, ensuring with high confidence that the true class is captured while preserving a reasonably small set of possible alarm flood classes.

Keywords

• Abnormal situations
• alarm management
• conformal prediction
• early time series classification
• industrial alarm floods
• industrial process diagnosis