Abstract
Abstract Industrial plan alarm systems form an essential part of the operator interfaces for automatically monitoring plant state deviations and for attracting plant operators' attention to changes that require their intervention. To design effective plant alarm systems, it is essential to evaluate their performances. In this presentation, I introduce two methods for evaluating plant alarm systems. The first is the data-based evaluation method that refers to operation and alarm event data in a plant. It uses event correlation analysis to detect statistical similarities among time series data of discrete alarm and operation events. Grouping correlated events on the basis of their degrees of similarity makes it easier to consider countermeasures for reducing frequently generated alarms than merely analyzing individual alarm and operation events. The second is a modelbased evaluation method, where an operator model is used to mimic humans' fault detection and identification behaviors. Analyzing simulated fault detection and identification tracks after all assumed malfunctions have occurred in a plant makes it possible to evaluate alarm system performance without human-subject-based experiments. Case study results demonstrated the usefulness of these methods for safe and stable plant operations in the chemical process industries.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
