Assessment of alarm systems for mixture processes and intermittent faults

Abstract

Alarm systems are becoming increasingly important in ensuring high levels of safety, lowering pollutants, and reducing the financial losses associated with industrial processes. An industrial alarm system’s goal is to detect an unwanted deviation in the process variable (PV) as soon as possible. A thorough understanding of PV behaviour is required when constructing an alarm system. If distinct PV’s behaviour features are not taken into account, problems like flooding and chattering will occur. While the PV belongs to a vital industrial site, such as a nuclear plant, the severity of the damage will grow. Statistical techniques are used to design an alarm system in three stages: creating a model for PV based on statistical features, calculating performance assessment indices (FAR, MAR, and AAD) in the presence of design scenarios, and selecting the optimum design policy using optimization algorithms. The first two steps are the topic of this article. To do this, the Finite Mixture Model is utilized to model the behaviour of an Intermittent Fault (IF), which is a fault that alternates between faulty and non-faulty behaviour at discrete random intervals. Finally, different scenarios are explored, and the designed alarm system is tested over time for each of them, with Monte-Carlo simulation being used for further validation.