An optimal alarm system can robustly predict a level-crossing event that is specified over a fixed prediction horizon. The code contained in this package provides the tools necessary to design an optimal alarm system for a simple stationary linear dynamic system driven by white Gaussian noise.
Given a specific modeling paradigm that can be learned using appropriate data-driven or machine learning techniques, an optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability. When the modeling paradigm is a simple stationary linear dynamic system driven by white Gaussian noise, it is easy to use the well-known Kalman filter to enable value prediction of future process values. During implementation, these value predictions can subsequently be used to enable the prediction of associated level-crossing events that may occur in the future, using a parameter that was selected to achieve robustly optimal performance during the alarm system design stage.