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Optimal Alarm System Design and Implementation

An optimal alarm system can robustly predict a level-crossing event that is specified over a fixed prediction horizon. The code contained in this packages provides the tools necessary to design an optimal alarm system for a simple stationary linear dynamic system driven by white Gaussian noise.

The innovation described herein is an alarm system that can robustly predict a level-crossing event that is specified over a fixed prediction horizon. 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.

Software

This software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.3 or later.

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