Project Overview

Overall Problem

The United States Coast Guard Rescue 21 communications system relies on speed and efficiency to save lives. This state of the art technology is incredibly dependable, however issues can arise within this complex communications network. The current system uses a ticket system to generate reports regarding system failures. System Engineers parse through these tickets manually to classify system failures.These issues may be systems related or occur due to hardware failures. Determining the location and frequency of these errors can be a slow and tedious process for system engineers.

The initial concept for this project was provided by our sponsor, in the form of a Capstone project proposal.

General Solution

EMELIA is the solution that will help system engineers respond to system failures. EMELIA stands for Event-driven MachinE Learning, Intelligent Assessor. System engineers will interact with EMELIA through a command line interface. EMELIA will utilize a neural network machine-learning model to solve the issue of manually classifying system failure reports. By using previous system failure reports to train the classifier, EMELIA will be capable of accurately classifying future reports. This process will streamline maintenance for system engineers and allow first responders to work more effectively.

To learn more about our project development, please visit our GitHub page.