The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary/categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. PyMKAD is a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. This code address an anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance.

The objective of this project is to automate the analysis of flight safety incidents in a way that combines both analysis of discrete and continuous parameters.

This repository contains the following files in its top level directory:

• PythonCode
  The source code of the repository includes: preprocessing modules, the main mkad code, and a post processing visualization tool. The code is uses a command line interface and a json file for configuring.

• documentation
  Documents describing how to configure and run the program, as well as how to interpret the results.

• MKAD NOSA 2019.pdf
  Licensing for MKAD

NASA Point of contact: Nikunj Oza nikunj.c.oza@nasa.gov, Data Science Group Lead.

For questions regarding the research and development of the algorithm, please contact Bryan Matthews bryan.l.matthews@nasa.gov, Senior Research Engineer.

Notices:

Copyright © 2019 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved.

Disclaimers

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