Firm:

          

Metron, Inc.

          

   

Address:

          

1818 Library Street, Reston, VA 20190

          

   

Phone:

          

(703) 787-8700                                                                                                                                                                                

          

   

Name:

          

Sean Daugherty

          

   

E-mail:

          

daugherty@metsci.com

          

   

Address:

          

1818 Library Street Ste. 600, 20190 - 5602

          

   

Phone:

          

(703) 787-8700                                                                                                                                                                                

          

   

Name:

          

Seth Blackwell

          

   

E-mail:

          

blackwell@metsci.com

          

   

Address:

          

1818 Library Street, VA 20190 - 5602

          

   

Phone:

          

(703) 787-8700                                                                                                                                                                                

          

   

Estimated Technology Readiness Level (TRL) :                                                                                                                                                          
Begin: 1
End: 3

          

          

     

Technical Abstract (Limit 2000 characters, approximately 200 words):

We propose to develop, train, and validate machine learning methods to automate the discovery of safety incident precursors so that incidents can be avoided. This supports NASA’s In-Time System-Wide Safety Assurance (ISSA) focus area. We will extend recent research by NASA that discovers the times at which precursors occurred for individual flights. Our extension will discover spatiotemporal precursors, i.e., both the times and data feature values that precede the incidents. Our method will find local precursors for individual safety incidents and global precursors specifying general rules of thumb that are patterns across many incidents. Currently, such pattern finding is a manual process. This work will generate new insights into the causes of unstable approaches to inform accident investigators, pilots, ATC, policy makers, and machine learning model developers. This initial Phase I study lays the groundwork for finding precursors of more complex safety incident types such as anomalies detected by black box models developed by Metron and NASA. Additionally, our precursor models will enable in-time safety incident prediction. Our team includes experts in air traffic, machine learning, and airspace data processing.

          

          

     

Potential NASA Applications (Limit 1500 characters, approximately 150 words):

• Extends NASA systemwide safety research by discovering precursor data features and thresholds.
• Integration with NASA’s In-Time Aviation Safety Management System (IASMS) will discover precursors for NASA-developed anomaly detectors and risk predictors.
• Integration with NASA’s Digital Information Platform (DIP) provides predictions of safety incidents to stakeholders and other analytic service providers.

          

          

     

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words):

• Wide applicability to explaining time series predictions across problem domains.
• Develop add-on technology to predictive services developed for Metron clients such as DARPA, Navy, Army, and DHS.
• Literature publications will advance the public knowledge.

          

          

     

Duration:     6