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Energy Consumption Abnormality Detection (ECAD)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8571-21-C-0029
Agency Tracking Number: F193-021-0308
Amount: $749,971.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF193-021
Solicitation Number: 19.3
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-09-01
Award End Date (Contract End Date): 2023-09-01
Small Business Information
17150 W 95th Place
Arvada, CO 80007-1111
United States
DUNS: 130770055
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Christopher Bowman
 (303) 469-9828
Business Contact
 Dr. Christopher Bowman
Phone: (303) 469-9828
Research Institution

The focus problem addressed in this effort is to automatically learn historical normal Computer Numerical Control (CNC) system behavior to produce the following:   unmodeled unknown CNC abnormal behavior detections with historical classification characterizations if seen historically abnormal behavior and similar abnormal class characterization flags with abnormality and classification scores categorization of similar normal unmodeled from historical behavior in real-time CNC behavior with category trust scores to inform the user how similar the current real-time abnormal declared class signature is to signatures that the categorization neural networks were trained on   recommended responses to the abnormality detections and valid categorizations We plan to apply the ECAD Deep Multi-Start Residual Training (D-MSRT) NNs, Smoking Gun, and maintenance condition categorization D-MSRT NNs capabilities for as many aviation systems as available. We will train D-MSRT abnormality detection NNs to learn the labeled repair conditions that were used for each categorization NN to provide a categorization NN result trust score to the user. We will incorporate into ECAD our existing goal-driven turnkey NN capabilities that determine when to retrain, what data to retrain on, what data to test on, how to evaluate, and when to promote to on-line operations. This allow ECAD to automatically evolve and improve its performance based on progressing user goals. We will adapt the ECAD graphical user interfaces (GUI) for user-tier roles with a standardized software deployment approach designed for ease of deployment. ECAD will provide early detection and characterization of system anomalies and component failures.  DF&NN will deliver ECAD Docker REpresentational State Transfer (REST) API services which support sharing of NNs and results across distributed operations. We will use these to validate ECAD performance and increase user trust in ECAD results. We will work closely with the sponsor to identify operational transition opportunities. ECAD will not be a black box solution. ECAD will provide performance sensitivity analyses and declaration confidences. ECAD will provide a system that develops trust with operators and provides CBM capabilities.   A sample use case is for historical T4 measurand behavior to be learned by TrnSatDP. Historical abnormal signatures are automatically clustered, named, and tracked. These are shown in ADCV for the user to flag those of interest. Categorization D-MSRT NNs are trained to be able to flag these signatures in real-time when they occur again. Trust D-MSRT NNs are trained to provide a confidence in the categorization NNs class declarations. All 3 of these NNs are run in real-time to detect the unknown unexpected abnormal behaviors, classify signatures that need to be found, and provide trust scores.     

* Information listed above is at the time of submission. *

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