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Monitoring and Diagnosis via Energy Consumption Auditing

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8571-20-C-0027
Agency Tracking Number: F193-021-0308
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF193-021
Solicitation Number: 19.3
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-05-01
Award End Date (Contract End Date): 2021-05-01
Small Business Information
17150 W 95th Place
Arvada, CO 80007
United States
DUNS: 130770055
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christopher Bowman
 President
 (303) 469-9828
 cbowman@df-nn.com
Business Contact
 Dr. Christopher Bowman
Phone: (303) 469-9828
Email: cbowmanphd@gmail.com
Research Institution
N/A
Abstract

Goal is to develop intelligent system tools that learn normal patterns of life from energy consumption auditing of both cyber and manufacturing devices in manufacturing systems, and use a hybrid machine-learning (ML) and a digital-twin (DT) approach to learn and correlate changed patterns from physical and cyber threats. Unknown anomalies in a manufacturing machine will be detected and characterized by the Energy Consumption Abnormality Detection (ECAD) prototype system based upon the DF&NN Goal-Driven Condition-Based Predictive Maintenance (GCPM) baseline Condition-Based Maintenance (CBM). The DF&NN-QSI team will apply our ISA tools to generate temporally overlapping known and unknown manufacturing system and energy consumption abnormality detection and historical abnormality categorization event tracks. We will apply our Smoking Gun and TEAMS tools to discover correlation relationships in these events and use these to improve cause diagnosis and determine the effectiveness of using energy consumption data to detect cyber and physical attacks. The anomalies form inputs to QSI’s TEAMS® models that capture system-agnostic functional failure-cause and effect dependency relationships. The TEAMS® model facilitates mapping these anomalies to the causal model thereby allowing TEAMS® runtime reasoning engines to perform failure root-cause isolation and corrective/preventive action determination when such anomalies are detected.

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

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