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Smart Fault Management (SFM)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC18P2103
Agency Tracking Number: 182942
Amount: $111,144.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: S5
Solicitation Number: SBIR_18_P1
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-27
Award End Date (Contract End Date): 2019-02-15
Small Business Information
2050 Winners Drive, Fairmont, WV, 26554-2655
DUNS: 962024274
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Max Spolaor
 (304) 816-3600
Business Contact
 Scott Zemerick
Phone: (304) 806-2090
Research Institution

The Phase I of the SBIR “Smart Fault Management” project aims to demonstrate how the fault management process can be innovated by exploiting concepts from a combination of well-established and emerging disciplines such as Bayesian Statistics, GPU-accelerated numerical simulations, Data Science, and Machine Learning that are disrupting the current status quo in many scientific and engineering fields. Each of these disciplines provides well-established, cutting-edge tools that accurately combined and fine-tuned for the Fault Management arena will reshape the current FM architecture paradigm.

TMC will develop a proof-of-concept SFM system under the inputs and guidance of the NASA Center leading the effort. A representative spacecraft simulator will be identified in order to serve as the ground-truth model and data source. Test scenarios focusing on randomized fault injection will be developed and exercised against the software-only-simulation. The Monte Carlo simulation will be converted into a GPU-accelerated scientific application by exploiting massively parallel computing techniques enabled by the Compute Unified Device Architecture (CUDA) general purpose parallel computing architecture of modern NVIDIA GPUs. The large amount of raw diagnostic data, produced by the simulations system bundle (software-only-simulations plus Monte Carlo simulations), will be reduced, analyzed, visualized and modelled using big data mining techniques – a combination of data science concepts and machine learning algorithms – to thoroughly explore the higher-dimensional output space. The results will grant unbiased and unique access to the most influential variable trends, individual design parameters, and specific combinations of parameters that play a critical role in system failures and in the overall fault management behavior of a spacecraft system.

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

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