A Plug-and-Play (PnP) Tool based on Online Machine Learning for Real-Time Monitoring and Control of Mechanical Systems

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911QX-18-P-0180
Agency Tracking Number: A181-034-0726
Amount: $99,990.08
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A18-034
Solicitation Number: 2018.1
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-18
Award End Date (Contract End Date): 2019-01-17
Small Business Information
701 McMillian Way NW, Huntsville, AL, 35806
DUNS: 185169620
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Jackson Cornelius
 (256) 726-4800
 proposals-contracts@cfdrc.com
Business Contact
 Tanu Singhal
Phone: (256) 726-4924
Email: tanu.singhal@cfdrc.com
Research Institution
N/A
Abstract
The proposed effort aims to develop and demonstrate a plug-and-play (PnP) tool/platform based on online neural network (NN) learning and modeling for real-time monitoring, prognostics, and control of mechanical systems. The salient aspects of the proposed solution are: (1) around-the-clock learning and identification of mechanical system enabling continuous dynamics tracking and model updating; (2) feature selection to extract the most representative inputs/features for compact model structure; (3) model predictive control (MPC) for real-time optimization and control synthesis; and (4) innovation in software-hardware architecture to streamline NN learning, model construction, MPC on a hybrid and embedded platform to meet demanding Size, Weight, and Power (SWaP) requirements in HUMS. In Phase I, key components including a NN-based online ML module, an input/feature selection module, a MPC module, and an embedded system architecture will be developed. Feasibility will be demonstrated via case studies of US Army interest, including real-time situational response demonstrations in a controlled simulated environment using datasets from both synthetic and real-world operations. The Phase II effort will focus on capability extension, algorithm optimization, software integration, extensive technology validation and demonstration, and technology insertion into Armys workflow.

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

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