NASA SBIR 2020-I Solicitation

Proposal Summary


PROPOSAL NUMBER:
 20-1- S5.03-6415
SUBTOPIC TITLE:
 Accelerating NASA Science and Engineering through the Application of Artificial Intelligence
PROPOSAL TITLE:
 Deep Learning Based Real-Time Engine Prognostics and Health Management (DL-RTEPHM)
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Opto-Knowledge Systems, Inc. (OKSI)
19805 Hamilton Avenue
Torrance, CA 90502
(310) 756-0520

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Dr. Chris Holmes-Parker
E-mail:
chris.holmesparker@oksi.com
Address:
19805 Hamilton Avenue Torrance, CA 90502 - 1341
Phone:
(310) 756-0520

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Mr. Joel Gat
E-mail:
joel.gat@optoknowledge.com
Address:
19805 Hamilton Avenue Torrance, CA 90502 - 1341
Phone:
(310) 752-0520
Estimated Technology Readiness Level (TRL) :
Begin: 2
End: 4
Technical Abstract (Limit 2000 characters, approximately 200 words)

Testing and verifying rocket engines requires the careful placement, calibration, and analysis of dozens of sensors to measure important system characteristics such as temperature, density, flow, vibration, etc.  Due to the volume and complexity of the raw outputs of these sensors, most are not monitored live and require extensive amounts of time from engineers to analyze and verify tests in an offline manner.  In recent decades, Machine learning (ML) technologies have been proven to speed up the process and reliability of identifying anomalies.  However, as the volume and complexity of sensor data has grown, traditional ML methods have failed to scale and require carefully crafted sensor models to get meaningful results.  More recently, Deep Learning (DL) methods have been proven to achieve superhuman performance and reliability in extremely complex domains such as object recognition, natural language processing, and games.  They achieve this by identifying patterns and relationships in data that humans are unable to quantify and encoding them hierarchically within deep neural networks.  With DL technology having been proven in many domains, we will build a DL-based, real-time engine sensor diagnostic and health management system to enable superhuman-level analysis and decision-making during firing tests and launches. 

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

NASA is a major developer and operator of rocket engines.  Machine Learning (ML)/Deep learning (DL) can provide great a benefit for automated engine operations, health monitoring, prognostics, fault detection and, remaining useful life predictions for various components By bringing ML/DL to the area of rocket engine testing and operations, NASA will benefit from the latest development in Artificial Intelligence (AI) and also will transition rocket engine technology to a new era of automation. 

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

The rocket industry serves both NASA the USAF, and commercial launch companies.  The proposed DL applications will be transitioned to industry.  Other potential users include manufacturing facilities, stationary machinery such as generators, turbines, etc., as well as mobile applications such as automotive engines and airplane engines.

Duration: 6

Form Generated on 06/29/2020 21:12:40