NASA SBIR 2018-I Solicitation

Proposal Summary

 18-1- H6.01-8522
 Integrated System Health Management for Sustainable Habitats
 Habitat ISHM Using Non-Invasive Load Management Analytics
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
MHI Energy Information Solutions
4011 University Drive, Suite 204
Durham , NC 27707-
(919) 433-2400

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)
Mr. Michael Chevalier
4011 University Dr., Ste. 204 Durham, NC 27707 - 2549
(919) 433-2400

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Matthew Heric
4011 University Dr., Ste. 204 Durham, NC 27707 - 2549
(919) 433-2400
Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 4
Technical Abstract

We believe a robust approach to integrated system health management (ISHM) design is the application of redundancy. Redundancy is often thought of in terms of hardware; however, functional, analytic, and information redundancy strategies should also be considered. 
Modeling sensor information is invaluable for diagnostics and critical path analysis. A total system approach is an efficient means of prognostics as well as identifying the time of failure. However, fidelity and resolution must be considered in both approaches. There are compounding errors as the subsystems are aggregated in a component model. Sensors themselves introduce a point of error and require due consideration of size, weight, and power (SWaP).

Signal processing, machine learning, and data mining techniques are common approaches in ISHM to improve the accuracy of alerts for known issues and an ability to identify latent and unknown failure conditions. Such techniques are not limited to ISHM. They are also used in fraud detection, image processing, medical diagnostics, and other domains.

Our innovation draws from the domain of electrical power systems with the application of non-invasive load management (NILM) models for load disaggregation. NILM is a means of extracting and analyzing discrete end-use system components from an aggregate energy signal. NILM evolution has run parallel with the developments in signal processing, machine learning, and data mining for feature extraction, classification, and action. A NILM approach for managing habitat subsystems allows for optimization of the number of sensors, mitigating points of information failure and the constraints of size, weight, and power while providing analytical redundancy to hardware systems. We submit that the application of disaggregation analytics is an innovative ISHM technology that supports NASA missions.

Potential NASA Applications

When NILM algorithms reside on a SSE device, the device becomes a component “smart sensor”. The reduction in the number of sensors mitigates sensory overload and aids in alarm management without compromising the crew’s ability to respond to emergencies. The use of NILM analytics and smart sensors is not limited to spaceflight or extra-terrestrial operations. The methodology can be applied in building management systems for facilities to reduce cost of operations.

Potential Non-NASA Applications

There are a limited number of NILM disaggregation applications and none that integrate ISHM as a feature. NILM tools, in conjunction with smart meters, could provide information to improve equipment design, building simulation, and construction, utility operations, and policy decisions. These would benefit residential and commercial consumers with greater efficiencies and lower costs. A reduction of only 0.01% would have yielded $3.2BM in savings in 2016 (Energy Information Administration data).



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