Tech Briefs Magazine - January 2023 - 36

Software
that require adjustments in a robotic
prosthetic's behavior: tile, brick, concrete,
grass, " upstairs, " and " downstairs. "
The new " environmental context "
framework incorporates both hardware
and software elements. The researchers
designed the framework for use with any
lower-limb robotic exoskeleton or robotic
prosthetic device but with one additional
piece of hardware: a camera. They
used cameras worn on eyeglasses and
cameras mounted on the lower-limb
prosthesis itself and evaluated how the
AI was able to make use of computer vision
data from both types of camera, separately
and when used together.
The AI teaches deep-learning systems
how to evaluate and quantify uncertainty
in a way that allows the system to incorporate
uncertainty into its decision-making.
While it is relevant for robotic prosthetics,
the work could be applied to any
type of deep-learning system.
To train the AI system, researchers connected
the cameras to able-bodied individuals,
who then walked through a variety of
indoor and outdoor environments. The
researchers then did a proof-of-concept
evaluation by having a person with lower-limb
amputation wear the cameras
while traversing the same environments.
They found that the model can be appropriately
transferred so the system can operate
with subjects from different populations
- the AI works well even though it
was trained by one group of people and
used by somebody else.
For more information, contact Edgar
Lobaton at edgar.lobaton@ncsu.edu.
Enhancing Fault Isolation and Detection for Electric
Powertrains of UAVs
A method for rapidly understanding, isolating, and informing response to faults in a
complex system.
Ames Research Center, Mountain View, CA
N
ASA's System-Wide Safety (SWS)
project is developing innovative data
solutions to assure safe, rapid, and repeatable
access to a transformed National Airspace
System. The increasing number of
electric propulsion systems that will enter
the airspace will require systems that ensure
high safety standards in the low-altitude
airspace. One element that can help
ensure safety is proper diagnosis of failures
via Fault Detection and Isolation
(FDI). NASA Ames has developed a fault
isolation approach for electric powertrains
of unmanned aerial vehicles.
The approach leverages a combination
of Failure Mode and Effect Analysis (FMEA)
and Bayesian Networks (BN) to create a
dependability structure within a diagnostic
framework-mapping fault and failure
events from the FMEA within a BN. This
framework helps the fault isolation process
by identifying the probability of occurrence
of specific fault or root causes given
evidence observed through sensor signals.
The tool developed through this work
merges information from the electric
propulsion system design phase with diagnostic
tools. Information from the FMEA
from the system design phase is embedded
within BN. Each node in the network
can represent either a fault, failure mode,
root cause or effect, and the causal relationships
between different elements are
described through the connecting edges.
This novel approach can help FDI,
producing a framework capable of isolating
the cause of sub-system level fault
and degradation. This system: Identifies
36
and quantifies the effects of the identified
hazards, the severity and probability
of their effects, their root cause, and
the likelihood of each cause; Uses a
Bayesian framework for FDI; Based on
the FDI output, estimates health of the
faulty component and predicts the remaining
useful life (RUL) by also performing
uncertainty quantification
(UQ); Identifies potential electric powertrain
hazards and performs a functional
hazard analysis (FHA) for unmanned
aerial vehicles (UAVs)/Urban
Air Mobility (UAM) vehicles.
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Despite being developed for and
demonstrated with an application to an
electric UAV, the methodology is generalized
and can be implemented in other
domains. These domains range from
manufacturing facilities to various autonomous
vehicles.
NASA is actively seeking licensees to
commercialize this technology. Please
contact NASA's Licensing Concierge at
Agency-Patent-Licensing@mail.nasa.gov
or call at 202-358-7432 to initiate licensing
discussions. For more information, visit
https://technology.nasa.gov.
Tech Briefs, January 2023
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Tech Briefs Magazine - January 2023

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