Aerospace & Defense Technology - May 2024 - 17
Understanding the Limits
of Artificial Intelligence
for Predictive Maintenance
T
he U.S. Air Force (USAF) deploys flying units with readiness
spares packages (RSPs) to try to ensure that the
units are stocked with enough parts to be self-sufficient
for 30 days. This report is the third in a five-volume
series addressing how AI could be employed to assist warfighters
in four distinct areas: cybersecurity, predictive
maintenance, wargames, and mission planning, with predictive
maintenance in focus.
Predicting which parts are likely to fail - and, therefore,
which parts should be included in the RSPs - is important
because overstocking can be expensive and understocking can
threaten mission readiness. This report presents a discussion of
whether and when artificial intelligence (AI) methods could be
used to improve parts failure analysis, which currently uses a
model that assumes a probability distribution. To do this, several
machine learning (ML) models were developed and tested
on historical data to compare their performance with the optimization
and prediction software currently employed by the
USAF, using A-10C aircraft data as a test case.
Using A-10C aircraft data as a test case, we addressed the following
research questions:
* How does the current RSP failure analysis approach perform
in a retrospective analysis against historical data?
* How can AI help inform the failure analysis process, and what
are its limitations?
* What other potential improvements can augment the existing
approach?
The USAF currently uses the aircraft sustainability model
(ASM) to calculate RSPs to maintain aircraft fleet availability
and to manage maintenance budgets. ASM outputs a shopping
list of parts for part managers to purchase for their RSPs. ASM
was created by the Logistics Management Institute in the 1970s
to calculate the composition of war reserve spares kits (WRSKs),
the predecessor to RSPs. ASM calculates the composition of
RSPs to maximize aircraft availability across the fleet for a given
budget. A summary of this process is shown in Figure 2.1.
ASM predicts part failures (and thus demand for spare parts) for
a given base within a 30-day period. Using this prediction and the
amount of on-shelf inventory, ASM estimates the shortfall (number
of backorders) in parts. Each backordered part, across all
bases, results in not-mission- capable aircraft and decreases fleetAerospace
& Defense Technology, May 2024
mobilityengineeringtech.com
17
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Aerospace & Defense Technology - May 2024
Table of Contents for the Digital Edition of Aerospace & Defense Technology - May 2024
Aerospace & Defense Technology - May 2024 - Intro
Aerospace & Defense Technology - May 2024 - Sponsor
Aerospace & Defense Technology - May 2024 - Cover1
Aerospace & Defense Technology - May 2024 - Cover2
Aerospace & Defense Technology - May 2024 - 1
Aerospace & Defense Technology - May 2024 - 2
Aerospace & Defense Technology - May 2024 - 3
Aerospace & Defense Technology - May 2024 - 4
Aerospace & Defense Technology - May 2024 - 5
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Aerospace & Defense Technology - May 2024 - 7
Aerospace & Defense Technology - May 2024 - 8
Aerospace & Defense Technology - May 2024 - 9
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Aerospace & Defense Technology - May 2024 - Cover3
Aerospace & Defense Technology - May 2024 - Cover4
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