TY - GEN
T1 - Automated Condition-Based Maintenance (CBM) Using Artificial Intelligence (AI) and Machine Learning (ML) for Unmanned Systems
AU - Proulx, Craig
AU - Reichard, Karl
N1 - Publisher Copyright:
© AUVSI XPONENTIAL 2022. All Rights Reserved.
PY - 2022
Y1 - 2022
N2 - As unmanned systems advance in capabilities, there are some aspects or components of this technology that lags behind and has a hard time catching up. It’s been evident that the maintenance techniques and sustainment of unmanned systems has not kept pace with all of the autonomous technological advancements. As militaries, municipalities, and commercial unmanned assets multiply into larger and more complex fleets of assets there is now a demand for increased reliability and endurance. Going from one or two unmanned systems to a fleet of autonomous aerial, land and water assets, you create much larger operational risks and financial obligations for yourself. Once your mission develops into longer duration periods and distance, reliability and endurance needs increase in a no-touch contested environment. You have now created additional risk factors that are more complex and challenging than the unmanned system itself. The Department of Defense and commercial companies are now in a catch-up mode to increase reliability, endurance while decreasing sustainment life-cycle costs. Availability and endurance are needed while reducing life cycle sustainment costs. The Congressional Budget Office (CBO) indicated the U.S. Government budgeted over $6 billion dollars for AI in fiscal year 2022, but less than 4% will be used for manned and unmanned sustainment technology. Scheduled and planned maintenance policies continue to be the norm. Old maintenance practices can’t and will not keep pace with increased availability and endurance requirements and will only increase sustainment costs. Current maintenance, logistics/supply systems and life-cycle engineering processes typically lack the intelligence to calculate remaining life expectancy and determine when parts will fail. They lack an integrated data environment that permits the prediction of optimal system performance, logistic support, and maintenance needs.
AB - As unmanned systems advance in capabilities, there are some aspects or components of this technology that lags behind and has a hard time catching up. It’s been evident that the maintenance techniques and sustainment of unmanned systems has not kept pace with all of the autonomous technological advancements. As militaries, municipalities, and commercial unmanned assets multiply into larger and more complex fleets of assets there is now a demand for increased reliability and endurance. Going from one or two unmanned systems to a fleet of autonomous aerial, land and water assets, you create much larger operational risks and financial obligations for yourself. Once your mission develops into longer duration periods and distance, reliability and endurance needs increase in a no-touch contested environment. You have now created additional risk factors that are more complex and challenging than the unmanned system itself. The Department of Defense and commercial companies are now in a catch-up mode to increase reliability, endurance while decreasing sustainment life-cycle costs. Availability and endurance are needed while reducing life cycle sustainment costs. The Congressional Budget Office (CBO) indicated the U.S. Government budgeted over $6 billion dollars for AI in fiscal year 2022, but less than 4% will be used for manned and unmanned sustainment technology. Scheduled and planned maintenance policies continue to be the norm. Old maintenance practices can’t and will not keep pace with increased availability and endurance requirements and will only increase sustainment costs. Current maintenance, logistics/supply systems and life-cycle engineering processes typically lack the intelligence to calculate remaining life expectancy and determine when parts will fail. They lack an integrated data environment that permits the prediction of optimal system performance, logistic support, and maintenance needs.
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M3 - Conference contribution
AN - SCOPUS:85133130783
T3 - AUVSI XPONENTIAL 2022
BT - AUVSI XPONENTIAL 2022
PB - Association for Unmanned Vehicle Systems International
T2 - AUVSI XPONENTIAL 2022
Y2 - 25 April 2022 through 28 April 2022
ER -