Automated Condition-Based Maintenance (CBM) Using Artificial Intelligence (AI) and Machine Learning (ML) for Unmanned Systems

Craig Proulx, Karl Reichard

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAUVSI XPONENTIAL 2022
PublisherAssociation for Unmanned Vehicle Systems International
ISBN (Electronic)9781713852230
StatePublished - 2022
EventAUVSI XPONENTIAL 2022 - Orlando, United States
Duration: Apr 25 2022Apr 28 2022

Publication series



Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Aerospace Engineering
  • Control and Systems Engineering


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