Democratizing AI for Condition-Based Maintenance using Probabilistic Programming

Kenneth Lu1, Sanja Cvijic1, David Dewhurst1, Joe Gorman1, Rob Hyland1, James Templin2

Proceedings of The 69th Annual Reliability & Maintainability Symposium (RAMS®) (January 2023)

Advances in AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes; however, AI/ML solutions themselves inevitably become a maintenance liability, wherein the end users must repeatedly work with a data scientist to update the AI/ML application.  In this paper, we present novel research that alleviates this problem by enabling non-AI/ML experts to create and maintain AI/ML applications with minimal guidance from a data scientist.  We describe this research as the “Democratization” of AI/ML and accomplish this by leveraging cutting edge techniques in knowledge capture and probabilistic programming.

1 Charles River Analytics
2 Army Futures Command

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