Understanding the Limits of Artificial Intelligence for Predictive Maintenance

The 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.