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Unscheduled maintenance has contributed to longer downtime for vehicles andincreased costs for Logistic Readiness Squadrons (LRSs) in the Air Force. Whenvehicles are in need of repair outside of their scheduled time, depending ontheir priority level, the entire squadron's slated repair schedule istransformed negatively. The repercussions of unscheduled maintenance arespecifically seen in the increase of man hours required to maintain vehiclesthat should have been working well: this can include more man hours spent onmaintenance itself, waiting for parts to arrive, hours spent re-organizing therepair schedule, and more. The dominant trend in the current maintenance systemat LRSs is that they do not have predictive maintenance infrastructure tocounteract the influx of unscheduled repairs they experience currently, and asa result, their readiness and performance levels are lower than desired.  We use data pulled from the Defense Property and Accountability System(DPAS), that the LRSs currently use to store their vehicle maintenanceinformation. Using historical vehicle maintenance data we receive from DPAS, weapply three different algorithms independently to construct an accuratepredictive system to optimize maintenance schedules at any given time. Throughthe application of Logistics Regression, Random Forest, and Gradient BoostedTrees algorithms, we found that a Logistic Regression algorithm, fitted to ourdata, produced the most accurate results. Our findings indicate that not onlywould continuing the use of Logistic Regression be prudent for our researchpurposes, but that there is opportunity to further tune and optimize ourLogistic Regression model for higher accuracy.

Predicting Breakdown Risk Based on Historical Maintenance Data for Air Force Ground Vehicles