  We develop a novel generative model to simulate vehicle health and forecast
faults, conditioned on practical operational considerations. The model, trained
on data from the US Army's Predictive Logistics program, aims to support
predictive maintenance. It forecasts faults far enough in advance to execute a
maintenance intervention before a breakdown occurs. The model incorporates
real-world factors that affect vehicle health. It also allows us to understand
the vehicle's condition by analyzing operating data, and characterizing each
vehicle into discrete states. Importantly, the model predicts the time to first
fault with high accuracy. We compare its performance to other models and
demonstrate its successful training.
