Generative Learning for Simulation of Vehicle Faults
This work addresses predictive maintenance for military vehicles, but it appears incremental as it builds on existing generative methods applied to a specific domain.
The paper tackles the problem of forecasting vehicle faults for predictive maintenance by developing a generative model trained on US Army data, which predicts the time to first fault with high accuracy and allows for timely interventions before breakdowns.
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.