Generative Learning for Simulation of Vehicle Faults

TL;DR

This paper introduces a novel generative model trained on military vehicle data to simulate vehicle health, forecast faults in advance, and support predictive maintenance by analyzing operational data and vehicle states.

Contribution

The paper presents a new generative modeling approach that incorporates real-world operational factors for vehicle fault simulation and early fault prediction.

Findings

Accurately predicts time to first fault

Successfully trained on US Army vehicle data

Outperforms existing models in fault forecasting

Abstract

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.