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.

翻译：我们开发了一种新颖的生成式模型，用于在给定实际运行条件的情况下模拟车辆健康状况并预测故障。该模型基于美国陆军“预测性后勤”项目的数据进行训练，旨在支持预测性维护。其预测故障的时间窗口足够提前，以便在故障发生前执行维护干预。模型整合了影响车辆健康状况的现实因素。通过分析运行数据，该模型还能帮助我们理解车辆状况，并将每辆车划分为若干离散状态。重要的是，该模型能以高精度预测首次故障发生的时间。我们将其性能与其他模型进行了比较，并展示了其成功的训练过程。