Abstract The increased complexity of sensor-intensive systems with expensive subsystems and costly repairs and failures calls for efficient real-time control and decision making policies. Deep reinforcement learning has demonstrated great potential in addressing highly complex and challenging control and decision making problems. Despite its potential to derive real-time policies using real-time data for dynamic systems, it has been rarely used for sensor-driven maintenance related problems. In this paper, we propose two novel decision making methods in which reinforcement learning and particle filtering are utilized for (i) deriving real-time maintenance policies and (ii) estimating remaining useful life for sensor-monitored degrading systems. The proposed framework introduces a new direction with many potential opportunities for system monitoring. To demonstrate the effectiveness of the proposed methods, numerical experiments are provided from a set of simulated data and a turbofan engine dataset provided by NASA.

中文翻译：

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用于实时传感器驱动决策和预测分析的深度强化学习方法

摘要 传感器密集型系统具有昂贵的子系统和昂贵的维修和故障的复杂性增加，需要有效的实时控制和决策策略。深度强化学习在解决高度复杂和具有挑战性的控制和决策问题方面显示出巨大的潜力。尽管它有可能使用动态系统的实时数据导出实时策略，但它很少用于传感器驱动的维护相关问题。在本文中，我们提出了两种新颖的决策方法，其中利用强化学习和粒子滤波来 (i) 导出实时维护策略和 (ii) 估计传感器监控的退化系统的剩余使用寿命。提议的框架引入了一个新方向，具有许多系统监控的潜在机会。为了证明所提出方法的有效性，从一组模拟数据和 NASA 提供的涡扇发动机数据集提供了数值实验。