Abstract
•A real-time control and decision making framework for system maintenance.•A Bayesian Reinforcement Learning framework to estimate remaining life.•Stochastic system control policies using system’s latent states over time.•Generalizing sensor observations to previously unseen states and conditions.
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.
