  Failure of mission-critical equipment interrupts production and results in
monetary loss. The risk of unplanned equipment downtime can be minimized
through Predictive Maintenance of revenue generating assets to ensure optimal
performance and safe operation of equipment. However, the increased
sensorization of the equipment generates a data deluge, and existing
machine-learning based predictive model alone becomes inadequate for timely
equipment condition predictions. In this paper, a model-free Deep Reinforcement
Learning algorithm is proposed for predictive equipment maintenance from an
equipment-based sensor network context. Within each equipment, a sensor device
aggregates raw sensor data, and the equipment health status is analyzed for
anomalous events. Unlike traditional black-box regression models, the proposed
algorithm self-learns an optimal maintenance policy and provides actionable
recommendation for each equipment. Our experimental results demonstrate the
potential for broader range of equipment maintenance applications as an
automatic learning framework.
