Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach
This addresses the risk of unplanned downtime and monetary loss for industries using edge-based sensor networks, though it appears incremental as it builds on existing deep reinforcement learning methods.
The paper tackles the problem of timely predictive maintenance for mission-critical equipment by proposing a model-free Deep Reinforcement Learning algorithm that self-learns optimal maintenance policies from sensor data, demonstrating potential for broader applications as an automatic learning framework.
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.