Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a model-free deep reinforcement learning approach for predictive maintenance in sensor networks, enabling equipment to self-learn optimal maintenance policies from sensor data, reducing downtime and operational costs.

Contribution

The paper presents a novel deep reinforcement learning algorithm tailored for equipment maintenance, addressing the limitations of traditional models in handling large sensor data streams.

Findings

Demonstrates effective prediction of equipment health status.

Shows the algorithm can learn maintenance policies autonomously.

Potential for broad application in automated equipment management.

Abstract

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.