论文标题
基于边缘的传感器网络的预测维护：一种深厚的增强学习方法
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach
论文作者
论文摘要
关键任务设备的失败会中断生产并导致货币损失。可以通过预测维护收入产生资产来确保设备的最佳性能和安全操作来最大程度地减少计划外设备停机时间的风险。但是，设备的感应化增加会产生一个数据洪水，而现有的基于机器的预测模型就无法实现及时的设备状况预测。在本文中，提出了一种无模型的深钢筋学习算法，用于从基于设备的传感器网络环境中进行预测设备维护。在每个设备中，传感器设备汇总了原始传感器数据，并分析了针对异常事件的设备健康状况。与传统的黑盒回归模型不同，拟议的算法是自学习的最佳维护政策，并为每种设备提供了可行的建议。我们的实验结果表明，作为自动学习框架，具有更广泛的设备维护应用程序的潜力。
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.
