论文标题
DQLAP：带有真正蒸汽轮机系统的更新策略的深Q学习算法
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System
论文作者
论文摘要
在现代工业系统中，及时诊断故障并使用最佳方法变得越来越关键。如果未检测到故障或迟到，可能会使系统或浪费资源失败。机器学习和深度学习提出了各种基于数据的故障诊断的方法，我们正在寻找最可靠，最实用的方法。本文旨在开发基于深度学习和强化学习以进行故障检测的框架。我们可以提高准确性，克服数据不平衡，并通过更新新数据时更新增强学习政策来更好地预测未来的缺陷。通过实施此方法，与典型的背部型多层神经网络预测相比，在所有评估指标中，所有评估速度的提高，预测速度的提高和$ 3 \％$ - $ 4 \％$在所有评估指标中都会增加$ 3 \％$。
In modern industrial systems, diagnosing faults in time and using the best methods becomes more and more crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning have proposed various methods for data-based fault diagnosis, and we are looking for the most reliable and practical ones. This paper aims to develop a framework based on deep learning and reinforcement learning for fault detection. We can increase accuracy, overcome data imbalance, and better predict future defects by updating the reinforcement learning policy when new data is received. By implementing this method, we will see an increase of $3\%$ in all evaluation metrics, an improvement in prediction speed, and $3\%$ - $4\%$ in all evaluation metrics compared to typical backpropagation multi-layer neural network prediction with similar parameters.
