FASTRONIC涡轮机数据的不平衡分类：新的邻近策略优化
Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization
摘要：
在工业和现实世界的系统中检测故障并实施最佳方法越来越重要。我们正在寻找人工智能应用程序提出的最值得信赖和实用的基于数据的故障检测方法。在本文中，我们提出了一个基于强化学习的故障检测框架和一个称为邻近策略优化的策略。由于缺乏故障数据，传统策略的一个显著问题是其在检测故障类别方面的弱点，这通过改变代价函数来解决。使用修改的邻近策略优化，我们可以提高性能，克服数据不平衡，并更好地预测未来的故障。当我们修改后的政策实施，所有的评估指标将增加$3\%$到$4\%$相比，传统的政策在第一个基准测试，之间的$20\%$和$55\%$在第二个基准测试，和之间的$6\%$和$14\%$在第三个基准测试，以及性能和预测速度的改进相比，以前的方法。
英文摘要：
There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data-based fault detection methods proposed by artificial intelligence applications. In this paper, we propose a framework for fault detection based on reinforcement learning and a policy known as proximal policy optimization. As a result of the lack of fault data, one of the significant problems with the traditional policy is its weakness in detecting fault classes, which was addressed by changing the cost function. Using modified Proximal Policy Optimization, we can increase performance, overcome data imbalance, and better predict future faults. When our modified policy is implemented, all evaluation metrics will increase by $3\%$ to $4\%$ as compared to the traditional policy in the first benchmark, between $20\%$ and $55\%$ in the second benchmark, and between $6\%$ and $14\%$ in the third benchmark, as well as an improvement in performance and prediction speed compared to previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
