论文标题
使用机器学习的低碳能量生产中不频繁的不良事件预测
Infrequent adverse event prediction in low carbon energy production using machine learning
论文作者
论文摘要
我们解决了预测在预测维护的背景下不频繁的不良事件发生的问题。我们将相应的机器学习任务视为一个不平衡的分类问题，并提出了一个框架来解决该任务，该框架能够利用不同的分类器以预测发生在发生之前发生不良事件的情况。特别是，我们专注于在低碳能量产生中产生的两种应用：厌氧消化中的泡沫形成和核电站蒸汽涡轮机中的冷凝器管泄漏。一组广泛的计算实验的结果表明了我们提出的技术的有效性。
We address the problem of predicting the occurrence of infrequent adverse events in the context of predictive maintenance. We cast the corresponding machine learning task as an imbalanced classification problem and propose a framework for solving it that is capable of leveraging different classifiers in order to predict the occurrence of an adverse event before it takes place. In particular, we focus on two applications arising in low-carbon energy production: foam formation in anaerobic digestion and condenser tube leakage in the steam turbines of a nuclear power station. The results of an extensive set of omputational experiments show the effectiveness of the techniques that we propose.
