Abstract

Artificial intelligence is becoming a larger part of operations for many industries. One industry where this is occurring rapidly is the nuclear industry. Researchers from around the world are looking to implement this technology in various areas of the nuclear industry. This paper explores the use of machine learning to diagnose problems. This project makes use of synthetic data collected from a Generic Pressurized Water Reactor (GPWR) simulator on whether a reactor is operating normally or experiencing one of four different transient events. A dataset was created consisting of over 30 000 reactor operational states. The data were explored and wrangled using Python and the Pandas package, using a variety of methods. Once ready, the data were randomly shuffled, with half the data being used for training and the other half being used for testing. Six different machine learning models were created using scikit-learn and the AutoML package Tree-based Pipeline Optimization Tool (TPOT). These models were created using six data scaling methods along with six feature reduction/selection methods. These models were validated using accuracy, precision, recall, and F1 score. The accuracy of the individual transients was also calculated. All six of the models had validation scores above 95%, with the decision tree and logistic regression models performing the best. These results are promising for the possible future use of machine learning in reactor diagnostics.

摘要

人工智能正在成为许多行业运营的重要组成部分。这种情况正在迅速发生的一个行业是核工业。来自世界各地的研究人员正在寻求在核工业的各个领域实施这项技术。本文探讨了使用机器学习来诊断问题。该项目利用从通用压水反应堆 (GPWR) 模拟器收集的合成数据，了解反应堆是否正常运行或经历四种不同瞬态事件之一。创建了一个包含 30 000 多个反应堆运行状态的数据集。使用 Python 和 Pandas 包，使用多种方法对数据进行了探索和处理。一旦准备好，数据就会被随机打乱，一半的数据用于训练，另一半用于测试。使用 scikit-learn 和 AutoML 包基于树的管道优化工具 (TPOT) 创建了六种不同的机器学习模型。这些模型是使用六种数据缩放方法以及六种特征减少/选择方法创建的。这些模型使用准确度、精确度、召回率和 F1 分数进行了验证。还计算了各个瞬态的准确度。所有六个模型的验证分数均高于 95%，其中决策树和逻辑回归模型表现最佳。这些结果对于未来可能在反应堆诊断中使用机器学习很有希望。这些模型是使用六种数据缩放方法以及六种特征减少/选择方法创建的。这些模型使用准确度、精确度、召回率和 F1 分数进行了验证。还计算了各个瞬态的准确度。所有六个模型的验证分数均高于 95%，其中决策树和逻辑回归模型表现最佳。这些结果对于未来可能在反应堆诊断中使用机器学习很有希望。这些模型是使用六种数据缩放方法以及六种特征减少/选择方法创建的。这些模型使用准确度、精确度、召回率和 F1 分数进行了验证。还计算了各个瞬态的准确度。所有六个模型的验证分数均高于 95%，其中决策树和逻辑回归模型表现最佳。这些结果对于未来可能在反应堆诊断中使用机器学习很有希望。