Failure Modes Detection of Nuclear Systems Using Machine Learning
Failure Modes Detection of Nuclear Systems Using Machine Learning
复制标题
DOI：
10.1109/dsa.2018.00017
复制
发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
David Tian;Jiamei Deng;E. Zio;F. Maio;Fu-cheng Liao
中科院分区：
文献类型：
--
作者：
David Tian;Jiamei Deng;E. Zio;F. Maio;Fu-cheng Liao
核系统故障的早期检测是核能领域的一个重要课题。提出了三种机器学习方法，用于探测LBE-XADS核系统在3000 s使命时间的前10%、50%和90%时间段后的故障模式（FM）。第一种方法在第一个10%的时间段之后检测LBE-XADS的FM，并且由两个基于高斯混合（基于GM）的分类器组成。第二种方法在第一个50%时间段之后检测LBE-XADS的FM，并且由基于GM的分类器和神经网络MLP 1组成。第三种方法检测LBE-XADS的故障模式后的第一个90%的时间段，并由一个基于GM的分类器和神经网络MLP 2。所提出的三种方法优于模糊相似性的方法，以前的工作。
Early detection of the failure of a nuclear system is an important topic in nuclear energy. This paper proposes three machine learning methodologies to detect the failure modes (FM) of the Lead-Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS) nuclear system after the first 10%, 50% and 90% time periods of the 3000 seconds mission time of the LBEXADS. The first methodology detects the FM of the LBE-XADS after the first 10% time period and consists of two Gaussian mixture-based (GM-based) classifiers. The second methodology detects the FM of the LBE-XADS after the first 50% time period and consists of a GM-based classifier and a neural network MLP1. The third methodology detects the failure mode of the LBE-XADS after the first 90% time period and consists of a GM-based classifier and a neural network MLP2. The three proposed methodologies outperformed the fuzzy similarity approach of the previous work.