基于机器学习的控制棒驱动机构传动副磨损寿命预测研究  

Research on Wear life Prediction of CRDM Transmission Pair Based on Machine Learning

作　　者：肖聪[1] 刘承敏 罗英[1] 彭航[1] 李维 张志强 黄擎宇 Xiao Cong;Liu Chengmin;Luo Ying;Peng Hang;Li Wei;Zhang Zhiqiang;Huang Qingyu(National Key Laboratory of Nuclear Reactor Technology,Nuclear Power Institute of China,Chengdu,610213,China)

机构地区：[1]中国核动力研究设计院核反应堆技术全国重点实验室,成都610213

出　　处：《核动力工程》2025年第2期209-216,共8页Nuclear Power Engineering

摘　　要：控制棒驱动机构(CRDM)是反应堆内唯一具有相对运行的设备单元,可快速调节反应堆反应性,对反应堆安全运行十分重要,磨损是影响CRDM传动副功能失效的主要因素,直接决定其使用寿命。本文通过CRDM传动副磨损实验,发现传动副主要磨损形式有3种:磨粒磨损、疲劳磨损和氧化磨损,同时发现当传动副顶部区域磨损体积比达16.46%时,CRDM出现滑棒,可判定此刻转动部件出现了磨损失效,将此刻的磨损体积值作为传动副的失效阈值。在获得传动副磨损体积数据和外部振动信号后,本文构建了内部磨损体积与外部振动信号的关联关系,并通过外部振动信号,基于支持向量回归(SVR)、卷积神经网络(CNN)、长短期记忆网络(LSTM)3种机器学习算法,分别构建了CRDM传动副寿命预测模型,通过对比分析认为,在预测精度上LSTM模型优于CNN模型优于SVR模型,在计算效率上SVR模型优于CNN模型优于LSTM模型。Control Rod Drive Mechanism(CRDM)is the only equipment unit with relative operation in the reactor.It can adjust the reactivity of the reactor quickly,so it is very important for the safe operation of the reactor.Wear is the main factor affecting the functional failure of CRDM transmission pair,directly determining its service life.Through the wear life test of the transmission pair of CRDM,it is found that the three main wear forms of the transmission pair are abrasive wear,fatigue wear and oxidation wear.At the same time,it is found that when the wear volume ratio at the top of the transmission pair reaches 16.46%,the sliding rod appears in the driving mechanism,which can be judged that the rotating parts have worn out,and the wear volume value at this moment is taken as the failure threshold of transmission pair.After obtaining the data of transmission pair wear degradation and external vibration signals,the relationship between internal wear and external vibration signals is constructed.Through external vibration signals and based on three machine learning algorithms SVR,CNN and LSTM,the life prediction models of CRDM transmission pair were established respectively.Through comparative analysis,it is concluded that in terms of prediction accuracy,the LSTM model outperforms the CNN model,which in turn outperforms the SVR model,while in terms of computational efficiency,the SVR model surpasses the CNN model,which in turn surpasses the LSTM model.

关 键 词：控制棒驱动机构(CRDM) 传动副 磨损 剩余寿命 

分 类 号：TL334[核科学技术—核技术及应用]