摘要
核电厂阀门故障的及时、准确诊断,对核电站的安全、可靠运行意义重大。传统侵入式阀门诊断需要停止阀门运行,通过定位器或在阀门内部结构中安装传感器或应变片来测量阀杆的扭矩和推力,进而判断阀门的健康状态。其缺点是干扰阀门正常运行且影响阀门内部结构。为避免传统阀门故障检测的弊端,提出了一种基于小波变换的核电厂阀门故障识别诊断新方法。通过对阀门数据信号的分析,识别和表征阀门信号时间和频域中的瞬态现象,并与阀门故障特征相关联,为核电厂阀门故障识别诊断提供了一种新的预测性维护方法。该方法实现了在不停止阀门运行且不侵入阀门内部结构的前提下完成阀门状态的检测,有效提高了核电站运维水平,同时也为信号分析理论应用于核电站故障检测提供了一种新的思路。
Timely and accurate diagnosis of valve faults in nuclear power plants is of great significance to the safe and reliable operation of nuclear power plants.Traditional invasive valve diagnostics require stopping valve operation and determining valve health by measuring stem torque and thrust through a locator or by installing a sensor or strain gauge in the valve’s internal structure.Its disadvantage is that it interferes with the normal operation of the valve and affects the internal structure of the valve.In order to avoid the disadvantages of traditional valve fault detection,a new method based on wavelet transform for valve fault identification and diagnosis in nuclear power plant was proposed.Through the analysis of valve data signals,the transient phenomena in the time and frequency domain of valve signals are identified and characterized,which are correlated with valve fault characteristics,and a new predictive maintenance method is provided for valve fault identification and diagnosis in nuclear power plants.This method realizes the valve state detection without stopping the valve operation and invading the internal structure of the valve,which effectively improves the operation and maintenance level of nuclear power plants,and also provides a new idea for the application of signal analysis theory to fault detection of nuclear power plants.
作者
胡佳堃
蒋燕祥
万骄谊
田骏
杨磊
HU Jiakun;JIANG Yanxiang;WAN Jiaoyi;TIAN Jun;YANG Lei(China Nuclear Power Enginering Co.,Ltd.,Shenzhen 518000,China;College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《自动化仪表》
CAS
2021年第S01期26-31,共6页
Process Automation Instrumentation
关键词
核电厂
故障识别
预测性维护
非侵入式
小波变换
故障特征
小波分解
信号处理
Nuclear power plant
Fault identification
Predictive maintenance
Non-intrusive
Wavelet transform
Fault feature
Wavelet decomposition
Signal processing
