核测量系统关键电路故障诊断与预测方法研究  

Research for Fault Diagnosis and Predetermination Methods of Crucial Circuit in the Nuclear Measurement System

作　　者：何佳佶 万波 闵渊 李昆 黎刚 蔡娇 HE Jiaji;WAN Bo;MIN Yuan;LI Kun;LI Gang;CAI Jiao(Nuclear Power Institute of China Nuclear Power Design and Research Sub-institute,Chengdu 610213,China)

机构地区：[1]核反应堆系统设计技术重点实验室,成都610213

出　　处：《核电子学与探测技术》2024年第2期262-268,共7页Nuclear Electronics & Detection Technology

基　　金：国家自然科学基金(No.12205287)。

摘　　要：探讨了一种可实现核测量系统关键电路故障诊断与预测的方法。以核测量系统放大电路为研究对象,采用PSPICE对电路进行建模,通过小波包变换从电路脉冲响应信号中提取出代表电路状态的故障特征信息,将故障特征信息输入BP神经网络模型开展故障诊断研究,同时,基于相关向量机模型(RVM)开展故障预测研究。研究结果表明:该故障诊断模型对不同故障模式的识别准确率高达99%,且基于量子粒子群滤波算法(QPSO)的RVM模型能够实现电路故障指标发展趋势的准确预测。本项研究可为核测量系统关键电路的维护、保障提供更充实的理论支撑。This paper presents the methods that could achieve the fault diagnosis and predetermination of crucial circuit in the nuclear measurement system.The analog electric current amplifying circuit was selected as the object of our study and it was simulated by PSPICE.The fault characteristics of the analog circuit were extracted from the output shock response through wavelet packet transform method.These characteristics were used as the input information of BP neural network for fault type identification.At the same time,fault prediction research is carried out based on the relevance vector machine(RVM).The calculation results show that,the efficiency of fault diagnosis was 99%for different fault types,and the RVM model optimized with quantum-behaved particle swarm optimization(QPSO)can accurately predict the development trend of circuit faults.This study provides more substantial theoretical support for the maintenance and repair of crucial circuit in nuclear measurement system.

关 键 词：核测量系统 模拟电路 故障诊断 故障预测 小波包变换 

分 类 号：TL362[核科学技术—核技术及应用]