基于BP神经网络的核探测器故障诊断方法研究  

Study of Nuclear Detector Fault Diagnosis Method Based on BP Neural Network

作　　者：谢宇希 颜拥军[1] 李翔 丁天松 马川 XIE Yuxi;YAN Yongjun;LI Xiang;DING Tiansong;MA Chuan(School of Nuclear Science and Technology,University of South China,Hengyang 421001,China;College of Physics and Electronic Engineering,Hengyang Normal University,Hengyang 421008,China)

机构地区：[1]南华大学核科学技术学院,湖南衡阳421001 [2]衡阳师范学院物理与电子工程学院,湖南衡阳421008

出　　处：《原子能科学技术》2021年第10期1857-1864,共8页Atomic Energy Science and Technology

基　　金：国家自然科学基金(11575081);湖南省自然科学基金(2018JJ2317);“铀矿勘查技术”湖南省工程研究中心开放基金(YK20K02)。

摘　　要：核探测器是核设施放射性监测的重要设备,为了保障该设备的持续稳定运行,本研究针对闪烁体探测器提出了一种基于BP神经网络的在线智能故障诊断方法。采用小波包变换将探测器输出信号从时域变换至频域后提取特征向量,将得到的特征向量作为BP神经网络故障诊断模型的输入,再通过误差梯度下降法对该模型的参数进行优化,最终利用最优的诊断模型完成故障类型的智能识别与分类,并将该方法与统计诊断方法和基于支持向量机的故障诊断方法进行横向的对比研究。研究结果表明,新方法的平均诊断准确率均优于上述两种方法。因此,该方法的应用能有效地提高核探测器的故障诊断准确率。Nuclear detectors play important roles in radioactive monitoring.In order to keep nuclear detectors stable,an on-line intelligent fault diagnosis method based on BP neural network was proposed for scintillation detectors.By transforming detector’s output signals to frequency domain from time domain,characteristic vectors were obtained from wavelet packet transform,then these vectors were treated as input of BP neural network fault diagnosis model,and the parameters of fault diagnosis model were optimized by error gradient descent method.Finally,the optimal fault diagnosis model was employed to identify and classify fault types intelligently,which was also compared with a statistical model and another models based on support vector machine.Experimental results show that the outcome of proposed method is more accurate than the two methods above.Therefore,an application of this method can effectively improve the accuracy of nuclear detector fault diagnosis.

关 键 词：小波包 神经网络 故障诊断 闪烁体探测器 

分 类 号：TL812[核科学技术—核技术及应用]