基于核探测器信号波形特征的故障诊断研究

Research on Fault Diagnosis Based on Signal Waveform Characteristic of Nuclear Detector

核探测器是一种特殊的随机信号转换器,发生故障时,采用传统的人工方法很难及时有效地对故障进行诊断。本文提出了一种针对闪烁体探测器信号波形特征的在线智能故障检测与分类方法,通过分析闪烁体探测器不同故障时的输出信号变化特征,建立了相应的故障模型。使用小波包算法与支持向量机理论分解并提取特征向量,即可判断故障类型。以ST401闪烁体探测器为例,进行了模拟仿真实验。实验结果表明,基于信号波形特征的数字化方法能快速有效地对闪烁体探测器进行故障自动诊断。

The nuclear detector is a kind of special random signal converter,and it is difficult to diagnose effectively and quickly by traditional manual method when fault occurs.In this paper,an online intelligent fault detection and classification method was proposed by analyzing the characteristic of the different fault output signals of scintillator detector,and the corresponding fault models were established.Using wavelet packet algorithm and support vector machine(SVM)theory to decompose and extract eigenvectors,the fault type can be judged.Taking ST401 scintillator detector as an example,the simulation experiment was carried out.The experimental results show that the digital method based on signal waveform characteristic can diagnose scintillator detector fault quickly and effectively.