基于模式识别技术的光电探测器故障辨识研究

Research on fault identification of photodetector based on pattern recognition technology

当前光电探测器故障辨识错误率高,为提升光电探测器故障辨识效果,设计了基于模式识别技术的光电探测器故障辨识方法。首先采集光电探测器状态信号,并从光电探测器状态信号中提取特征,然后利用主成分分析算法对特征进行降维处理,得到最优光电探测器状态辨识特征,最后将光电探测器状态特征作为支持向量机的输入,光电探测器状态作为支持向量机输出,通过支持向量机学习设计光电探测器状态辨识器,实验结果表明,本方法可以有效辨识光电探测器辨识故障,光电探测器故障辨识正确率超过了90%,光电探测器故障辨识时间控制在20 ms以内,为光电探测器状态分析提供了理论依据。

At present,there are many errors in fault identification of photodetectors.In order to improve the effect of fault identification of photodetectors,a fault identification method of photodetectors based on pattern recognition technology is designed.Firstly,the state signal of the photodetector is collected,and the features are extracted from the state signal of the photodetector.Then the principal component analysis algorithm is used to reduce the dimension of the features to obtain the optimal photodetector state identification feature.Finally,the photodetector state feature is used as the input of the support vector machine,and the photodetector state is used as the output of the support vector machine.The photodetector state identifier is designed through the support vector machine learning,The experimental results show that this method can effectively identify the fault of the photoelectric detector,the correct rate of the fault identification of the photoelectric detector is more than 90%,and the fault identification time of the photoelectric detector is controlled within 20 ms,which provides a basis for the status analysis of the photoelectric detector.