自适应选择的空间电源系统故障检测技术  

Fault Detection Technology of Space Power System Based on Adaptive Selection

作　　者：南作用 钟志刚 陈任翔 王亚 Nan Zuoyong;Zhong Zhigang;Chen Renxiang;Wang Ya(Zhongxun Post and Telecommunications Consulting and Design Institute,China Unicom,Zhengzhou 450000,China)

机构地区：[1]中国联通中讯邮电咨询设计院有限公司,郑州450000

出　　处：《单片机与嵌入式系统应用》2023年第11期29-32,共4页Microcontrollers & Embedded Systems

摘　　要：为了提高空间电源系统故障检测准确度,将卷积神经网络(Convolutional Neural Networks,CNN)与长短期记忆(Long Short Term Memory,LSTM)相结合,提出了一种自适应选择的空间电源系统故障检测模型,从而实现空间电源系统的故障检测。通过建立数字孪生虚拟模型引入典型故障,增加故障数据种类和数量,作为训练模型的数据集;采用CNN LSTM算法对样本数据集进行机器学习和训练,从而构建故障检测模型。通过实验验证了CNN LSTM模型故障检测准确度可达98%且损失函数值较少;在对各类型故障检测上,平衡F分数最低为96%,最高可达100%,更进一步说明提出方案的有效性和可行性,具有一定的实用价值。In order to improve the accuracy of spatial power system fault detection,Convolutional Neural Networks(CNN)are combined with Long Short Term Memory(LSTM).A fault detection model of space power system based on adaptive selection is proposed to realize the fault detection of space power system.By establishing digital twin virtual model,introducing typical faults,increasing the type and quantity of fault data,it is used as the data set of training model.The CNN LSTM algorithm is used for machine learning and training of sample data set to build fault detection model.The experiment results show that the fault detection accuracy of CNN LSTM model can reach 98%and the loss function is less.For each type of fault detection,the balance F score is as low as 96%and as high as 100%.The results demonstrate the effectiveness and feasibility of the proposed scheme,which has certain practical value.

关 键 词：空间电源系统 故障检测 CNN LSTM 数字孪生 自适应选择 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]