基于深度极限学习机的模拟电路故障诊断  

Fault diagnosis of analog circuits based on deep extreme learning machine

作　　者：颜学龙[1] 马润平 YAN Xue-long;MA Run-ping(The CAT Lab of Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学CAT实验室

出　　处：《计算机工程与科学》2019年第11期1911-1918,共8页Computer Engineering & Science

基　　金：2018年桂林电子科技大学研究生科研创新项目(2018YJCX73)

摘　　要：针对模拟电路故障诊断中特征提取以及模型训练时间较长的难题,采用了一种基于深度极限学习机的模拟电路故障诊断算法。该算法将深度学习中自编码器的思想引入到极限学习机中,构建深度网络,将底层的故障特征转换更加抽象的高级特征,能自主地学习数据特征,避免了繁琐的特征提取和选择。最终通过Sallen-Key和四运放双二次高通滤波2个模拟电路进行仿真研究,实验结果验证了算法在模拟电路故障诊断上的可行性,也表明模型学习速度快、泛化能力好,具有较强的诊断能力,故障诊断分类准确率可以达到100%,诊断时间在0.3 s左右。Aiming at the problems of feature extraction and long model training time in analog circuit fault diagnosis, an analog circuit fault diagnosis algorithm based on deep extreme learning machine is adopted. The algorithm introduces the idea of auto encoder in deep learning into the extreme learning machine to construct a depth network, and forms more abstract advanced features from the underlying fault features. It can learn data features autonomously, avoiding additional feature extraction and selection. Finally, two analog circuits of Sallen-Key and four operational amplifiers with double quadratic high-pass filtering are taken as examples to carry out the simulation research. The experimental results verify the feasibility of fault diagnosis in analog circuits, and also show that the model has fast learning speed, good generalization ability and strong diagnosis ability. The classification accuracy of fault diagnosis can reach 100%, and the diagnosis time is about 0.3 s.

关 键 词：故障诊断 深度学习 自编码器 极限学习机 

分 类 号：TN406[电子电信—微电子学与固体电子学]