摘要
针对模拟电路故障诊断中故障信息的多特征、高噪声以及故障诊断时间较长的问题,提出了一种基于H-DELM的模拟电路故障诊断模型。该模型的架构单元为双随机隐藏层的深度极限学习机DELM-AE,2个随机隐藏层用于编码特征,1个输出层用于解码特征。将DELM-AE以分层结构堆叠构建H-DELM模型,由于DELM-AE可以进行特征表示,而且输出与原始输入信息相同,因此H-DELM可以尽可能多地复制原始输入数据,进而可以学习到更具表现力和紧凑性的特征。最终通过四运放双二次高通滤波器和更复杂的二级四运放双二阶低通滤波器2个电路进行验证。实验结果表明了该模型在模拟电路故障诊断上的可行性;与其他模型的比较表明该模型的鲁棒性较强,分类速度可以达到1 s左右,故障分类准确率可以达到100%。
Aiming at the problems of multiple features,high noise and long fault diagnosis time in analog circuit fault diagnosis,an analog circuit fault diagnosis model based on H-DELM is proposed.The architecture unit of the model is a deep extreme learning machine(DELM-AE)with double random hidden layers.Two random hidden layers are used to encode features and one output layer is used to decode features.The H-DELM model is constructed by stacking DELM-AE in hierarchical structure.Because DELM-AE can represent the features and the output is the same as the original input information,H-DELM can copy the original input data as much as possible,and then learn more expressive and compact features.Finally,the verification is carried out by two circuits:quadruple operational amplifier double-order high-pass filter and two-stage four-op-amp biquad lowpass filter.The experimental results show that the model is feasible in analog circuit fault diagnosis.Compared with other model,it is proved that the proposed model has high robustness,the classification speed is about 1s,and the accuracy rate of fault classification can reach 100%.
作者
谈恩民
王晨
TAN En-min;WANG Chen(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《计算机工程与科学》
CSCD
北大核心
2022年第1期27-35,共9页
Computer Engineering & Science
基金
国家自然科学基金(61741403)。
