CAJ | 학술논문

小波变换是一种时频分析方法，具有多分辨率特性，被誉为数学显微镜；而BP神经网络具有较好的泛化能力，很适合判断电路状态属于哪种故障类型的分类问题。将二者结合起来，采用基于小波神经网络的模拟电路故障诊断方法，应用小波变换对模拟电路幅频响应的采样信号进行故障特征提取，然后利用BP神经网络对各种状态下的特征向量进行分类决策，实现模拟电路的故障诊断。通过对电路进行仿真，证明该方法能够实现故障检测及定位，具有准确率高的特点。
소파변환시일충시빈분석방법，구유다분변솔특성，피예위수학현미경；이BP신경망락구유교호적범화능력，흔괄합판단전로상태속우나충고장류형적분류문제。장이자결합기래，채용기우소파신경망락적모의전로고장진단방법，응용소파변환대모의전로폭빈향응적채양신호진행고장특정제취，연후이용BP신경망락대각충상태하적특정향량진행분류결책，실현모의전로적고장진단。통과대전로진행방진，증명해방법능구실현고장검측급정위，구유준학솔고적특점。
Wavelet transform is a time?frequency analysis method,and is honoured as the mathematical microscope be?cause of its multi?resolution characteristics. Since BP neural network has great generalization ability,and is suitable to deter?mine which kind of fault type the circuit status belongs to,the analog circuit fault diagnosis method based on wavelet neural net?work is adopted by combining with the two methods. The wavelet transform is used to extract the fault feature of sampled signal responding to amplitude?frequency in analog circuit,and then the eigenvectors in various status are classified and determined with BP neural network to implement analog circuit fault diagnosis. Simulation of the circuit proves that the method can realize fault detection and location,and has high precision.