基于覆盖算法的模拟电路故障诊断方法

Approach of Fault Diagnosis in Analog Circuit Based on Covering Algorithm

模拟电路故障诊断理论与方法的研究是目前热门的研究课题。针对传统神经网络内部结构难以确定、可理解性差、难以用硬件实现的问题,本文构建一种基于领域覆盖算法的模拟电路故障诊断方法,并针对领域覆盖算法构造的神经网络内部神经元个数过多,初始点选择随机的情况,提出领域搜索覆盖算法。最后通过对某一个带通滤波电路进行故障诊断,降低神经网络中神经元的个数,提高了该神经网络的泛化能力,故障诊断率也提高了9个百分点。验证了该方法的可行性。

The research on theory and method of analog circuit fault diagnosis is a hot research topic at present. Traditional neural network learning algorithm has limitations,such as it is difficult to determine its structure,to comprehend and to achieve with the hardware. To solve these problems of traditional neural network in fault diagnosis of analog circuits,we used the neighborhood covering algorithm（ NCA） to determine the structure of the neural network. In order to reduce number of neurons and determine a good initial point of NCA,this paper studied an improved method（ NSCA）. In the end,the fault diagnosis of a certain bandpass filter circuit,which reduces the number of neurons in the neural network,improves the generalization ability of the neural network,and improves the accuracy of diagnosis about 9 percentage point. Simulation results show that the method is more effective.