基于液体状态机的模拟电路故障诊断方法  

Analog Circuit Fault Diagnosis Based on Liquid State Machine

作　　者：周世官[1] 李国君[1] 郑燕[1] 

机构地区：[1]宁波工程学院电子与信息工程学院,浙江宁波315016

出　　处：《兵工自动化》2016年第8期80-82,96,共4页Ordnance Industry Automation

基　　金：浙江省教育厅科研项目(Y201431680);宁波市科技攻关项目(2014C50048);宁波市自然科学基金(2015A610153)

摘　　要：针对模拟电路故障特征难以识别的问题,结合液体状态机神经网络的特点,从模拟电路故障特征样本获取和故障模式识别两方面入手,提出一种基于液体状态机的模拟电路故障诊断方法。该方法利用Matlab和PSpice联合仿真,实现大量故障样本数据的自动获取,采用液体状态机进行故障模式的分类,并对两级阻容耦合放大电路的故障诊断实例进行仿真。仿真结果表明:该方法和目前应用最广泛的BP神经网络相比,故障识别准确率会有所下降,但训练时间远小于BP神经网络,且泛化能力强,对模拟电路故障诊断研究有一定的实际意义。In order to solve the difficulty of recognition in analog circuit fault diagnosis, under the two aspects of analog circuit fault feature extraction and fault pattern recognition, combined with the respective characteristic of liquid state machine, this paper present a new analog circuit fault diagnosis method based on liquid state machine. Firstly, the soft of MATLAB and PSpice is used to obtain the large number fault sample data automatically, and then the fault mode is classified by liquid state machine. Simulation show that the accuracy rate of fault identification can be reduced compared with the most widely used BP neural network. However, the training time of this method is much less than that of BP neural network, and the generalization ability is strong. It has a certain practical significance for the fault diagnosis of analog circuits.

关 键 词：故障诊断 液体状态机 充电模型 模拟电路 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]