摘要
论述了利用多类电量测试信息、应用神经网络与D-S证据理论实现模拟电路故障诊断的基本原理,提出了一种基于可测点电压与不同测试频率下的电路增益经决策层信息融合的故障诊断新方法。分别利用此两类测试信息,各用一个独立的改进BP网络对电路进行初步诊断,再运用所提融合诊断算法实现故障定位。模拟实验结果表明:所提方法对硬故障与元件参数偏移较小的软故障均适用,故障定位准确率高。
Based on neural network and D-S evidence theory, this paper discusses the fundamentals of analog fault diagnosis by means of multiform circuit responses. A new fault diagnosis method is proposed based on data fusion by measuring accessible node voltages and circuit gains of output to input under different test frequencies. Preliminary diagnosis is performed separately by an independent improved BP network employing one kind of circuit responses. Fault location is accomplished by using the proposed decision fusion algorithm according to the preliminary diagnosis results. Theoretical analysis and experimental results show that the proposed fusion diagnosis method avoids limitation of single information and has the capability to diagnose catastrophic and parametric faults of analog circuits with satisfactory accuracy.
出处
《电路与系统学报》
CSCD
北大核心
2005年第1期35-39,共5页
Journal of Circuits and Systems
基金
国家自然科学基金资助项目(50277010)
湖南省自然科学基金资助项目(04JJ6034)
高等院校博士学科点专项科研基金资助项目(20020532016)
湖南省科技计划资助项目(04FJ2003
03GKY3115)
关键词
故障定位
神经网络
证据理论
决策融合
模拟电路
fault location
neural network
evidence theory
decision fusion
analog circuit
