一种基于综合智能计算的模拟电路故障诊断方法

An Analog Circuit Fault Diagnosis Method Based on Integrated Calculation of Intelligent

文章针对目前模拟电路故障诊断中存在的容差和非线性特性所带来的诊断难点,提出了一种基于粗集-主成分分析的模拟电路故障诊断的方法。这种方法利用粗集理论属性约简、数据归一化、主成分分析对输入数据进行预处理,提取主要参数,然后利用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。对标准电路仿真结果表明:该方法能够实现快速故障检测与定位,具有准确率高的特点。

The tolerance and nonlinearity in analog circuit make its fault diagnosisic difficult. In order to solve this problem, this paper presents an analog circuit fault diagnosis method based on Rough Sets-Neural Network. The method uses Rough Sets attribute reduction, normalization of data and principle component analysis to preprecess input data and generate major ones. Feature vectors under certain states can be classified by using NN, and fault diagnosis is realized. Simulation results on benchmark circuits show that this scheme is feasible and has many powerful features, such as diagnosing and locating faults quickly and exactly.