基于智能信息融合的模拟电路故障定位方法

针对模拟电路故障诊断中存在的测试信息不足、故障定位准确性较低等问题,提出了一种应用智能信息融合技术的模拟电路故障定位方法.该法首先分别采用可测点工作电压及不同测试频率下的电路增益,各用一个独立的神经网络依据改进的BP算法对电路实施初级诊断;然后根据初级诊断结果,运用模糊融合诊断方法进行故障定位.所提方法能充分利用故障信息,对模拟电路的软故障与多故障均可进行诊断,故障定位准确率高.

Fault Location of Analog Circuits Using Intelligent Information Fusion Technology

In order to solve the problems of insufficient test data and low diagnosis accuracy, a fault location approach for analog circuits is developed based on intelligent information fusion technology including data fusion, neural network and fuzzy theory. Using accessible node voltages and circuit gains under different test frequencies respectively, preliminary diagnosis is performed separately by an independent improved BP network employing one kind of test data. According to the preliminary diagnosis results, fault location is accomplished by means of the proposed fuzzy data fusion algorithm and fault recognition method. The proposed approach makes full use of the fault information and has the capability to diagnose multiple and soft faults of analog circuits with satisfactory accuracy.