Analog Circuit Fault Diagnosis With Tolerances By Using BP Neural Network And Fault Tree Analysis

The analog fault diagnosis research is one of the forefronts of the testing field, one of the reason is that the reliability of electrical systems depends on that of analog circuits and systems.This paper focuses on the analog circuit fault diagnosis with tolerances by using BP neural network and fault tree analysis. With the classical pattern recognition theory, the back propagation neural network (BPNN) are applied to analog circuit fault diagnosis. The robustness, associated memory and nonlinear mapping of BPNN make this method more advantageous over traditional methods. For improving on recognition ability of network, this paper experiments with new training samples.Fault tree analysis (FTA) originated from analysis technique for systematic reliability. The diagnosis results of FTA are easy to be interpreted in that its course is based on rule. To analog circuit with tolerances, this paper present fuzzy fault tree analysis based on node information of circuit. This method uses the node information, for example node voltage value and node resistance vale, to build fault tree, then calculates the reliability of basic event by fuzzy reasoning. Finally, This paper compares BPNN with Fuzzy FTA in analog fault diagnosis.