Circuit Fault Location Based on Dynamic Bayesian Network

摘要

To improve the accuracy of analog circuit fault diagnosis, on account of the problem that is difficult to obtain a high accuracy of the test results for a single model, based on combinatorial optimization theory, an analog circuit fault diagnosis model based on dynamic Bayesian network is proposed. Firstly, circuit fault features are extracted, and then hidden Markov model and least squares support vector machine are used to establish combination diagnosis model of analog circuit fault, and finally the simulation experiment is used to analyze the performance of combination models. The results show that compared to other analog circuit fault diagnosis models, the proposed model not only improves the accuracy of analog circuit fault detection, but also has faster speed of fault diagnosis.