The invention discloses a
direct current master device fault diagnosis method based on a
hybrid neural network. The method includes the following steps that firstly, associated data needed for device fault diagnosis are acquired, wherein the associated data comprise
source data and real-
time data, and the
source data include offline experimental data, dot experimental data, online
monitoring data and historical data composed of various
polling data; secondly,
information fusion is conducted on the associated data through a neural network; thirdly, a
particle swarm optimization algorithm, a
Hopfield network and a BP network are combined, the
hybrid neutral network is designed, the associated data obtained after
information fusion in the second step are predicted, and then the prediction state of a
direct current master device is acquired; fourthly, the prediction state corresponds to the original state of the
direct current master device and is shown in different
modes or / and forms, wherein the original state is the historical state shown by the
source data. By means of the method, the overhaul efficiency of the fault device and the running reliability of a
power grid are improved.