Research on soft fault diagnosis method of pulsed power supply based on multi-feature fusion
As the cornerstone component in technologies such as electrothermal and electromagnetic emission,the stability of the pulse power supply is pivotal to the performance of the entire emission system.In addressing soft faults in pulse power sources,a fault diagnosis method based on the integration of multi-feature into a Back Propagation(BP)neural network is proposed.By constructing a simulation model of the pulse power supply,we gathered discharge current fault data samples.Time-domain analysis and wavelet analysis were applied to these samples to extract time-domain parameters and the energy within specific frequency bands,thereby creating a feature vector that encapsulates a spectrum of characteristics.The genetic algorithm was utilized to optimize the initial weights and thresholds of the BP neural network,thus achieving precise recognition of the fault patterns of the pulse power supply.Comparative experiments with other diagnostic methods have corroborated the efficacy of this approach.
pulse power supplyfault diagnosiswavelet packet transformback propagation networkmulti-feature fusion
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