As the object of anodic current signal of 160 kA prebaked anode cells

these signals of different conditions were analyzed by the method of “spectrum  wavelet packet  neural network”.The results show that the anode current signals of the different status have different peak frequency value

so it is possible to extract energy characteristics vectors of anode current signals in different cell states using wavelet packets decomposition and wavelet packets reconstruction.According to the wavelet packet energy characteristics vectors extracted from anode current signals

diagnosis model based on BP neural network was established and verified.The simulation results show that the model of network identification is simple in construction

high accuracy in recognition

and convenient to realize online monitoring and realtime identification for the anode current signals of the different status.