Deep neural network prediction method of ring network tank life based on internal discharge characteristics

In order to improve the effect of ring network cabinet life prediction, the deep neural network life prediction method based on the characteristics of internal distribution of ring network cabinet is studied. Using the optimal wavelet packet transform method, the local discharge characteristics of ring network cabinet are extracted. Nuclear principal component analysis was used to reduce dimension to deal with the local discharge characteristics of ring network cabinet. The bidirectional long-term memory deep neural network was established. The local distribution characteristics after dimensionality reduction were input into the network and the autoregressive comprehensive moving average model, and the life prediction results of the ring network cabinet with nonlinear and linear characteristics were output. The final life estimation results are obtained by combining the two estimation results. Experimental results show that the algorithm can effectively extract and reduce the dimension of the internal local discharge features of ring network cabinet. It can accurately predict the service life of the ring network cabinet under different types of local distribution. Under different local discharge intensities, the R-square coefficient of the algorithm for predicting the life of the ring network cabinet is higher, which has better prediction effect.