Abstract: As power equipment is the basic element of the power system,the prediction of its fault risk can effectively reduce the loss caused by grid fault risks. The currently applied fault prediction model of high-voltage power equipment ignores the blind source separation processing of high-voltage power equipment signals,and cannot remove false fault components,resulting in inaccurate fault prediction results and long timeconsuming problems. Therefore,a new fault prediction model of high voltage power equipment based on fuzzy neural network is constructed. The wavelet denoising method is introduced into BSS to complete BSS and wavelet decomposition of high voltage power equipment signals. The false components in the decomposition results are deleted by the mutual information method. The fault features are extracted by interpolation morphological filtering and set as the input variables of fuzzy neural network to construct the fault prediction model of high voltage power equipment. The experimental results show that the error of the model is always less than 2.5% and the root mean square error is less than 3.4% in the process of 30 experimental iterations. The prediction time test results are 14ms～23ms. The data suggests that the prediction accuracy of the model is higher,the prediction speed is faster,and it has obvious application advantages.