Research on transformer fault voiceprint recognition based on Mel time-frequency spectrum-convolutional neural network

A series of faults will occur during the operation of the transformer, which will aggravate its vibration and noise signals to a certain extent. In order to realize the detection and recognition of transformer related faults during uninterrupted operation, a voiceprint recognition model based on Mel time spectrum convolution neural network is proposed in this paper. According to the sound signal collected by the transformer in actual operation, Mel time domain spectrum as a data set of deep learning model can be obtained through feature extraction preprocessing and Mel filter dimensionality reduction. On this basis, the Mel time domain spectrum convolution neural network deep learning model is constructed, and Adam is used as the optimizer to optimize the data set through the super parameter optimization combination with multi classification cross entropy as the loss function, so as to realize the voiceprint recognition under four different operating faults of the transformer. The simulation results show that the identification model proposed in this paper is feasible and effective in fault identification.